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Open AccessProceeding Paper

Application of an Adaptive Neuro-Fuzzy Inference System for the Removal of Cadmium (II) from Acid Mine Drainage onto Modified Cellulose Nanocrystals

by Banza Jean Claude, Vhahangwele Masindi and Linda L. Sibali

Eng. Proc. 2025, 117(1), 1; https://doi.org/10.3390/engproc2025117001 - 18 Nov 2025

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Abstract

This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), [...] Read more.

This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10–20 mg/L). According to these findings, cadmium (II) breakthrough occurred more quickly at lower bed heights, higher flow rates, and higher cadmium (II) concentrations. The Thomas model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (backpropagation and least squares estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solutions via modified cellulose nanocrystals. To compare the model’s predicted results with experimental data, statistical approaches were employed, including calculating the coefficient of determination (R²) and mean square error (MSE). The ANFIS model used to predict cadmium (II) adsorption via modified cellulose nanocrystals had a strong correlation value of 0.997 for least squares estimation (LSE) and 0.999 for the gradient descent (backpropagation) method, indicating the effectiveness of the trained model in predicting the cadmium (II) adsorption process. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1416 KB

Open AccessProceeding Paper

Enzyme-Assisted Extraction of Bioactive Compounds from Origanum dictamnus L.

by Zafeiria Lemoni, Roza Konstantina Leka, Theopisti Lymperopoulou and Diomi Mamma

Eng. Proc. 2025, 117(1), 2; https://doi.org/10.3390/engproc2025117002 - 19 Nov 2025

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Abstract

Enzyme-assisted extraction (EAE) was applied to extract bioactive compounds from the leaves of Origanum dictamnus L. using the commercial enzyme preparation Cellic^® CTec3 HS. A Taguchi experimental design was applied to determine the optimal EAE conditions. The variables were enzyme loading, solid-to-liquid [...] Read more.

Enzyme-assisted extraction (EAE) was applied to extract bioactive compounds from the leaves of Origanum dictamnus L. using the commercial enzyme preparation Cellic^® CTec3 HS. A Taguchi experimental design was applied to determine the optimal EAE conditions. The variables were enzyme loading, solid-to-liquid ratio, extraction time and the responses of total phenolic content (TPC), and total flavonoid content (TFC). Under optimized conditions, EAE achieved TPC yield of 164.8 ± 5.2 mg GAE/g and TFC yield reached 92.5 ± 5.7 mg CAE/g. The results support the potential of EAE as an efficient method for extraction of bioactive compounds from Origanum dictamnus L. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1880 KB

Open AccessProceeding Paper

Design and Integration of a Retrofit PV–Battery System for Residential Energy Savings and Thermal Comfort

by Dimitrios Rimpas, Nikolaos Rimpas, Vasilios A. Orfanos and Ioannis Christakis

Eng. Proc. 2025, 117(1), 3; https://doi.org/10.3390/engproc2025117003 - 26 Nov 2025

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Abstract

This study presents the design and implementation of a prototype dual-function photovoltaic window system that integrates flexible solar panels for dynamic shading and a compact lithium battery for local energy storage. The methodology involves developing an experimental setup where translucent, flexible photovoltaic panels [...] Read more.

This study presents the design and implementation of a prototype dual-function photovoltaic window system that integrates flexible solar panels for dynamic shading and a compact lithium battery for local energy storage. The methodology involves developing an experimental setup where translucent, flexible photovoltaic panels are retrofitted onto a standard residential window. The system is connected to a charge controller and a small-capacity lithium-ion battery pack. Key performance metrics, including solar irradiance, power generation efficiency, reduction in thermal transmittance, and battery state of charge, are continuously monitored under varying real-world environmental conditions. The integrated panels can significantly reduce solar heat gain, thereby lowering indoor ambient temperature and reducing the building’s cooling load. Simultaneously, the system will generate sufficient electricity to be stored in the lithium battery, providing a self-contained power source for low-draw applications such as lighting or charging personal devices. This research highlights the viability of developing cost-effective, multi-functional building components that transform passive architectural elements into active energy-saving and power-generating systems in terms of green environment goals. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 2322 KB

Open AccessProceeding Paper

Photocatalytic Activity of ZnFe₂O₄ for the Degradation of Fast Green FCF and Orange II

by Nashra Fatima, Ekhlakh Veg, Srishti Dwivedi, Anushka Pandey and Tahmeena Khan

Eng. Proc. 2025, 117(1), 4; https://doi.org/10.3390/engproc2025117004 - 28 Nov 2025

Cited by 2 | Viewed by 926

Abstract

In recent years, photocatalysis using semiconductor materials has gained significant attention as an effective strategy for dye degradation under mild conditions. Among various metal oxide photocatalysts, zinc ferrite (ZnFe₂O₄) has gained attention due to its narrow band gap, good [...] Read more.

In recent years, photocatalysis using semiconductor materials has gained significant attention as an effective strategy for dye degradation under mild conditions. Among various metal oxide photocatalysts, zinc ferrite (ZnFe₂O₄) has gained attention due to its narrow band gap, good stability, low cost, and activation under visible light. ZnFe₂O₄ nanoparticles (NPs) were synthesized using a co-precipitation process and tested for their photocatalytic effectiveness in degrading synthetic dyes Fast Green FCF and Orange II Sodium Salt under visible light. This study emphasizes the benefits of utilizing ZnFe₂O₄ as a visible light-activated, cost-effective, and environmentally friendly photocatalyst. These findings add to the growing research on wastewater treatment options. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 3444 KB

Open AccessProceeding Paper

Biowax Impregnation of Recyclable Packaging Papers with Enhanced Water and Oil Barrier Properties

by Pieter Samyn

Eng. Proc. 2025, 117(1), 5; https://doi.org/10.3390/engproc2025117005 - 3 Dec 2025

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Abstract

The industrial processing of innovative packaging papers with enhanced barrier properties has become ever more challenging due to the more stringent regulations on single-use plastics (SUPs), with an extended applicability to coated papers. Although the traditional packaging papers are based on renewable sources, [...] Read more.

The industrial processing of innovative packaging papers with enhanced barrier properties has become ever more challenging due to the more stringent regulations on single-use plastics (SUPs), with an extended applicability to coated papers. Although the traditional packaging papers are based on renewable sources, they do not provide water and oil resistance and traditionally require the deposition of extruded polymer films or dispersion coatings that interfere with the paper recycling process. In this study, an alternative method has been investigated through the impregnation of papers with various types of biowax, including a synthetic PE wax, palm oil wax, sunflower wax, rice bran wax, rapeseed wax, castor wax, rice bran wax, and candelilla wax. The close control of processing conditions in an industrial pilot-line is critical to produce an optimized product quality with enhanced water and oil contact angles. In particular, the variations in wax type and wax loadings after single- or dual-side impregnation and the control of processing temperatures have been related to the oil and water contact angles. The stable water contact angles in the range of 100 to 120° were obtained depending on the biowax type. Meanwhile, the increase in oil contact angles up to 60° is in line with the enhanced grease resistance. The good recyclability scores of biowax-impregnated papers were demonstrated following the “Harmonized European laboratory test method to generate parameters enabling the assessment of the recyclability of paper and board products in recycling mills with conventional process (Part I)”, version February 2024. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 425 KB

Open AccessProceeding Paper

Electrified Pressure Swing Distillation: A Systems-Based Sustainability Assessment

by Jonathan Wavomba Mtogo, Gladys Wanyaga Mugo, Emmanuel Karimere Kariuki, Martin Murimi Gichungu and Bevin Nabai Kundu

Eng. Proc. 2025, 117(1), 6; https://doi.org/10.3390/engproc2025117006 - 3 Dec 2025

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Abstract

The decarbonisation of energy-intensive separation processes is critical for achieving net-zero goals in the chemical industry. While widely used for separating azeotropic mixtures, pressure swing distillation (PSD) remains highly energy-intensive due to significant thermal demands. This work presents a comprehensive systems-based assessment of [...] Read more.

The decarbonisation of energy-intensive separation processes is critical for achieving net-zero goals in the chemical industry. While widely used for separating azeotropic mixtures, pressure swing distillation (PSD) remains highly energy-intensive due to significant thermal demands. This work presents a comprehensive systems-based assessment of electrified distillation designs, with a specific focus on tetrahydrofuran–water separation as a case study. Using Aspen Plus and Aspen Plus Dynamics, key performance indicators, including controllability, thermal and exergy efficiencies, and CO₂ emissions reduction potential, are evaluated. The electrified configurations employed heat pumps as substitutes for conventional steam heating. Disturbance rejection was applied to compare the input–output pairings and select pairings with the best controllability and disturbance rejection indices. Results showed that the conventional PSD (CPSD) exhibited higher Morari Resiliency Index (MRI) and acceptable Condition Number (CN) values, indicating better robustness and disturbance rejection than the heat pump-assisted PSD (HPAPSD). Despite this, HPAPSD achieved a 59% reduction in primary energy demand, a 23% increase in exergy efficiency, and an 82% reduction in CO₂ emissions. This study demonstrates the potential of electrification to transform PSD systems from rigid, energy-intensive operations into flexible and sustainable processes. The findings support a shift towards integrated, systems-driven design strategies in chemical separation, aligning with broader goals in process electrification, circularity, and net-zero manufacturing. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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15 pages, 534 KB

Open AccessProceeding Paper

Modelling Rabies Transmission with Vaccination: Incorporating Pharmaceutical and Particle Processing for Pre-Exposure Prophylaxis Optimization

by Kuppusamy Anjali, Thangaraj Nandha Gopal, Thangavel Megala, Anbulinga R. Ashwin and Arunachalam Yasotha

Eng. Proc. 2025, 117(1), 7; https://doi.org/10.3390/engproc2025117007 - 3 Dec 2025

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Abstract

Rabies remains a persistent zoonotic threat, particularly in regions where domestic dogs are the main source of human and animal infections. This mathematical model studies the dynamics of rabies transmission between canine populations (dog-to-dog) and from canines to humans (dog-to-human). The model incorporates [...] Read more.

Rabies remains a persistent zoonotic threat, particularly in regions where domestic dogs are the main source of human and animal infections. This mathematical model studies the dynamics of rabies transmission between canine populations (dog-to-dog) and from canines to humans (dog-to-human). The model incorporates susceptible, infected, and vaccinated compartments for both species, with pre-exposure vaccination as the key control strategy. Processes such as encapsulation, stability enhancement, and controlled release are modelled as parameters influencing vaccination rates in both dogs and humans. Specifically, the model introduces processing-dependent vaccination functions that reflect improved bioavailability, immunogenicity, and delivery efficiency due to advanced formulation techniques. This interdisciplinary approach bridges mathematical epidemiology and pharmaceutical technology. Earlier rabies models focus on transmission and static vaccination, often ignoring vaccine formulation and delivery. Our current work fills this gap by incorporating pharmaceutical and particle engineering parameters into the vaccination terms of the model, thereby providing a more comprehensive framework for optimizing rabies control strategies in endemic regions. Positivity and boundedness analyses confirm that all model variables remain biologically feasible and bounded over time. Stability analysis identifies thresholds for disease elimination or persistence. Numerical simulations show that enhancing pharmaceutical parameters increases vaccination impact, reducing peak infection prevalence in dogs from 18% to 5% and in humans from 4% to 0.8%, and shortening elimination time from 8 years to 3 years. Formulations with controlled release and improved stability maintain over 90% reduction in transmission for more than 5 years, compared to 60% over 3 years for conventional vaccines. This will ensure that the model’s predictions are validated against realistic conditions and can effectively guide rabies control strategies. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 2412 KB

Open AccessProceeding Paper

Facile Wet-Chemical Synthesis of Graphene Oxide-Hydroxyapatite Composite for Potent, Accelerated and Synergistic Sonophotocatalytic Degradation of Diclofenac Under Light and Ultrasound Irradiation

by Joe Mari Biag, Justin Carl Briones, Crystal Cayena Dancel, Florely De Villa, Christian Ibarra Durante, Rugi Vicente Rubi and Rich Jhon Paul Latiza

Eng. Proc. 2025, 117(1), 8; https://doi.org/10.3390/engproc2025117008 - 3 Dec 2025

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Abstract

The widespread disposal of pharmaceutical waste, particularly diclofenac (DCF), poses a significant threat to aquatic ecosystems. The current degradation methods, including biological treatments and standalone advanced oxidation processes, often prove insufficient, leaving residual DCF concentrations. This study proposes a novel solution using a [...] Read more.

The widespread disposal of pharmaceutical waste, particularly diclofenac (DCF), poses a significant threat to aquatic ecosystems. The current degradation methods, including biological treatments and standalone advanced oxidation processes, often prove insufficient, leaving residual DCF concentrations. This study proposes a novel solution using a rapidly synthesized graphene oxide/hydroxyapatite (GO/HAp) nanocomposite via wet-chemical precipitation to enhance DCF degradation through synergistic sonophotocatalysis. The synthesized nanocomposite’s structure was confirmed using Fourier transform infrared spectroscopy FTIR, x-ray diffraction XRD, and scanning electron microscope SEM analyses, revealing the successful formation of a hexagonal HAp phase on GO sheets. Optimization of the sonophotocatalytic parameters revealed that pH and loading significantly influenced degradation, while time had a less pronounced effect. The optimal conditions (a pH pf 4, 45 mg GO/HAp, 30 min) achieved a remarkable 93.86% DCF degradation, significantly outperforming standalone photocatalysis (72.76%) and sonolysis (63.76%). This enhanced performance is attributed to the synergistic effect of sonophotocatalysis, which increases the active surface area and radical generation, coupled with the high surface area and adsorption capacity of the GO/HAp nanocomposite. This research demonstrates that rapid wet-chemical synthesis of the GO/HAp nanocomposite, coupled with an optimized sonophotocatalytic process, offers a potent, accelerated, and efficient method for degrading DCF, paving the way for improved pharmaceutical wastewater treatment. Ultimately, this research provides a foundation for developing effective water treatment solutions to combat pharmaceutical contaminants. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 2356 KB

Open AccessProceeding Paper

Nitrogen-Doped Carbon Dots Derived from Onion Peel (Allium cepa) for Fluorescence-Based Detection of Microplastics

by Ma. Sofia Sam Pintoy, Fayeeh Joy Dabalus, Joemari Voluntad, Carlou Eguico, Allan N. Soriano, Nathaniel P. Dugos and Rugi Vicente Rubi

Eng. Proc. 2025, 117(1), 9; https://doi.org/10.3390/engproc2025117009 - 3 Dec 2025

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Abstract

Microplastics, plastic particles smaller than 5 mm, are now ubiquitous and represent a form of pollution that threatens ecosystems and human health, infiltrating the environment, air, and food chain. The search for solutions to microplastics requires industrial policies that limit plastic production and [...] Read more.

Microplastics, plastic particles smaller than 5 mm, are now ubiquitous and represent a form of pollution that threatens ecosystems and human health, infiltrating the environment, air, and food chain. The search for solutions to microplastics requires industrial policies that limit plastic production and technological innovations for removal and recycling. Specifically, this paper reports a sustainable and cost-effective method for the detection of high-density polyethylene (HDPE) and low-density polyethylene (LDPE) microplastics using nitrogen-doped carbon dots (N-CD) synthesized from onion peel and L-cysteine via hydrothermal carbonization. Two precursor ratios (1:1 and 1:0.30 w/w) were evaluated. The resulting N-CDs exhibited bright yellow-green fluorescence (470–500 nm) and excitation-dependent photoluminescence under 365 nm UV light. FTIR and UV-Vis spectroscopy confirmed the presence of nitrogen-containing functional groups and effective graphitization, particularly in the 1:0.30 ratio. Fluorescence imaging revealed stronger intensity and greater stain uniformity in thermally softened MPs treated with 1:0.30 N-CDs, with a peak emission of 10,230.02 a.u. at 2 h and PMT 11—surpassing the 1:1 ratio. Bandgap and absorbance analyses supported the superior optical behavior of the lower-concentration formulation. Image analysis further indicated increased luminescent area over time, and two-way ANOVA confirmed statistically significant effects of heating time and PMT settings (p < 0.05). Compared to traditional filtration staining, thermal-assisted application offered enhanced and stable fluorescence. These findings demonstrate the efficacy of green-synthesized N-CDs for MP detection, with potential scalability and environmental applicability. Future work should explore alternative biomass sources and assess N-CD performance under field conditions to optimize environmental sensing strategies. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 286 KB

Open AccessProceeding Paper

Synthesis of 2-Naphthyl 2-Chloroacetate and Study of Its Nucleophilic Substitution Reactions with Citric Acid

by Ruzimurod Jurayev, Azimjon Choriev, Anvar Abdushukurov and Ilyos Normurodov

Eng. Proc. 2025, 117(1), 10; https://doi.org/10.3390/engproc2025117010 - 8 Dec 2025

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Abstract

In this study, an efficient and regioselective synthetic method was developed for the preparation of 3-hydroxy-3-((2-(naphthalen-2-yloxy)-2-oxoethoxy)carbonyl)pentanedioic acid, a multifunctional ether–ester compound of potential interest for pharmaceutical and material science applications. The target compound was synthesized via the nucleophilic substitution (SN2) and esterification reactions [...] Read more.

In this study, an efficient and regioselective synthetic method was developed for the preparation of 3-hydroxy-3-((2-(naphthalen-2-yloxy)-2-oxoethoxy)carbonyl)pentanedioic acid, a multifunctional ether–ester compound of potential interest for pharmaceutical and material science applications. The target compound was synthesized via the nucleophilic substitution (SN2) and esterification reactions of 2-naphthyl chloroacetate with the monosodium salt of citric acid. Optimization of the reaction conditions was carried out by varying the molar ratio of the reagents, reaction temperature, and duration. The highest yield of 83% was achieved under the conditions of a 2:1 molar ratio of chloroacetate to citrate, a temperature of 70–80 °C, and a reaction time of 6 h. The enhanced product yield observed under these conditions is attributed to the dual reactivity of the citric acid monosodium salt, which contains a free hydroxyl group capable of undergoing SN2 etherification, and free carboxylic acid groups that participate in esterification with the electrophilic 2-naphthyl chloroacetate. The stoichiometric 2:1 ratio ensures that both reactive centers on the citrate anion are fully utilized, leading to efficient and selective transformation into the desired product. Mechanistically, the ether bond formation proceeds through the classical Williamson ether synthesis pathway, where the alkoxide formed from the hydroxyl group attacks the electrophilic carbon of the chloroacetate, displacing the chloride ion. Concurrently, esterification enhances molecular complexity and stability. The results underline the synthetic utility of citric acid derivatives in forming complex organic architectures via environmentally benign routes. This study not only contributes a practical approach to multifunctional molecule synthesis but also reinforces the applicability of green chemistry principles in ester–ether coupling strategies. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

7 pages, 872 KB

Open AccessProceeding Paper

Application of Reduced Graphene Oxide in Biocompatible Composite for Improving Its Specific Electrical Conductivity

by Mikhail Savelyev, Artem Kuksin, Ekaterina Otsupko, Victoria Suchkova, Kristina Popovich, Pavel Vasilevsky, Ulyana Kurilova, Sergey Selishchev and Alexander Gerasimenko

Eng. Proc. 2025, 117(1), 11; https://doi.org/10.3390/engproc2025117011 - 8 Dec 2025

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Abstract

The reduced graphene oxide (rGO) combination in association with the single-walled carbon nanotubes (SWCNTs) in a dispersion minimizes the number of carbon particles to obtain a hydrogel with the same level of specific conductivity. When developing neuroimplants intended to restore damaged neural networks [...] Read more.

The reduced graphene oxide (rGO) combination in association with the single-walled carbon nanotubes (SWCNTs) in a dispersion minimizes the number of carbon particles to obtain a hydrogel with the same level of specific conductivity. When developing neuroimplants intended to restore damaged neural networks or modulate pain transmission, biocompatibility and the permeability of stimulating currents are key requirements. The specific conductivity of the resulting hydrogels with the addition of different carbon nanoparticles was 19 mS/cm (1-SWCNTs), 17 mS/cm (2-rGO), and 35 mS/cm (3-SWCNTs/rGO). The results confirm the possibility of regulating the degradation time. Colorimetric assay for assessing cell metabolic activity (MTT) assay using the Neuro 2A cell line showed sufficient biocompatibility for the amount of SWCNTs and rGO used. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1306 KB

Open AccessProceeding Paper

Prediction and Optimisation of Cr (VI) Removal by Modified Cellulose Nanocrystals from Aqueous Solution Using Machine Learning (ANN and ANFIS)

by Banza Jean Claude, Vhahangwele Masindi and Linda L. Sibali

Eng. Proc. 2025, 117(1), 12; https://doi.org/10.3390/engproc2025117012 - 9 Dec 2025

Viewed by 441

Abstract

Cellulose nanocrystals (CNCs) have emerged as highly efficient adsorbents for heavy metal removal owing to their biodegradability, wide availability, and rich surface chemistry. Their abundant hydroxyl and other reactive functional groups provide a high density of active sites, significantly enhancing their affinity and [...] Read more.

Cellulose nanocrystals (CNCs) have emerged as highly efficient adsorbents for heavy metal removal owing to their biodegradability, wide availability, and rich surface chemistry. Their abundant hydroxyl and other reactive functional groups provide a high density of active sites, significantly enhancing their affinity and adsorption capacity for toxic metal ions such as chromium (VI). The green adsorbent was characterised using FTIR to identify the functional groups. The optimum conditions were pH 6, concentration 140 mg/L, time 120 min, and adsorbent dosage 6 g/L, with a percentage removal of 95%. Deep machine learning was employed to predict the removal capacity of green and biodegradable adsorbents for chromium (VI) removal from wastewater. The findings show that adaptive neuro-fuzzy inference systems effectively model the prediction of Chromium (VI) adsorption. The Levenberg–Marquardt algorithm (LM) was used to train the network through feedforward propagation. In the training dataset, R² was 0.966, Mean Square Error (MSE) 0.042, Absolute average relative error (AARE) 0.053, Root means square error (RMSE) 0.077, and average relative error (ARE) 0.053 for the artificial neural network. The RMSE of 0.021, AARE of 0.015, ARE of 0.01, MSE of 0.017, and R² of 0.998 for the adaptive neuro-fuzzy inference system. These findings confirm the strong adsorption potential of CNCs and the suitability of advanced machine learning models for forecasting heavy metal removal efficiency. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 971 KB

Open AccessProceeding Paper

Parametric Study of Slow Pyrolysis on Invasive Water Hyacinth for Energy Recovery and Towards Cleaner Blue Carbon Technologies

by Pauline Patrice Tamoria, Eugenie Mhel Chavez, Trisha Camille Garcia, Winnieruth Manio, Ivy Jane Milana, Rugi Vicente Rubi, Eric Halabaso and Rich Jhon Paul Latiza

Eng. Proc. 2025, 117(1), 13; https://doi.org/10.3390/engproc2025117013 - 10 Dec 2025

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Abstract

The urgent need for cleaner energy sources has driven exploration into innovative and sustainable solutions. This study investigates the potential of the invasive aquatic plant, the water hyacinth, to contribute to energy recovery and support the preservation of blue carbon ecosystems through biomass [...] Read more.

The urgent need for cleaner energy sources has driven exploration into innovative and sustainable solutions. This study investigates the potential of the invasive aquatic plant, the water hyacinth, to contribute to energy recovery and support the preservation of blue carbon ecosystems through biomass removal. Employing slow pyrolysis, this study examines the influence of temperature (300–500 °C) and residence time (30–90 min) on bio-oil and biochar production in a fixed-bed reactor. Results revealed that residence time was the key operational parameter significantly influencing total liquid condensate yield, which peaked at 34.34 wt% at 400 °C after 90 min. Moisture content reveals an actual organic bio-oil yield of approximately 3.4–4.8 wt%. In contrast, biochar yield (max. 43.74 wt%) was not significantly affected by the tested parameters. The resulting bio-oil exhibited a high heating value of up to 25.84 MJ/kg, suggesting its potential as a renewable fuel. This study concludes that slow pyrolysis of invasive water hyacinth provides a dual-benefit pathway: it co-produces renewable bio-oil for energy recovery alongside a stable biochar, offering a tangible route for blue carbon sequestration. This integrated approach transforms an environmental liability into valuable resources, contributing to a cleaner environment and a more sustainable future. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1520 KB

Open AccessProceeding Paper

Robust Control Design for an Off-Board EV Charger Considering Grid Impedance Variation

by Chhaytep Born, Menghorng Sy, Panha Soth, Heng Tang, Socheat Yay, Seven Siren, Channareth Srun and Chivon Choeung

Eng. Proc. 2025, 117(1), 14; https://doi.org/10.3390/engproc2025117014 - 12 Dec 2025

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Abstract

Grid impedance variation has the possibility of leading to voltage oscillation and control instability, which poses a serious problem to electric vehicle (EV) charger design. In response to this problem, this paper proposes a robust control approach that is capable of dealing with [...] Read more.

Grid impedance variation has the possibility of leading to voltage oscillation and control instability, which poses a serious problem to electric vehicle (EV) charger design. In response to this problem, this paper proposes a robust control approach that is capable of dealing with grid impedance variation and system uncertainties. The proposed dual-loop control strategy is composed of an outer-loop proportional–integral (PI) controller and an inner-loop robust state feedback controller with integral action. The benefits of control are maximized according to linear matrix inequality (LMI) techniques. This paper aims to address the effects of grid impedance variation by including the uncertainty model considering the potential varying parameters in the control design process. Additionally, the uncertainty model considers sixteen possible sets, which are described by variations in the four most important parameters: grid impedance, grid resistance, filter impedance, and filter resistance. The simulations suggest that the proposed controller maintains stable current regulation for uncertainty factors as high as γ = 3.3, where all closed-loop poles remain within the unit circle. For all the tested uncertainty levels, the grid-current tracks the reference of 10 A with a faster settling time at γ = 1.1 and no overshoot for higher uncertainty levels. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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18 pages, 2500 KB

Open AccessProceeding Paper

Interface Engineering in Hybrid Energy Systems: A Case Study of Enhance the Efficiency of PEM Fuel Cell and Gas Turbine Integration

by Abdullatif Musa, Gadri Al-Glale and Magdi Hassn Mussa

Eng. Proc. 2025, 117(1), 15; https://doi.org/10.3390/engproc2025117015 - 18 Dec 2025

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Abstract

Integrating electrochemical fuel cells and internal combustion engines can enhance the total efficiency and sustainability of power systems. This study presents a promising solution by integrating a Proton Exchange Membrane Fuel Cell (PEMFC) with a mini gas turbine, forming a hybrid system called [...] Read more.

Integrating electrochemical fuel cells and internal combustion engines can enhance the total efficiency and sustainability of power systems. This study presents a promising solution by integrating a Proton Exchange Membrane Fuel Cell (PEMFC) with a mini gas turbine, forming a hybrid system called the “Oya System.” This approach aims to mitigate the efficiency losses of gas turbines during high ambient temperatures. The hybrid model was designed using Aspen Plus for modelling and the EES simulation program for solving mathematical equations. The primary objective of this research is to enhance the efficiency of gas turbine systems, particularly under elevated ambient temperatures. The results demonstrate a notable increase in efficiency, rising from 37.97% to 43.06% at 10 °C (winter) and from 31.98% to 40.33% at 40 °C (summer). This improvement, ranging from 5.09% in winter to 8.35% in summer, represents a significant achievement aligned with the goals of the Oya System. Furthermore, integrating PEMFC contributes to environmental sustainability by utilising hydrogen, a clean energy source, and reducing greenhouse gas emissions. The system also enhances efficiency through waste heat recovery, further optimising performance and reducing energy losses. This research highlights the critical role of interface engineering in the hybrid system, particularly the interaction between the PEMFC and the gas turbine. Integrating these two systems involves complex interfaces that facilitate the transfer of electrochemistry, energy, and materials, optimising the overall performance. This aligns with the conference session’s focus on green technologies and resource efficiency. The Oya System exemplifies how innovative hybrid systems can enhance performance while promoting environmentally friendly processes. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 1298 KB

Open AccessProceeding Paper

A Novel Circular Waste-to-Energy Pathway via Cascading Valorization of Spent Coffee Grounds Through Non-Catalytic Supercritical Transesterification of Pyrolytic Oil for Liquid Hydrocarbon

by Elmer Jann Bantilan, Joana Batistil, Bernice Ann Calcabin, Ephriem Organo, Neome Mitzi Ramirez, Jayson Binay, Reibelle Raguindin, Rugi Vicente Rubi and Rich Jhon Paul Latiza

Eng. Proc. 2025, 117(1), 16; https://doi.org/10.3390/engproc2025117016 - 4 Jan 2026

Viewed by 717

Abstract

The ever-growing global consumption of coffee generates millions of tons of spent coffee grounds (SCG) annually, posing a significant waste disposal problem. Although some SCG find use in composting or biogas production, a large portion remains underutilized. This study introduces a novel circular [...] Read more.

The ever-growing global consumption of coffee generates millions of tons of spent coffee grounds (SCG) annually, posing a significant waste disposal problem. Although some SCG find use in composting or biogas production, a large portion remains underutilized. This study introduces a novel circular waste-to-energy pathway to tackle this challenge. Our proposed technology employs a cascading valorization approach, utilizing non-catalytic supercritical transesterification of pyrolytic oil derived from SCG for liquid hydrocarbon production. The process begins with pyrolysis, which converts SCG into pyrolytic oil. This oil is then upgraded via supercritical transesterification with methanol. Experiments were conducted using a 1:6 oil-to-methanol ratio at precisely controlled conditions of 239.4 °C and 1200 psi for 20 min. This optimized process yielded an impressive 96% of valuable liquid hydrocarbon product. The resulting product exhibited highly favorable characteristics, including a density of 755.7 kg/m³, a viscosity of 0.7297 mm²/s, and a high heating value (HHV) of 48.86 MJ/kg. These properties are remarkably comparable to conventional biofuels and standard fossil fuels, demonstrating the product’s potential as a viable energy source. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 1994 KB

Open AccessProceeding Paper

A Leptolyngbya-Dominated Consortium for the Optimized Biological Treatment of Mixed Agro-Industrial Effluents

by Vasiliki Patrinou, Dimitris V. Vayenas and Athanasia G. Tekerlekopoulou

Eng. Proc. 2025, 117(1), 17; https://doi.org/10.3390/engproc2025117017 - 7 Jan 2026

Viewed by 540

Abstract

Many individual wastewater streams exhibit imbalanced or poor nutrient profiles, limiting their suitability for efficient biological treatment. In regions where several agro-industrial activities coexist, these streams are often produced in small volumes and vary considerably in composition, making their combined use an effective [...] Read more.

Many individual wastewater streams exhibit imbalanced or poor nutrient profiles, limiting their suitability for efficient biological treatment. In regions where several agro-industrial activities coexist, these streams are often produced in small volumes and vary considerably in composition, making their combined use an effective way to obtain a more balanced influent. This study aimed to identify the optimal mixing ratio of two agro-industrial wastewaters, second cheese whey (SCW) and poultry wastewater (PW), for the cultivation of a Leptolyngbya-dominated consortium. Four mixing ratios of SCW:PW (50:50%, 60:40%, 70:30%, and 85:15%) were examined based on an initial dissolved chemical oxygen demand (d-COD) concentration of 3000 mg L⁻¹. The 70:30% ratio was led to significant biomass production (268.3 mg L⁻¹ d⁻¹), while simultaneously exhibiting the highest lipid content (14.0% d.w.), and the highest removal of d-COD (89.2%), total nitrogen (64%) and PO₄³⁻-P (60%). Overall, the experiments showed that using nutritionally balanced wastewater streams is a promising strategy to enhance biological treatment efficiency and lipid production. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 1944 KB

Open AccessProceeding Paper

An Optimized ANFIS Model for Predicting Water Hardness and TDS in Ion-Exchange Wastewater Treatment Systems

by Jaloliddin Eshbobaev, Adham Norkobilov, Komil Usmanov, Zafar Turakulov, Azizbek Kamolov, Sarvar Rejabov and Sitora Farkhadova

Eng. Proc. 2025, 117(1), 18; https://doi.org/10.3390/engproc2025117018 - 7 Jan 2026

Cited by 2 | Viewed by 427

Abstract

Industrial wastewater treatment processes often exhibit highly nonlinear, dynamic behavior, making accurate prediction and control difficult when using conventional modeling approaches. This study presents an enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for modeling the ion-exchange purification process based on 200 experimentally collected [...] Read more.

Industrial wastewater treatment processes often exhibit highly nonlinear, dynamic behavior, making accurate prediction and control difficult when using conventional modeling approaches. This study presents an enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for modeling the ion-exchange purification process based on 200 experimentally collected data samples obtained from a laboratory-scale treatment system. The initial ANFIS structure was generated using subtractive clustering to automatically derive the rule base, while hybrid learning combining backpropagation and least-squares estimation was applied to train the model. The training results demonstrated stable convergence across 100, 200, and 300 epochs, with progressive improvements in model accuracy. To further enhance performance, several meta-heuristic optimization methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the Adam optimizer, were integrated within a Python 3.13-based environment to refine model parameters. Ensemble learning and an extended Boosting++ strategy was subsequently employed to reduce variance, correct residual errors, and strengthen generalization capability. The optimized ANFIS model achieved strong predictive accuracy across both training and unseen test datasets. The performance metrics for the full dataset yielded RMSE (Root Mean Square Error) = 1.3369, MAE (Mean Absolute Error) = 0.9989, and R² = 0.9313, while correlation analysis showed consistently high R-values for training (0.96745), validation (0.95206), test (0.95754), and overall data (0.96507). The results demonstrate that the combination of subtractive clustering, hybrid learning, meta-heuristic optimization, and ensemble boosting produces a highly reliable soft-computing model capable of effectively capturing the nonlinear dynamics of ion-exchange wastewater treatment. The proposed approach provides a robust foundation for intelligent monitoring and control strategies in industrial purification systems. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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13 pages, 1859 KB

Open AccessProceeding Paper

Assessing an Optimal Green Hydrogen Strategy for an Inland Refinery

by Miroslav Variny, Martina Mócová, Dominika Polakovičová and Ladislav Švistun

Eng. Proc. 2025, 117(1), 19; https://doi.org/10.3390/engproc2025117019 - 8 Jan 2026

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Abstract

This study assesses four hydrogen production pathways (electrolysis, ammonia cracking, steam biomethane reforming, and methane pyrolysis) for an inland refinery under European Renewable Energy Directive III (RED III) goals. Using multicriteria decision analysis (MCDA), economic, environmental, technological, and implementation factors were evaluated. The [...] Read more.

This study assesses four hydrogen production pathways (electrolysis, ammonia cracking, steam biomethane reforming, and methane pyrolysis) for an inland refinery under European Renewable Energy Directive III (RED III) goals. Using multicriteria decision analysis (MCDA), economic, environmental, technological, and implementation factors were evaluated. The results show that biomethane reforming offers the lowest cost, while electrolysis provides the best environmental and technological performance. Sensitivity analysis highlights electricity price as the key factor. The MCDA model proved to be effective for systematic comparison and informed strategic decision making. However, RED III regulatory requirements may favor ammonia or electrolysis for renewable fuel of non-biological origin production, emphasizing the need for long-term strategic planning to maintain competitiveness. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1347 KB

Open AccessProceeding Paper

NIR Spectral Analysis in Twin-Screw Melt Granulation: Effects of Binder Content, Screw Design, and Temperature

by Jacquelina C. Lobos de Ponga, Ivana M. Cotabarren, Juliana Piña, Ana L. Grafia and Mariela F. Razuc

Eng. Proc. 2025, 117(1), 20; https://doi.org/10.3390/engproc2025117020 - 8 Jan 2026

Viewed by 427

Abstract

This study evaluates the feasibility of Near-Infrared (NIR) spectroscopy combined with chemometric modeling for monitoring twin-screw melt granulation. Lactose monohydrate was used as a model excipient and polyethylene glycol (PEG 6000) (Sistemas Analíticos S.A, Buenos Aires, Argentina) as a meltable binder. Granules were [...] Read more.

This study evaluates the feasibility of Near-Infrared (NIR) spectroscopy combined with chemometric modeling for monitoring twin-screw melt granulation. Lactose monohydrate was used as a model excipient and polyethylene glycol (PEG 6000) (Sistemas Analíticos S.A, Buenos Aires, Argentina) as a meltable binder. Granules were produced under different processing conditions by varying binder content, screw configuration (kneading or conveying elements), and measurement temperature. NIR spectra were acquired on-line on a conveyor belt and analyzed using Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression. The regression models showed excellent predictive performance for PEG 6000 content in lactose-based granules, with coefficients of determination higher than 0.998 for both raw and preprocessed spectral data. PCA successfully discriminated between granulated and non-granulated materials, as well as between granules produced with different screw configurations, demonstrating the sensitivity of the technique to processing conditions and granule formation mechanisms. In addition, spectral differences associated with measurement temperature were detected, with derivative-based preprocessing improving the discrimination between warm and cooled granules. Overall, the results demonstrate that NIR spectroscopy, coupled with multivariate analysis, is a robust and non-invasive tool for real-time monitoring of twin-screw melt granulation, with strong potential to enhance process understanding, control, and product consistency in continuous pharmaceutical manufacturing. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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23 pages, 4136 KB

Open AccessProceeding Paper

Advances in Pharmaceutical Processing and Particle Engineering of Garlic Extract-Based Formulations for Antifungal Therapy Against Candida tropicalis

by Bindu Sadanandan and Kavyasree Marabanahalli Yogendraiah

Eng. Proc. 2025, 117(1), 21; https://doi.org/10.3390/engproc2025117021 - 8 Jan 2026

Viewed by 1148

Abstract

The increasing resistance of Candida tropicalis to conventional antifungal agents has necessitated the development of effective, biocompatible alternatives derived from natural sources. Garlic (Allium sativum), known for its potent antimicrobial activity, contains 33 bioactive sulfur compounds, some of them being allicin, [...] Read more.

The increasing resistance of Candida tropicalis to conventional antifungal agents has necessitated the development of effective, biocompatible alternatives derived from natural sources. Garlic (Allium sativum), known for its potent antimicrobial activity, contains 33 bioactive sulfur compounds, some of them being allicin, ajoene, and diallyl sulfides, that exhibit strong antifungal effects. However, the clinical application of garlic extract in pharmaceutical formulations remains limited due to its chemical instability, rapid degradation, and limited bioavailability. This review highlights recent advancements in pharmaceutical processing and particle engineering approaches to enhance the stability, delivery, and therapeutic efficacy of garlic extract-based antifungal formulations. Key strategies such as nanoparticle encapsulation, nanoemulsification, advanced drying techniques, and hydrogel-based delivery systems are discussed as effective approaches to enhance the stability and antifungal performance of garlic extract formulations. Special attention is given to hydrogel-based systems due to their excellent mucoadhesive properties, ease of application, and sustained release potential, making them ideal for treating localized C. tropicalis infections. The review also discusses formulation challenges and in vitro evaluation parameters, including minimum inhibitory concentration, minimum fungicidal concentration, and biofilm inhibition. By analyzing recent findings and technological trends, this review underscores the potential of garlic extract-based particle-engineered systems as sustainable and effective antifungal therapies. The scope of this review includes an in-depth evaluation of garlic extract-derived formulations, the application of particle processing technologies, and their translational potential in the design of next-generation antifungal delivery systems for managing C. tropicalis infections. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 1017 KB

Open AccessProceeding Paper

Modelling of Open Circuit Cooling Systems Chemical Emissions to River Water via Blowdown Water and Their Impact on the Quality of Effluents Discharged

by Pavlo Kuznietsov, Olha Biedunkova, Alla Pryshchepa and Oleg Mandryk

Eng. Proc. 2025, 117(1), 22; https://doi.org/10.3390/engproc2025117022 - 8 Jan 2026

Cited by 1 | Viewed by 563

Abstract

Introduction: Open-circuit cooling systems (OCCSs), integral to many industrial processes, often release blowdown water containing elevated concentrations of treatment chemicals. These discharges, if uncontrolled, pose substantial risks to aquatic ecosystems and human health. This study addresses the environmental implications of chemical emissions from [...] Read more.

Introduction: Open-circuit cooling systems (OCCSs), integral to many industrial processes, often release blowdown water containing elevated concentrations of treatment chemicals. These discharges, if uncontrolled, pose substantial risks to aquatic ecosystems and human health. This study addresses the environmental implications of chemical emissions from OCCS blowdown through the development of a predictive model designed to estimate contaminant concentrations in receiving water bodies. Methods: The research employs a computational model based on mass-balance equations to simulate the dynamics of chemical emissions from blowdown water. It incorporates key operational variables, including flow rates, degradation rates, and evaporation characteristics. The model evaluates two chemical dosing strategies, continuous and fractional, and their resultant pollutant dispersal patterns in river systems. Validation was performed using empirical data from sulfuric acid (H₂SO₄) applications at a nuclear power plant between 2015 and 2022. Results: The model demonstrated strong agreement with observed sulfate ion concentrations in the receiving water body, confirming its predictive reliability. Continuous dosing resulted in stable levels of pollutants, while fractional dosing caused temporary peaks that did not exceed regulatory limits. Conclusion: The modeling of blowdown water reveals important implications for river water quality and suggests that current wastewater management practices may be insufficient, benefiting from the integration of predictive modeling for blowdown discharges in industrial settings. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 1087 KB

Open AccessProceeding Paper

Computational Studies of Thiosemicarbazone-Based Metal Complexes and Their Biological Applications

by Kulsum Hashmi, Satya, Priya Mishra, Ekhlakh Veg, Tahmeena Khan and Seema Joshi

Eng. Proc. 2025, 117(1), 23; https://doi.org/10.3390/engproc2025117023 - 13 Jan 2026

Viewed by 574

Abstract

Thiosemicarbazones are known for their versatile coordination behavior and wide-ranging applications in the field of materials science, catalysis, and medicinal chemistry. Several investigations have reported on the biological potential of transition metal complexes of TSCs. In addition, the structural, electronic, and reactive properties [...] Read more.

Thiosemicarbazones are known for their versatile coordination behavior and wide-ranging applications in the field of materials science, catalysis, and medicinal chemistry. Several investigations have reported on the biological potential of transition metal complexes of TSCs. In addition, the structural, electronic, and reactive properties of these complexes are explored through computational studies using molecular docking and density functional theory (DFT). Such investigations not only support the interpretation of experimental results but also influence synthetic design by predicting the structural behavior of the complexes. In this study, we explore the computational studies of thiosemicarbazone metal complexes along with their biological activities. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 3080 KB

Open AccessProceeding Paper

Natural Zeolites-Supported Green-Synthesized CeO₂@Polybenzimidazole Hybrid Materials for Dye Degradation

by Katerina Zaharieva, Silvia Dimova, Filip Ublekov and Hristo Penchev

Eng. Proc. 2025, 117(1), 24; https://doi.org/10.3390/engproc2025117024 - 13 Jan 2026

Viewed by 436

Abstract

Natural zeolites—clinoptilolite from Golobradovo and mordenite from Lyaskovets deposits, Bulgaria—were used for the preparation of zeolite-supported CeO₂@Polybenzimidazole (PBI) hybrid materials, incorporating green-synthesized ceria using Veronica officinalis L extract. In order to prepare the hybrid composites, the zeolites/CeO₂ were surface-impregnated with [...] Read more.

Natural zeolites—clinoptilolite from Golobradovo and mordenite from Lyaskovets deposits, Bulgaria—were used for the preparation of zeolite-supported CeO₂@Polybenzimidazole (PBI) hybrid materials, incorporating green-synthesized ceria using Veronica officinalis L extract. In order to prepare the hybrid composites, the zeolites/CeO₂ were surface-impregnated with an ethanolic KOH solution of meta-PBI, which served as a dispersant medium. The composites were investigated by WDXRF, PXRD, FTIR, POM, and TG. The materials were tested in the photocatalytic degradation of 5 ppm Reactive Black 5 dye under UV light. The higher degree of degradation (98%) was achieved using mordenite/CeO₂@PBI as the photocatalyst compared to the clinoptilolite/CeO₂@PBI (14%). Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 775 KB

Open AccessProceeding Paper

Predictive Modeling of Polyphenol Concentration After Sequencing Batch Reactor Winery Wastewater Treatment

by Sérgio A. Silva, António Pirra, José A. Peres and Marco S. Lucas

Eng. Proc. 2025, 117(1), 25; https://doi.org/10.3390/engproc2025117025 - 15 Jan 2026

Viewed by 477

Abstract

Winery wastewater contains recalcitrant pollutants, such as phenolic compounds, which can hinder biological treatment processes. While monitoring these systems is essential to prevent treatment failure, quantifying recalcitrant compounds through conventional methods can be time-consuming and costly due to complex analytical procedures and chemical [...] Read more.

Winery wastewater contains recalcitrant pollutants, such as phenolic compounds, which can hinder biological treatment processes. While monitoring these systems is essential to prevent treatment failure, quantifying recalcitrant compounds through conventional methods can be time-consuming and costly due to complex analytical procedures and chemical disposal. In this study, machine learning (ML) was used to predict polyphenol concentration after the biological treatment of winery wastewater using a sequencing batch reactor (SBR). ML models, including ElasticNet (ENet), Multi-Layer Perceptron Regressor (MLPR), and Support Vector Regressor (SVR), were developed and tested using a small, high-dimensional dataset and leave-one-out cross-validation (LOOCV). Feature selection and hyperparameter tuning were applied to improve model performance. After optimization, the SVR model achieved the best performance, with MAE, MAPE, and R² of 0.88 mg/L, 9.3%, and 0.75, respectively. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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6 pages, 358 KB

Open AccessProceeding Paper

Phosphate Removal in a Pilot Filter System Using Sacha Inchi Cuticle and Eggshells

by Andrea Catalina Rodríguez-Torres, Nury Lorena López-Bermúdez, Angela María Otálvaro-Álvarez and Carlos Peña-Guzmán

Eng. Proc. 2025, 117(1), 26; https://doi.org/10.3390/engproc2025117026 - 19 Jan 2026

Viewed by 384

Abstract

This study investigated the removal of phosphates from water using an adsorbent composed of Sacha Inchi cuticle and eggshell. For this, a filtration system was constructed using 10 cm diameter PVC pipes and employing 155.56 g of the adsorbent, which corresponded to a [...] Read more.

This study investigated the removal of phosphates from water using an adsorbent composed of Sacha Inchi cuticle and eggshell. For this, a filtration system was constructed using 10 cm diameter PVC pipes and employing 155.56 g of the adsorbent, which corresponded to a filtering medium volume of 1.3 × 10⁻³ m³. With these design parameters, a working flow rate of 0.020 m³/h and a filtration velocity of 2.52 m³/(m²·h) were established. To test the system, 8 L of a solution with a concentration of 133 mg/L of PO₄⁻³ was prepared. As a result, a PO₄⁻³ removal percentage of 35.58 ± 1.57% was obtained. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 632 KB

Open AccessProceeding Paper

Simulation of Green Diesel by Hydrotreatment of Waste Vegetable Oil

by Pascal Mwenge, Thubelihle Mahlangu and Andani Munonde

Eng. Proc. 2025, 117(1), 27; https://doi.org/10.3390/engproc2025117027 - 20 Jan 2026

Viewed by 946

Abstract

Due to the world’s rising energy demand and reliance on fossil fuels, exploring cleaner energy sources is urgent. Green diesel from renewable resources, such as waste vegetable oil, is promising because it is compatible with petroleum diesel from fossil fuels. This study examined [...] Read more.

Due to the world’s rising energy demand and reliance on fossil fuels, exploring cleaner energy sources is urgent. Green diesel from renewable resources, such as waste vegetable oil, is promising because it is compatible with petroleum diesel from fossil fuels. This study examined the simulation of the hydrotreatment process of waste cooking oil (WCO) to produce green diesel. ChemCAD version 8.1 was used to develop the simulation, along with a kinetic model based on the Langmuir–Hinshelwood mechanism (an LH-C-ND model), where fatty acids, such as oleic, stearic, and palmitic acid, in WCO are converted into long-chain hydrocarbons (C15, C16, C17, and C18). The influence of process parameters on green diesel yield was assessed at various temperatures, pressures, and H₂/oil ratios. The best process conditions for green diesel production were identified as a temperature of 275 °C, a pressure of 30 bars, and an H₂/oil ratio of 0.3. Minimising the formation of CO₂, CO, and water. Under these conditions, a high green diesel yield was achieved, with WCO conversion exceeding 90%, and over 80% of the products were suitable for green diesel. This research supports SDG 7, which aims for universal access to affordable, reliable, sustainable, and modern energy, by exploring cleaner energy options, such as green diesel from waste vegetable oil. It is recommended to perform a life cycle assessment to evaluate the overall environmental impact. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 1172 KB

Open AccessProceeding Paper

Development of an ANFIS-Based Intelligent Control System for Free Chlorine Removal from Industrial Wastewater Using Ion-Exchange Resin

by Alisher Rakhimov, Rustam Bozorov, Ahror Tuychiev, Shuhrat Mutalov, Jaloliddin Eshbobaev and Alisher Jabborov

Eng. Proc. 2025, 117(1), 28; https://doi.org/10.3390/engproc2025117028 - 20 Jan 2026

Cited by 1 | Viewed by 399

Abstract

The removal of residual free chlorine ions from industrial wastewater is a critical step toward achieving sustainable and environmentally compliant water reuse. Excess chlorine in sludge collector water causes corrosion of process equipment, inhibits biological treatment, and leads to toxic discharge effects. In [...] Read more.

The removal of residual free chlorine ions from industrial wastewater is a critical step toward achieving sustainable and environmentally compliant water reuse. Excess chlorine in sludge collector water causes corrosion of process equipment, inhibits biological treatment, and leads to toxic discharge effects. In this study, an intelligent control strategy was developed for an ion-exchange-based dechlorination process to dynamically regulate chlorine concentration in the effluent stream. A pilot-scale ion-exchange filtration unit, designed with a nominal capacity of 500 L h⁻¹, was constructed using a strong-base anion-exchange resin to selectively adsorb chloride and free chlorine ions. A total of 200 experimental observations were obtained to characterize the nonlinear relationship between inlet flow rate and outlet chlorine concentration under varying operational conditions. Based on these experimental data, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed in MATLABR2025 to simulate and control the ion-exchange process. Two model-optimization techniques, Grid Partition + Hybrid and Subtractive Clustering + Hybrid, were applied. The subtractive clustering approach demonstrated faster convergence and superior accuracy, achieving RMSE = 0.147 mg L⁻¹, MAE = 0.101 mg L⁻¹, and R² = 0.993, outperforming the grid-partition model (RMSE ≈ 0.29, R² ≈ 0.97). The resulting ANFIS model was subsequently integrated into a MATLAB/Simulink-based intelligent control system for real-time regulation of chlorine concentration. A comparative dynamic simulation was performed between the proposed ANFIS controller and a conventional PID (Proportional-Differential-Integral) controller. The results revealed that the ANFIS controller achieved a faster response (rise time ≈ 28 s), lower overshoot (≈6%), and shorter settling time (≈90 s) compared to the PID controller (rise time ≈ 35 s, overshoot ≈ 18%, settling time ≈ 120 s). These improvements demonstrate the ability of the proposed model to adapt to nonlinear process behavior and to maintain stable operation under varying flow conditions. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 377 KB

Open AccessProceeding Paper

Solvent Selection for Efficient CO₂ Capture

by Adham Norkobilov, Sanjar Ergashev, Zafar Turakulov, Sarvar Rejabov and Azizbek Kamolov

Eng. Proc. 2025, 117(1), 29; https://doi.org/10.3390/engproc2025117029 - 20 Jan 2026

Viewed by 606

Abstract

Carbon capture is an essential technology for reducing industrial CO₂ emissions, particularly in the power and cement sectors. Among the various capture methods, solvent-based absorption systems are widely used due to their efficiency and scalability, making the selection of the right solvent [...] Read more.

Carbon capture is an essential technology for reducing industrial CO₂ emissions, particularly in the power and cement sectors. Among the various capture methods, solvent-based absorption systems are widely used due to their efficiency and scalability, making the selection of the right solvent critical for near-term applications. This study analyzes several solvents for use in an absorption-based CO₂ capture system, emphasizing identifying the most suitable solvent for 2025–2030. The research methodology involves process modeling in Aspen Plus, sensitivity analysis, and evaluation of the regeneration duty for each solvent. The objective is to achieve at least 90% CO₂ capture and 95% CO₂ purity. The flue gas composition considered in this analysis is 19.8% CO₂, 9.3% O₂, 63% N₂, 7.5% H₂O, and other trace gases. Various solvents are evaluated to determine their effectiveness in capturing CO₂ while minimizing the energy consumption during solvent regeneration. A sensitivity analysis was conducted to optimize the system’s performance based on the solvent type, operating conditions, and regeneration duty. The results showed that amine blends demonstrated a CO₂ capture rate of 92% and a CO₂ purity of 96%, with regeneration energy requirements of around 3.2 GJ/ton of CO₂, significantly lower than those of traditional MEA systems, which typically require around 4.0 GJ/ton. In contrast, ionic liquids showed a CO₂ capture rate of 89% and a purity of 95%, with a regeneration energy of 2.8 GJ/ton, though their current cost is higher, limiting their immediate large-scale application. Annual capital expenditure (CAPEX) calculation revealed that amine blends could potentially reduce the CAPEX by 15–20% compared to MEA, while amino acid salts showed similar CAPEX reductions with a capture efficiency of 90%. Overall, the results indicate that hybrid amine solvents are the most cost-effective and practical solution for 2025–2030, with ionic liquids and amino acid salts emerging as promising alternatives as their costs decrease. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 1445 KB

Open AccessProceeding Paper

Integrated DFT Study of CO₂ Capture and Utilization in Gingerol Extraction Using Choline Chloride–Lactic Acid Deep Eutectic Solvent

by Abdulsobur Olatunde and Toyese Oyegoke

Eng. Proc. 2025, 117(1), 30; https://doi.org/10.3390/engproc2025117030 - 21 Jan 2026

Viewed by 363

Abstract

Carbon dioxide (CO₂) emissions are a major contributor to climate change, requiring sustainable carbon capture and utilization (CCU) strategies. This study employed density functional theory (DFT) to assess a choline chloride–lactic acid deep eutectic solvent (CHL–LAC DES) as a dual system [...] Read more.

Carbon dioxide (CO₂) emissions are a major contributor to climate change, requiring sustainable carbon capture and utilization (CCU) strategies. This study employed density functional theory (DFT) to assess a choline chloride–lactic acid deep eutectic solvent (CHL–LAC DES) as a dual system for CO₂ capture and gingerol extraction. Using the wB97X-D functional theory for energy calculation with PM3-optimized geometries, the DES exhibited stronger CO₂ binding (–0.86 eV) than monoethanolamine (–0.234 eV) and a higher affinity for 6-gingerol (–1.87 eV). These results suggest that CHL–LAC DES can simultaneously capture CO₂ and extract bioactive compounds, advancing green pharmaceutical and integrated CCU applications. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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12 pages, 893 KB

Open AccessProceeding Paper

Real-Time Pollutant Forecasting Using Edge–AI Fusion in Wastewater Treatment Facilities

by Siva Shankar Ramasamy, Vijayalakshmi Subramanian, Leelambika Varadarajan and Alwin Joseph

Eng. Proc. 2025, 117(1), 31; https://doi.org/10.3390/engproc2025117031 - 22 Jan 2026

Viewed by 743

Abstract

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and [...] Read more.

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analyzing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge-computing-based smart framework that captures data from sensors, including ultraviolet, electrochemical, and microfluidic, along with other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyzes the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments, and other treatment plans. A logic layer is designed at the server level to process the real-time data from the sensor clusters and identify the discharge of nutrients, metals, and emerging contaminants in the water that affect the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis, and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonize sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad hoc analyses even during critical conditions. A user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI-enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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14 pages, 1253 KB

Open AccessProceeding Paper

Performance Evaluation of an Improved Particle Swarm Optimization Algorithm Against Nature-Inspired Methods for Photovoltaic Parameter

by Oussama Khouili, Fatima Wardi, Mohamed Louzazni and Mohamed Hanine

Eng. Proc. 2025, 117(1), 32; https://doi.org/10.3390/engproc2025117032 - 22 Jan 2026

Viewed by 469

Abstract

Accurate parameter extraction is essential for reliable photovoltaic (PV) modeling and performance assessment. This study proposes an improved Particle Swarm Optimization (IPSO) algorithm and presents a comparative evaluation against particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Artificial Bee Colony (ABC), [...] Read more.

Accurate parameter extraction is essential for reliable photovoltaic (PV) modeling and performance assessment. This study proposes an improved Particle Swarm Optimization (IPSO) algorithm and presents a comparative evaluation against particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Artificial Bee Colony (ABC), simulated annealing (SA), and Nelder–Mead (NM) for estimating the parameters of single-, double-, and triple-diode PV models. All algorithms are tested using identical experimental I–V data and evaluated in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), coefficient of determination (R²⁾, and computational time. The proposed IPSO significantly enhances convergence accuracy and stability for SDMs and DDMs, achieving very low best-case RMSE values with R² exceeding 0.9999. For the more complex TDM, IPSO attains the lowest best-case error, while DE and ABC exhibit superior robustness in terms of mean error and variance. Overall, the results demonstrate the effectiveness of the proposed IPSO and highlight the trade-off between accuracy and robustness when selecting optimization algorithms for PV parameter extraction. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 812 KB

Open AccessProceeding Paper

Hybrid Quantum-Fuzzy Control for Intelligent Steam Heating Management in Thermal Power Plants

by Noilakhon Yakubova, Ayhan Istanbullu, Isomiddin Siddiqov and Komil Usmanov

Eng. Proc. 2025, 117(1), 33; https://doi.org/10.3390/engproc2025117033 - 26 Jan 2026

Cited by 2 | Viewed by 340

Abstract

In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of quantum control methodologies-grounded in established principles of quantum mechanics—into [...] Read more.

In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of quantum control methodologies-grounded in established principles of quantum mechanics—into the automation of thermal processes in power plant operations. Specifically, it investigates a hybrid quantum-fuzzy control system for managing steam heating processes, a critical subsystem in thermal power generation. Unlike conventional control strategies that often struggle with nonlinearity, time delays, and parameter uncertainty, the proposed method incorporates quantum-inspired optimization algorithms to enhance adaptability and robustness. The quantum component, based on recognized models of coherent control and quantum interference, is utilized to refine the inference mechanisms within the fuzzy logic framework, allowing more precise handling of state transitions in multivariable environments. A simulation model was constructed using validated physical parameters of a pilot-scale steam heating unit, and the methodology was tested against baseline scenarios with conventional proportional-integral-derivative (PID) control. Experimental protocols and statistical analysis confirmed measurable improvements: up to 25% reduction in fuel usage under specific operational conditions, with an average of 1 to 2% improvement in energy efficiency. The results suggest that quantum-enhanced intelligent control offers a feasible pathway for bridging the gap between quantum theoretical models and macroscopic thermal systems, contributing to the development of more energy-resilient industrial automation solutions. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 556 KB

Open AccessProceeding Paper

Assessing the Environmental Sustainability and Footprint of Industrial Packaging

by Sk. Tanjim Jaman Supto and Md. Nurjaman Ridoy

Eng. Proc. 2025, 117(1), 34; https://doi.org/10.3390/engproc2025117034 - 27 Jan 2026

Viewed by 1362

Abstract

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental [...] Read more.

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental footprint of industrial packaging by integrating recent developments in life cycle assessment (LCA), ecological footprint (EF) methodologies, material innovations, and circular economy models. The assessment examines the sustainability performance of conventional and alternative packaging materials, plastics, aluminum, corrugated cardboard, and polylactic acid (PLA). Findings indicate that although corrugated cardboard is renewable, it still presents a measurable environmental burden, with evidence suggesting that incorporating solar energy into production can reduce its footprint by more than 12%. PLA-based trays demonstrate promising environmental performance when sourced from renewable feedstocks and directed to appropriate composting systems. Despite these advancements, several systemic challenges persist, including ecological overshoot in industrial regions where EF may exceed local biocapacity limitations in waste management infrastructure, and significant economic trade-offs. Transportation-related emissions and scalability constraints for bio-based materials further hinder large-scale adoption. Existing research suggests that integrating sustainable packaging across supply chains could meaningfully reduce environmental impacts. Achieving this transition requires coordinated cross-sector collaboration, standardized policy frameworks, and embedding advanced environmental criteria into packaging design and decision-making processes. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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14 pages, 1237 KB

Open AccessProceeding Paper

Fuzzy-Logic-Based Intelligent Control of a Cabinet Solar Dryer for Plantago major Leaves Under Real Climatic Conditions in Tashkent

by Komil Usmanov, Noilakhon Yakubova, Shakhnoza Sultanova and Zafar Turakulov

Eng. Proc. 2025, 117(1), 35; https://doi.org/10.3390/engproc2025117035 - 28 Jan 2026

Cited by 1 | Viewed by 662

Abstract

Solar drying is an energy-efficient and environmentally friendly method for dehydrating agricultural and medicinal products; however, its performance is strongly affected by fluctuating climatic conditions and nonlinear heat and mass transfer processes. In cabinet-type solar dryers, maintaining the drying air temperature and relative [...] Read more.

Solar drying is an energy-efficient and environmentally friendly method for dehydrating agricultural and medicinal products; however, its performance is strongly affected by fluctuating climatic conditions and nonlinear heat and mass transfer processes. In cabinet-type solar dryers, maintaining the drying air temperature and relative humidity within optimal ranges is particularly critical for medicinal plants such as Plantago major leaves, which are sensitive to overheating and non-uniform drying. In this study, a Mamdani-type fuzzy logic-based intelligent control system is developed and experimentally validated for a cabinet solar dryer operating under real summer climatic conditions in Tashkent, Uzbekistan. The proposed controller regulates fan speed using drying air temperature and relative humidity as inputs. To evaluate its effectiveness, the fuzzy logic controller is benchmarked against a conventionally tuned Proportional–Integral–Derivative (PID) controller under identical operating and climatic conditions. A coupled thermodynamic–hygrometric dynamic model of the drying process is implemented in MATLAB/Simulink (R2024a) to support controller design and analysis. Experimental results demonstrate that the fuzzy logic controller maintains the drying air temperature within the optimal range of 45–50 °C despite significant fluctuations in solar irradiance (650–900 W/m²), whereas the PID-controlled system exhibits noticeable overshoot and oscillations. Compared with PID control, the fuzzy-controlled dryer achieves a smoother reduction in relative humidity, a reduction of approximately 22% in total drying time for the same final moisture content (8–10% wet basis), and an 18% decrease in auxiliary electrical energy consumption. In addition, tray-wise moisture measurements indicate improved drying uniformity under fuzzy control, with moisture variation remaining within ±4%. Overall, the results confirm that fuzzy-logic-based intelligent control provides a robust and energy-efficient solution for cabinet solar dryers operating under hot continental climatic conditions, offering clear advantages over conventional PID control in terms of stability, drying performance, and uniformity. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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12 pages, 1312 KB

Open AccessProceeding Paper

Multi-Criteria Decision Analysis-Supported Evaluation of Biowaste Anaerobic Digestion Options in Slovakia

by Miroslav Variny, Martin Danielič and Dominika Polakovičová

Eng. Proc. 2025, 117(1), 36; https://doi.org/10.3390/engproc2025117036 - 28 Jan 2026

Viewed by 315

Abstract

Slovakia’s biomethane production potential represents up to 10% of Slovakia’s natural gas consumption, which is largely unexploited. The aim of this paper is to develop a model of each available technology (continuous, dry batch, and wet batch) as well as that of a [...] Read more.

Slovakia’s biomethane production potential represents up to 10% of Slovakia’s natural gas consumption, which is largely unexploited. The aim of this paper is to develop a model of each available technology (continuous, dry batch, and wet batch) as well as that of a biogas treatment unit and evaluate the energetic, economic, and environmental potential of building a new anaerobic digestion plant in Slovakia, considering four plant locations with feedstock abundance within a 30 km perimeter. Feedstock composition and availability, energy integration, and product usability are evaluated. The applied multi-criteria decision analysis (MCDA) considers four evaluation criteria: return on investment (ROI), CO₂ emissions production, potential industrial biowaste revenue, and municipal density within the operational region. Biogas plant deployment analysis yielded the Levice facility as top-ranked, primarily due to its minimal environmental impact and superior logistical performance, closely followed by the Žilina, Michalovce, and Prešov facilities. When comparing biomethane production facilities, the Levice plant was excluded due to economic infeasibility, and the Žilina facility emerged as the optimal choice, particularly due to its superior ROI performance and the largest biomethane production potential of over 1 million m³ biomethane per year. Thus, biomethane station deployment in Slovakia has proved feasible and can enhance the energy self-sustainability of the country and contribute to meeting the decarbonization goals. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 1231 KB

Open AccessProceeding Paper

The Effects of Acid Hydrolysis Parameters on the Production of Monomeric Sugars from Chicken Manure

by John Erick Siosana, John Kevin Anos, Irene Cheska Bacolcol, Philip Joseph Solmirano, Dominique Nikki Villareal, Jerry G. Olay and Rugi Vicente C. Rubi

Eng. Proc. 2025, 117(1), 37; https://doi.org/10.3390/engproc2025117037 - 29 Jan 2026

Viewed by 623

Abstract

The utilization of waste biomass such as chicken manure (CM) for producing valuable products like fermentable sugars has gained increasing research attention. However, limited studies have explored the effect of acid pretreatment on sugar recovery efficiency specifically from CM. This study investigates the [...] Read more.

The utilization of waste biomass such as chicken manure (CM) for producing valuable products like fermentable sugars has gained increasing research attention. However, limited studies have explored the effect of acid pretreatment on sugar recovery efficiency specifically from CM. This study investigates the production of glucose and xylose from CM using dilute sulfuric acid (H₂SO₄) at concentrations of 0.6, 0.8, and 1.0 M under varying conditions. The results indicate that the highest yields were achieved from decrystallized CM, producing 46.21 mg glucose/g CM and 8.47 mg xylose/g CM under optimal conditions of 0.6 M H₂SO₄ and 100 °C. In contrast, non-decrystallized CM yielded 13.98 mg glucose/g CM and 1.67 mg xylose/g CM under 1.0 M H₂SO₄ and 100 °C. The decrystallization process using concentrated sulfuric acid effectively disrupted the lignin structure and partially hydrolyzed hemicellulose, enhancing cellulose accessibility during subsequent dilute acid hydrolysis. The study also revealed that glucose and xylose yields decreased as the dilute acid concentration increased from 0.6 to 0.8 M and temperature rose from 80 to 100 °C for decrystallized CM. Conversely, for non-decrystallized CM, sugar yields increased with higher acid concentration and temperature. These findings highlight the critical role of pretreatment in improving sugar recovery from CM and suggest that optimizing acid concentration and thermal conditions can enhance the efficiency of biomass conversion. This research contributes to the sustainable valorization of agricultural waste into bio-based products. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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19 pages, 2008 KB

Open AccessProceeding Paper

A Novel Security Index for Assessing Information Systems in Industrial Organizations Using Web Technologies and Fuzzy Logic

by Sulieman Khaddour, Fares Abu-Abed and Valery Bogatikov

Eng. Proc. 2025, 117(1), 38; https://doi.org/10.3390/engproc2025117038 - 29 Jan 2026

Viewed by 388

Abstract

Industrial information systems based on web technologies (ISOWT) face escalating security challenges, particularly in critical sectors such as energy. Traditional qualitative security assessments often lack the ability to deliver actionable, real-time insights for managing complex, dynamic threats. This paper proposes a novel security [...] Read more.

Industrial information systems based on web technologies (ISOWT) face escalating security challenges, particularly in critical sectors such as energy. Traditional qualitative security assessments often lack the ability to deliver actionable, real-time insights for managing complex, dynamic threats. This paper proposes a novel security index for evaluating ISOWT in industrial organizations by integrating fuzzy logic, metric-based evaluation, fuzzy Markov chains, and multi-agent systems. The proposed index quantifies deviations from an ideal “center of safety,” enabling early risk detection and proactive mitigation. The methodology is validated through two real-world case studies on Syria’s energy sector, namely the Ministry of Electricity website and Mahrukat fuel management system. Experimental results demonstrate substantial improvements, including a 45.9–58.5% increase in security index, 56.9–60.3% reduction in page load times, and 78.3–82.4% decrease in detected vulnerabilities. Comparative analysis shows that the proposed approach outperforms existing methods in terms of quantitative precision, real-time monitoring, and predictive capabilities. The proposed framework is scalable, automated, and adaptable, addressing key limitations of existing ISOWT security assessment techniques and providing a robust tool for enhancing system resilience. Its flexibility enable applicability across diverse industrial domains, contributing to advanced cybersecurity practices for critical infrastructure. Future work will focus on integrating advanced technologies, expanding the framework to additional sectors, developing adaptive fuzzy models, accounting for human factors, and improving visualization techniques to further address the evolving security challenges faced by industrial organizations. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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13 pages, 1390 KB

Open AccessProceeding Paper

Synthesis and Characterization of Benzene-1,2,4-triyl Tris(2-(3-carboxy-4-hydroxybenzenesulfonate) Acetate)

by Ruzimurod Jurayev

Eng. Proc. 2025, 117(1), 39; https://doi.org/10.3390/engproc2025117039 - 28 Jan 2026

Viewed by 438

Abstract

Benzene-1,2,4-triyl tris(2-(3-carboxy-4-hydroxybenzenesulfonate) acetate) synthesis is an important step forward in the synthesis of multifunctional organic molecules, which have potential uses in material science and medical chemistry, among other domains. In analytical chemistry, it can also be utilized for metal ion determination. This work [...] Read more.

Benzene-1,2,4-triyl tris(2-(3-carboxy-4-hydroxybenzenesulfonate) acetate) synthesis is an important step forward in the synthesis of multifunctional organic molecules, which have potential uses in material science and medical chemistry, among other domains. In analytical chemistry, it can also be utilized for metal ion determination. This work presents a thorough and methodical approach to the synthesis of this complicated trisulfonated aromatic ester, emphasizing the effectiveness and scaling possibilities of the methodology. Choosing the right precursors to ensure that each one would contribute to the intended molecular architecture was the first step in the synthesis process. In the initial stages of the synthesis process, oxyhydroquinone was reacted with chloroacetyl chloride for 20 h. As a result, benzene-1,2,4-triyl tris(2-chloroacetate) of triatomic phenol-oxyhydroquinone was formed. The resulting phenacetyl chloride was reacted with sodium sulfosalicylate in the presence of N,N-dimethylformamide (DMFA). Benzene-1,2,4-triyl tris(2-(3-carboxy-4-hydroxybenzenesulfonate) acetate) was formed. To obtain high yields and purity, careful adjustment of the reaction conditions that including temperature, solvent selection, and reagent ratios was required. The synthesized molecule was characterized using advanced spectroscopic techniques such as NMR, IR, and UV spectrometry, which confirmed its structural integrity and functional group configuration. Benzene-1,2,4-triyl tris(2-(3-carboxy-4-hydroxybenzenesulfonate) acetate), the resultant product, has special physicochemical characteristics. In particular, it is more soluble and has the potential to be a useful intermediate in organic synthesis. Because it has several reactive sites, preliminary research indicates that it may be useful in the development of new polymeric materials and as a possible ligand in coordination chemistry. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 1841 KB

Open AccessProceeding Paper

Hydrophobic-to-Hydrophilic Transition of Polyethylene Surface via Salicylic Acid Grafting

by Ana Luisa Grafia and Silvia Elena Barbosa

Eng. Proc. 2025, 117(1), 40; https://doi.org/10.3390/engproc2025117040 - 30 Jan 2026

Viewed by 420

Abstract

Polyethylene is widely used in flexible packaging, but its hydrophobic and inert surface limits its compatibility with environmentally friendly water-based inks and paints. Conventional methods improve wettability only temporarily and with limited control. Here, we introduce a surface functionalization method in which salicylic [...] Read more.

Polyethylene is widely used in flexible packaging, but its hydrophobic and inert surface limits its compatibility with environmentally friendly water-based inks and paints. Conventional methods improve wettability only temporarily and with limited control. Here, we introduce a surface functionalization method in which salicylic acid is grafted onto polyethylene films through an aluminum-mediated alkylation process compatible with continuous film processing. Infrared-softened polyethylene films were sequentially sprayed with AlCl₃ and salicylic acid. Reaction occurrence was confirmed by chemical and morphological analyses, revealing the in situ formation of aluminum salicylate complexes anchored to the polyethylene surface. Wettability tests demonstrated enhanced compatibility with water-based paints. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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12 pages, 1042 KB

Open AccessProceeding Paper

Towards Sustainable Waste-to-Energy Solutions: Techno-Economic Insights from Scrap Tyre Pyrolysis in Nigeria

by Olusegun A. Ajayi, Daniel Iyanu Oluwatogbe, Umar Mogaji Muhammed and Toyese Oyegoke

Eng. Proc. 2025, 117(1), 41; https://doi.org/10.3390/engproc2025117041 - 2 Feb 2026

Viewed by 720

Abstract

This study assessed the techno-economic performance and energy efficiency of scrap tyre valorization through pyrolysis in Nigeria, comparing two configurations: a pyrolysis plant integrated with power generation (Scenario 1) and a standalone pyrolysis plant (Scenario 2). Process simulations were carried out using Aspen [...] Read more.

This study assessed the techno-economic performance and energy efficiency of scrap tyre valorization through pyrolysis in Nigeria, comparing two configurations: a pyrolysis plant integrated with power generation (Scenario 1) and a standalone pyrolysis plant (Scenario 2). Process simulations were carried out using Aspen Plus V12, and cost estimations were performed with the Aspen Process Economic Analyzer. For a feed capacity of 20 tons per hour, the pyrolysis process yielded steel wire (15.04%), char (35.57%), pyro-diesel (37.94%), gas (7.91%), and heavy oil (3.54%). Scenario 2 achieved a higher energy efficiency (94.44%) than Scenario 1 (51.23%). However, Scenario 1 demonstrated superior economic performance, with a Net Present Value (NPV) of USD 28.65 million and an Internal Rate of Return (IRR) of 34.48%, despite its higher capital investment of USD 27.63 million. Sensitivity analysis revealed that the selling price of pyro-diesel and the cost of scrap tyres were the most influential parameters affecting profitability. The findings provide useful insights for optimizing scrap tyre pyrolysis systems toward sustainable waste-to-energy applications in developing regions. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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12 pages, 764 KB

Open AccessProceeding Paper

Prediction of Drying Efficiency in Cabinet Solar Dryers for Medicinal Plants Using Artificial Neural Networks

by Komil Usmanov, Noilakhon Yakubova, Sarvar Rejabov, Jaloliddin Eshbobaev and Mirjalol Yusupov

Eng. Proc. 2025, 117(1), 42; https://doi.org/10.3390/engproc2025117042 - 2 Feb 2026

Viewed by 384

Abstract

This study presents an artificial neural network (ANN)-based predictive model for evaluating the drying efficiency of a cabinet-type solar dryer used for dehydrating Plantago major leaves under natural climatic conditions. The performance of solar drying systems is strongly affected by nonlinear and time-varying [...] Read more.

This study presents an artificial neural network (ANN)-based predictive model for evaluating the drying efficiency of a cabinet-type solar dryer used for dehydrating Plantago major leaves under natural climatic conditions. The performance of solar drying systems is strongly affected by nonlinear and time-varying factors such as solar irradiance, drying-chamber temperature, and ambient relative humidity, which limits the accuracy of conventional modeling approaches. To address this challenge, a multilayer feedforward ANN was developed using solar irradiance, chamber temperature, and relative humidity as input variables and drying efficiency as the output. Experimental data comprising 120 samples were collected during summer conditions and divided into training, validation, and testing subsets. The ANN was trained using the Levenberg–Marquardt algorithm and demonstrated strong predictive performance, achieving an overall correlation coefficient of R = 0.9556 and a low mean squared error of

1.22 \times 10^{- 4}

The results confirm that the proposed ANN model can reliably capture the nonlinear drying behavior and accurately predict drying efficiency, providing a practical tool for real-time performance evaluation and supporting the development of intelligent monitoring and control strategies for cabinet-type solar drying systems. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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14 pages, 971 KB

Open AccessProceeding Paper

Deep Learning for Cybersecurity Threat Detection in Industrial Process Control and Monitoring Systems

by Godfrey Perfectson Oise, Joy Akpowehbve Odimayomi, Belinda Nkem Unuigbokhai, Babalola Eyitemi Akilo and Samuel Abiodun Oyedotun

Eng. Proc. 2025, 117(1), 43; https://doi.org/10.3390/engproc2025117043 - 9 Feb 2026

Viewed by 732

Abstract

The increasing digital integration of Industrial Control Systems (ICS), including Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCSs), has improved operational efficiency while simultaneously increasing exposure to cyber threats. Traditional signature-based intrusion detection systems are limited in detecting novel and [...] Read more.

The increasing digital integration of Industrial Control Systems (ICS), including Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCSs), has improved operational efficiency while simultaneously increasing exposure to cyber threats. Traditional signature-based intrusion detection systems are limited in detecting novel and stealthy attacks in dynamic industrial environments. This study presents a deep learning–based anomaly detection framework for ICS cybersecurity using multivariate time-series data from sensors, actuators, and network traffic. Three architectures, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer models, are evaluated using the HAI Security Dataset. Experimental results show that the Transformer model achieves the highest accuracy (92%), followed by CNN (91%) and LSTM (90%), with all models attaining an F1-score of 91%. The Transformer demonstrates superior generalization by effectively modelling complex temporal dependencies. Key challenges, including data imbalance, overfitting, and limited interpretability, are discussed alongside potential mitigation strategies such as hybrid modelling, federated learning, and digital twin integration. The findings demonstrate the effectiveness of deep learning for scalable, real-time cybersecurity threat detection in industrial control environments. To address challenges such as class imbalance and overfitting, the study discusses mitigation strategies including regularization, early stopping, cost-sensitive learning, and future integration of data balancing and federated learning techniques for improved robustness and generalization. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1519 KB

Open AccessProceeding Paper

Precursor-Directed Synthesis of Graphitic Carbon Nitride–Biochar Composites for Improved Photodegradation of Recalcitrant Pharmaceuticals

by Felix Amaning-Kwarteng and Kingsley Safo

Eng. Proc. 2025, 117(1), 44; https://doi.org/10.3390/engproc2025117044 - 10 Feb 2026

Viewed by 381

Abstract

This study investigates how graphitic carbon nitride (g-C₃N₄), derived from melamine, urea, and thiourea, degrades recalcitrant pharmaceuticals. Among the materials used, g-C₃N₄ derived from urea showed the highest degradation of methyl orange (60.25%). When calcined with [...] Read more.

This study investigates how graphitic carbon nitride (g-C₃N₄), derived from melamine, urea, and thiourea, degrades recalcitrant pharmaceuticals. Among the materials used, g-C₃N₄ derived from urea showed the highest degradation of methyl orange (60.25%). When calcined with biochar derived from onion flower seed-cover biomass via pyrolysis and further activated with potassium hydroxide (KOH), it showed better adsorption and photodegradation results of 92.59%, 84.44%, 68.11%, and 61.11% for tetracycline, cefixime, ciprofloxacin, and carbamazepine, respectively. These results emphasize the potential of biochar-g-C₃N₄ composites as sustainable photocatalysts for water treatment focused on removing recalcitrant pharmaceutical contaminants. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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13 pages, 1032 KB

Open AccessProceeding Paper

Adaptive Fuzzy Control of Petroleum Extraction Columns Using Quantum-Inspired Optimization

by Noilakhon Yakubova, Komil Usmanov, Feruzakhon Sadikova and Shahnozakhon Sadikova

Eng. Proc. 2025, 117(1), 45; https://doi.org/10.3390/engproc2025117045 - 11 Feb 2026

Viewed by 359

Abstract

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to [...] Read more.

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. The hybrid quantum-inspired fuzzy controller is applied to a petroleum extraction column. The controller adopts fuzzy rule weights using a quantum-inspired optimization algorithm. Compared with classical PID and fuzzy controllers, it reduces settling time and solvent consumption. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted using synthetic data and varying operational parameters to evaluate control performance. The hybrid controller achieved a 0.7% reduction in phenol consumption and reduced temperature deviations by 2.2% compared to a baseline fuzzy controller. Energy savings ranged from 1% to 2% depending on the operating scenarios. These results were confirmed through repeated simulations and statistical analysis. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications. The statistical evaluation was based on repeated simulation runs and comparative performance metrics rather than physical experiments. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 508 KB

Open AccessProceeding Paper

The Separation of a CO₂ and H₂S Mixture

by Adham Norkobilov, Rakhmatullo Muradov, Abror Turakulov, Sanjar Ergashev and Zafar Turakulov

Eng. Proc. 2025, 117(1), 46; https://doi.org/10.3390/engproc2025117046 - 11 Feb 2026

Viewed by 549

Abstract

The separation and purification of carbon dioxide (CO₂) from sour gas streams is critical for emission reduction and industrial reuse. This study presents a chemical absorption-based process simulation of CO₂ (carbon dioxide) and H₂S (hydrogen sulfide) separation using [...] Read more.

The separation and purification of carbon dioxide (CO₂) from sour gas streams is critical for emission reduction and industrial reuse. This study presents a chemical absorption-based process simulation of CO₂ (carbon dioxide) and H₂S (hydrogen sulfide) separation using Aspen Plus V12.0, focusing on solvent-based treatment using an aqueous monoethanolamine (MEA) system selected based on industrial applicability and regeneration performance. The process was modeled for two gas streams originating from the Shurtan Gas Chemical Complex: a raw feed stream containing 3.42% CO₂ and 0.09% H₂S, and a treated dry gas containing 2.1% CO₂. The goal was to achieve high-purity CO₂ recovery (≥99.5%) with flow rates of 30 t/h and 20 t/h, respectively. Rate-based modeling was employed to simulate mass transfer and chemical kinetics in the absorber and regenerator columns. The simulation results indicated that at optimal solvent flow and absorber temperature (40–45 °C), over 98.6% CO₂ and 99.9% H₂S removal could be achieved. The specific energy requirement for solvent regeneration was estimated at 2.3 GJ per ton of CO₂, aligning with industrial efficiency benchmarks. The purified CO₂ is intended for use in the production of sodium carbonate (Na₂CO₃) at the Dehkanabad Potash Plant, which converts 20 t/h of CO₂ into 296,000 tons/year of calcined soda with 77% process efficiency. This approach enhances gas resource utilization while reducing atmospheric emissions. The model serves as a techno-economically viable foundation for scaling up CO₂ capture and utilization (CCU) in the Uzbek chemical industry. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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14 pages, 836 KB

Open AccessProceeding Paper

Processing of Gluten-Free Cupcakes Utilizing Plant-Based Lepidium sativum Seed Mucilage as a Fat Replacer

by Bhawna Tyagi, Karuna Singh, Muskan Chadha and Ratnakar Shukla

Eng. Proc. 2025, 117(1), 47; https://doi.org/10.3390/engproc2025117047 - 11 Feb 2026

Viewed by 421

Abstract

This study aimed to produce gluten-free cupcakes (GFCs) using a composite flour blend of Lepidium sativum seed mucilage (LSM) in different proportions. The GFCs substituted oil with 100% LSM (M100), revealing significant reduction in fat content (87.55%), protein 13.43 g/100 g, fiber 2.48 [...] Read more.

This study aimed to produce gluten-free cupcakes (GFCs) using a composite flour blend of Lepidium sativum seed mucilage (LSM) in different proportions. The GFCs substituted oil with 100% LSM (M100), revealing significant reduction in fat content (87.55%), protein 13.43 g/100 g, fiber 2.48 g/100 g, and carbohydrates 58.39 g/100 g. The M100 GFC showed favorable fatty acid ratios (1.80 MUFA:SFA, 0.95 PUFA:SFA, 0.97 omega 6:omega 9) and lipid health quality indices (0.72 atherogenicity index, 0.59 thrombogenic index, 1.39 health-promoting index, and 2.3% ∆desaturase index), with sensory acceptability at 7.07 ± 0.21. The LSM effectively replaced fat, enhanced functionality and improved sensory profiles 7.07 ± 0.21, suggesting its potential for industrial applications. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 229 KB

Open AccessProceeding Paper

Development and Characterization of an Apricot-Coconut Water Kefir Beverage: Evaluation of Physicochemical, Antioxidant, and Sensory Attributes

by Anisha Adya, Vishal Jha, Karuna Singh and Ratnakar Shukla

Eng. Proc. 2025, 117(1), 48; https://doi.org/10.3390/engproc2025117048 - 12 Feb 2026

Viewed by 398

Abstract

Water kefir is a non-dairy fermented water drink which includes lactic acid bacteria, acetic acid bacteria and yeasts which provide probiotic as well as antioxidant properties. Prunus armeniaca (apricot) is a promising raw material to develop a functional beverage because it is rich [...] Read more.

Water kefir is a non-dairy fermented water drink which includes lactic acid bacteria, acetic acid bacteria and yeasts which provide probiotic as well as antioxidant properties. Prunus armeniaca (apricot) is a promising raw material to develop a functional beverage because it is rich in carotenoids, vitamins, and phenolics. Coconut water is a natural hydrating substance and plant-based substrate. The aim of this study was to prepare and characterize apricot-coconut water kefir beverage, (ACWB) a fermented beverage having 20 g (w/v) dried apricot, 8 g (w/v) brown sugar, and 8 g (w/v) water kefir grains fermented together in 100 mL coconut water and compare its physicochemical, microbial, and antioxidant properties with a control sample excluded with dried apricot but having same concentration of rest of the ingredients. After fermentation, total soluble solids (TSS), pH, titratable acidity (TA), water activity (aw), total bacterial count (TBC), DPPH radical-scavenging activity, and total phenolic contents (TPC) were measured. ACWB exhibited significantly higher values (p < 0.05) in terms of TSS (10.07 ± 0.01 °Brix), TA (0.298 ± 0.01%), and TBC (1.92 × 10⁷ CFU/mL), with lower pH (3.98 ± 0.07) and aw (0.94 ± 0.02) compared to the control. Enhanced antioxidant activity (DPPH = 62.7 ± 0.86%) and TPC (19.92 ± 0.32 mg CE/100 mL) confirmed its superior bioactive potential. Sensory evaluation of ACWB also found it to be more preferred, with statistically significant difference in majority of the tested attributes. The apricot supplement enhanced the fermentation activity, microbial growth, as well as the antioxidant capacity of the end product, creating a stable, tangy, and nutritionally enriched non-dairy functional beverage that could be consumed by healthy and lactose intolerant consumers. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

8 pages, 1292 KB

Open AccessProceeding Paper

Effect of Pyrolytic Temperature on Biochar’s Physicochemical Properties Under Sodium Carbonate Catalyst Impregnation from Green Pea Peels

by Norbert Onen Rubangakene, Kingsley Safo and Hassan Shokry

Eng. Proc. 2025, 117(1), 49; https://doi.org/10.3390/engproc2025117049 - 13 Feb 2026

Viewed by 304

Abstract

This study examined the influence of temperature on the physicochemical and morphological properties of biochar from green pea peels. Biochar was produced by thermal degradation of the feedstock under Na₂CO₃ catalyst at temperatures of 300–800 °C. Characterization techniques revealed the [...] Read more.

This study examined the influence of temperature on the physicochemical and morphological properties of biochar from green pea peels. Biochar was produced by thermal degradation of the feedstock under Na₂CO₃ catalyst at temperatures of 300–800 °C. Characterization techniques revealed the biochar’s surface area increased from 0.6836 m²/g to a maximum of 683.2 m²/g, whereas the mean pore diameter decreased from 161.67 nm in GP300 to 2.1778 nm in GP800. Moreover, the high temperatures led to greater carbonization, aromatization, and stability. Additionally, the HHVs ranged from 16.56 to 23.6 MJ/kg compared with 15.50 MJ/kg in the feedstock, making the produced biochar materials highly suitable for multifaceted applications. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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6 pages, 519 KB

Open AccessProceeding Paper

Diagnosis of the Solid Biofuel Process from Agave Bagasse Through Arena Simulation

by Cristian Navarrete-Aguirre, J. Arturo Olguín-Rojas, Paulina Aguirre-Lara, Maurilio Tobón Gómez and José Miguel Téllez-Zepeda

Eng. Proc. 2025, 117(1), 50; https://doi.org/10.3390/engproc2025117050 - 13 Feb 2026

Viewed by 329

Abstract

In Mexico, mezcal production relies heavily on firewood, consuming up to 246 m³ per artisanal batch and producing approximately 2.4 t of bagasse for every 6 t of fermented Agave. This residue, with a calorific value of ≈19.4 MJ/kg, is a [...] Read more.

In Mexico, mezcal production relies heavily on firewood, consuming up to 246 m³ per artisanal batch and producing approximately 2.4 t of bagasse for every 6 t of fermented Agave. This residue, with a calorific value of ≈19.4 MJ/kg, is a promising alternative to solid biofuels. Using a discrete-event simulation in Arena™ (version 16.20.09), the substitution of firewood with processed bagasse briquettes was evaluated at a distillery in the region of Tecamachalco. The model included drying, grinding, briquetting, and distillation, analyzing yield, resource use, and bottlenecks. Sensitivity analyses identified solar drying as the main constraint. The results show a reduction of up to ~30% in firewood consumption, promoting the principles of the circular bioeconomy and sustainable rural energy transition. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 1163 KB

Open AccessProceeding Paper

A Fuzzy Logic-Based Temperature Prediction Model for Indirect Solar Dryers Using Mamdani Inference Under Natural Convection Conditions

by Sarvar Rejabov, Zafar Turakulov, Azizbek Kamolov, Alisher Jabborov, Dilfuza Ungboyeva and Adham Norkobilov

Eng. Proc. 2025, 117(1), 51; https://doi.org/10.3390/engproc2025117051 - 13 Feb 2026

Cited by 2 | Viewed by 481

Abstract

The drying process in indirect solar dryers is strongly influenced by rapidly changing ambient conditions, resulting in highly nonlinear and dynamic system behavior. Accurate modeling is therefore essential for performance evaluation, process optimization, and reliable prediction of the drying chamber temperature, which plays [...] Read more.

The drying process in indirect solar dryers is strongly influenced by rapidly changing ambient conditions, resulting in highly nonlinear and dynamic system behavior. Accurate modeling is therefore essential for performance evaluation, process optimization, and reliable prediction of the drying chamber temperature, which plays a key role in ensuring efficient moisture removal while preserving the nutritional and sensory quality of dried products. In this study, a fuzzy logic–based modeling approach using the Mamdani inference system is developed to predict the drying chamber temperature over a wide range of operating conditions. Experimental measurements were carried out with solar radiation varying from 400 to 950 W/m² and ambient temperature ranging from 20 to 50 °C, covering both static and dynamic system responses. The fuzzy model employs solar radiation and ambient temperature as input variables, represented by five and three triangular membership functions, respectively, while the drying chamber temperature is defined as the output variable using five triangular membership functions (T1–T5). The Mamdani inference system consists of 15 “if–then” rules, and centroid defuzzification is applied to obtain crisp output values. Model validation across the investigated operating range demonstrates a strong agreement between predicted and experimental temperatures. For example, at a solar radiation of 700 W/m² and an ambient temperature of 46 °C, the predicted chamber temperature is 50.9 °C compared to a measured value of 51.0 °C, while at 750 W/m² and 50 °C, the predicted temperature of 52.0 °C closely matches the experimental value of 51.8 °C. Statistical evaluation yields RMSE = 0.38 °C, MAE = 0.29 °C, and R² = 0.997, demonstrating effective temperature tracking capability within the tested operating range. These results show that the Mamdani fuzzy logic approach can effectively represent the thermal behavior of an indirect solar dryer within the tested operating range. The proposed model also provides a promising basis for the future development of real-time intelligent control strategies aimed at improving energy efficiency and product quality. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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23 pages, 2761 KB

Open AccessProceeding Paper

Optimizing Distribution System Using Prosumer-Centric Microgrids with Integrated Renewable Energy Sources and Hybrid Energy Storage System

by Djamel Selkim, Nour El Yakine Kouba and Amirouche Nait-Seghir

Eng. Proc. 2025, 117(1), 52; https://doi.org/10.3390/engproc2025117052 - 14 Feb 2026

Viewed by 583

Abstract

The increasing penetration of distributed renewable energy resources and the emergence of prosumers are reshaping the operational landscape of distribution grids. This work proposes a comprehensive prosumer-centric control and coordination framework integrated into the IEEE 33-bus radial distribution feeder. Selected buses are modeled [...] Read more.

The increasing penetration of distributed renewable energy resources and the emergence of prosumers are reshaping the operational landscape of distribution grids. This work proposes a comprehensive prosumer-centric control and coordination framework integrated into the IEEE 33-bus radial distribution feeder. Selected buses are modeled as aggregated prosumer nodes equipped with photovoltaic (PV) generation, wind turbines, oncentrated solar power (CSP), a hybrid energy storage system (HESS) including redox flow batteries (RFBs), superconducting magnetic energy storage (SMES), and fuel cells (FCs), as well as electric vehicle (EV) fleets. A hierarchical power management strategy is developed, combining a decentralized fuzzy logic controller for real-time dispatch with a Particle Swarm Optimization (PSO) layer that tunes membership functions and rule weights to enhance system stability and renewable utilization. Time-series simulations are conducted to evaluate the impact of prosumer integration on network performance. The results show a significant improvement in the voltage profile across all buses, particularly at downstream nodes, highlighting the effectiveness of distributed renewable injections and coordinated storage management. The proposed framework illustrates the potential of clustered prosumers to support voltage stability, improve grid operation and enable high-renewable penetration in distribution networks. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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12 pages, 1413 KB

Open AccessProceeding Paper

Development of a Temperature Regulation System for Solar Dryers Based on Artificial Neural Network-Driven Intelligent Control

by Sarvar Rejabov, Botir Shukurillaevich Usmonov, Komil Usmanov, Jaloliddin Eshbobaev and Mirjalol Yusupov

Eng. Proc. 2025, 117(1), 53; https://doi.org/10.3390/engproc2025117053 - 14 Feb 2026

Viewed by 482

Abstract

Solar drying is a sustainable and energy-efficient method for preserving agricultural products; however, its performance is strongly influenced by fluctuating environmental conditions. This study presents an artificial neural network (ANN)-based predictive temperature control system for an indirect forced-convection solar dryer. A data-driven dynamic [...] Read more.

Solar drying is a sustainable and energy-efficient method for preserving agricultural products; however, its performance is strongly influenced by fluctuating environmental conditions. This study presents an artificial neural network (ANN)-based predictive temperature control system for an indirect forced-convection solar dryer. A data-driven dynamic model of the drying process was developed using experimental measurements and implemented in MATLAB R2014a (MathWorks, Natick, MA, USA). The proposed ANN-based controller was evaluated against a conventional PID controller under identical operating conditions. The results show that the ANN-based approach reduced the settling time by approximately 36% (160 s compared to 250 s for PID) and maintained drying chamber temperature stability within ±1.2 °C. These improvements demonstrate the effectiveness of neural predictive control for enhancing dynamic response and temperature regulation accuracy in solar drying systems. The study is limited to a prototype-scale dryer and short-term experimental data; therefore, further validation under varying climatic conditions and larger-scale systems is required. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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6 pages, 584 KB

Open AccessProceeding Paper

Alkaline-Mediated Formation of Glucuronoxylomannan-Gold Nanoparticle Hybrids: Mechanism and Structural Transformation

by Sergii Kravchenko, Praskoviya Boltovets, Oleksiy Kovalenko and Borys Snopok

Eng. Proc. 2025, 117(1), 54; https://doi.org/10.3390/engproc2025117054 - 18 Feb 2026

Viewed by 280

Abstract

A hybrid material was synthesized via the formation of gold nanoparticles within a glucuronoxylomannan (GXM) polysaccharide matrix under alkaline conditions. The ionization of GXM carboxyl groups induced electrostatic repulsion, creating a flexible matrix structure. Furthermore, the alkaline environment facilitated GXM hydrolysis, leading to [...] Read more.

A hybrid material was synthesized via the formation of gold nanoparticles within a glucuronoxylomannan (GXM) polysaccharide matrix under alkaline conditions. The ionization of GXM carboxyl groups induced electrostatic repulsion, creating a flexible matrix structure. Furthermore, the alkaline environment facilitated GXM hydrolysis, leading to the gradual cleavage of polysaccharide chains into oligosaccharides and monosaccharides via base-catalyzed degradation. Such structural transformations within the matrix facilitate the growth of gold nanoparticles in various morphologies, including spherical, ellipsoidal, and planar shapes with tri-, tetra-, penta-, and hexagonal symmetries. The study highlights that the GXM matrix acts not only as a template but also as a dynamic component of the reaction. During formation, polysaccharides undergo hydrolysis in an alkaline environment, with the gradual cleavage of monosaccharide links occurring as part of the basic degradation process. This structural transformation is key to the stabilization of the resulting hybrid gold nanoparticles. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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7 pages, 1014 KB

Open AccessProceeding Paper

Extraction Kinetics and Composition of Chamomile Flower Extract Obtained by Supercritical CO₂

by Grimaldo Wilfredo Quispe Santivañez, Perfecto Chagua-Rodríguez, Walter Javier Cuadrado Campó, Julio Cesar Maceda Santivañez, Joselin Paucarchuco-Soto, Jamir Ever Vilchez De la Cruz, Maria Angela A. Meireles and Larry Oscar Chañi-Paucar

Eng. Proc. 2025, 117(1), 55; https://doi.org/10.3390/engproc2025117055 - 24 Feb 2026

Viewed by 474

Abstract

This study aimed to obtain chamomile flower extracts (CFEs) using supercritical CO₂ (200 bar and 40 °C) and analyze their composition by GC-MS. A yield of 2.8 ± 0.3% of CFE was obtained after 122.4 min of extraction. The CFE contained several [...] Read more.

This study aimed to obtain chamomile flower extracts (CFEs) using supercritical CO₂ (200 bar and 40 °C) and analyze their composition by GC-MS. A yield of 2.8 ± 0.3% of CFE was obtained after 122.4 min of extraction. The CFE contained several compounds, the most abundant of which were 4-(4-Hydroxy-2,2,6-trimethyl-7-oxabicyclo [4.1.0]hept-1-yl)butan-2-one (12.9%), (Z)-Tonghaosu (11.8%), 6-hydroxydihydrotheaspirane (11.5%), pentacosane (8.1%), cyclohexanethiol, 2,5-dimethylacetate (5.6%), and tetracontane (5.3%). The SFE process for obtaining CFE compounds is a suitable alternative; however, further studies are needed to evaluate this process and the composition of the extract, especially its most volatile fraction. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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6 pages, 679 KB

Open AccessProceeding Paper

Development of a Green Method for the Synthesis of Xanthene-1,8-dione Derivatives from Dimedone and Aldehydes

by Imene Maallem and Malika Berredjem

Eng. Proc. 2025, 117(1), 56; https://doi.org/10.3390/engproc2025117056 - 26 Feb 2026

Viewed by 373

Abstract

A green and efficient method was developed for the synthesis of 1,8-dioxo-octahydroxanthene derivatives using linear alkylbenzene sulfonic acid (LABSA) as an eco-friendly Brønsted acid catalyst under aqueous reflux conditions. This system combines micellar catalysis and acid activation to afford tricyclic products in high [...] Read more.

A green and efficient method was developed for the synthesis of 1,8-dioxo-octahydroxanthene derivatives using linear alkylbenzene sulfonic acid (LABSA) as an eco-friendly Brønsted acid catalyst under aqueous reflux conditions. This system combines micellar catalysis and acid activation to afford tricyclic products in high yields and with excellent purity. The transformation proceeds via a Knoevenagel–Michael sequence between dimedone and aromatic aldehydes, followed by intramolecular cyclization. The method exhibits broad substrate tolerance, affording yields between 80 and 92. The simplicity, scalability, and environmental compatibility of this process establish LABSA as a promising alternative to conventional acids for the green synthesis of pharmacologically relevant xanthene derivatives. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 872 KB

Open AccessProceeding Paper

Embedding Environmental Intelligence into Digital Twins for Resource-Aware Process Control in Computer Networks

by Alwin Joseph, Siva Shankar Ramasamy, Durgadevi Paramasivam and Vijayalakshmi Subramanian

Eng. Proc. 2025, 117(1), 57; https://doi.org/10.3390/engproc2025117057 - 27 Feb 2026

Viewed by 292

Abstract

Digital infrastructure consumes huge amounts of electricity directly and indirectly for global electricity consumption. Currently, some of the main consumers of electricity are data centers, high-speed networks, and devices that operate continuously to meet growing computing demands. In the current research, we propose [...] Read more.

Digital infrastructure consumes huge amounts of electricity directly and indirectly for global electricity consumption. Currently, some of the main consumers of electricity are data centers, high-speed networks, and devices that operate continuously to meet growing computing demands. In the current research, we propose a novel framework that integrates environmental intelligence into digital twins to enable resource-aware process control in digital infrastructure. In the proposed system, we monitored factors including power usage, temperature, and e-waste generation and created an energy profile of routers, switches, and computing nodes across time and usage conditions, generating real-time data to predict variations and impacts. A multi-objective optimization engine was integrated into the system to balance sustainability and performance objectives, with constraints on Service Level Agreement (SLA) adherence and hardware availability. The objective function optimized performance and energy consumption while maintaining network performance. We designed a proof-of-concept framework that acts like a cloud–edge network. The results showed that applying the modelling resulted in a 12.6% reduction in energy consumption and a 9.8% increase in performance under typical load scenarios. The system dynamically rerouted non-critical traffic during peak grid emissions, activated low-power modes during idle periods, and recommended infrastructure upgrades based on thermal hotspot forecasts and energy impact assessments. The proposed framework demonstrates how digital twins can align operational efficiency with sustainability by embedding intelligence into real-time control mechanisms. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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13 pages, 1516 KB

Open AccessProceeding Paper

Python-Powered Optimization of Sustainable 1,3-Butadiene Production from Ethanol: Bridging Thermodynamics, Kinetics, and Machine Learning

by Silmara Furtado da Silva and Amanda Lemette Teixeira Brandão

Eng. Proc. 2025, 117(1), 58; https://doi.org/10.3390/engproc2025117058 - 28 Feb 2026

Viewed by 461

Abstract

This work presents an integrated Python-based framework to optimize the ethanol-to-1,3-butadiene conversion over a K₂O:ZrO₂:ZnO/MgO–SiO₂ catalyst, a sustainable alternative in decarbonizing plastics and rubber manufacturing. Thermodynamic evaluations confirmed the feasibility of all elementary steps, while kinetic modeling identified [...] Read more.

This work presents an integrated Python-based framework to optimize the ethanol-to-1,3-butadiene conversion over a K₂O:ZrO₂:ZnO/MgO–SiO₂ catalyst, a sustainable alternative in decarbonizing plastics and rubber manufacturing. Thermodynamic evaluations confirmed the feasibility of all elementary steps, while kinetic modeling identified the butadiene-forming reaction as the most sensitive step. Experimental data were analyzed using multivariate surface-response methods, revealing an optimal operating window of 350–375 °C and 0.93–1.24 h⁻¹. A Random Forest model (R² = 0.91) ranked weight hourly space velocity (WHSV) and selectivity descriptors as the most dominant variables, providing a quantitative basis for data-driven process intensification. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 703 KB

Open AccessProceeding Paper

Enhancing Biogas Production Using Organic Waste as Clean Energy for Sustainable Development

by Francis Tetteh, Daniel Safo, Philip Yaro Laari, Mavis Berko and Leticia Osafo

Eng. Proc. 2025, 117(1), 59; https://doi.org/10.3390/engproc2025117059 - 2 Mar 2026

Viewed by 436

Abstract

This study evaluated the biogas generation potential of kitchen waste from five restaurants and traditional halls at Kwame Nkrumah University of Science and Technology (KNUST) through anaerobic digestion. A total of 6868 kg of peels from cassava, plantain, and yam were generated and [...] Read more.

This study evaluated the biogas generation potential of kitchen waste from five restaurants and traditional halls at Kwame Nkrumah University of Science and Technology (KNUST) through anaerobic digestion. A total of 6868 kg of peels from cassava, plantain, and yam were generated and processed during the study period, from which representative samples were collected and analyzed for total solids, volatile solids, and ash content. The waste showed high biodegradability, with methane yields between 0.45–0.52 m³ CH₄/kg, surpassing conventional livestock manure. These results demonstrate that institutional kitchen waste offers a sustainable substrate for decentralized biogas production, supporting circular economy initiatives and providing a low-cost solution for energy and waste management in West African campuses. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 878 KB

Open AccessProceeding Paper

Optimizing Tilt Angles: Enhancing PV Energy Output and Reducing Power Costs Across Diverse Climates

by Muhammad Tamoor

Eng. Proc. 2025, 117(1), 60; https://doi.org/10.3390/engproc2025117060 - 10 Mar 2026

Viewed by 387

Abstract

The tilt angle of photovoltaic (PV) modules strongly influences long-term energy yield, land-use efficiency, and the resulting cost of power generation, particularly under diverse climatic conditions. This study presents a systematic framework for optimizing the monthly tilt angle of PV modules with the [...] Read more.

The tilt angle of photovoltaic (PV) modules strongly influences long-term energy yield, land-use efficiency, and the resulting cost of power generation, particularly under diverse climatic conditions. This study presents a systematic framework for optimizing the monthly tilt angle of PV modules with the objective of minimizing power cost while maintaining high energy output. The proposed methodology integrates solar geometry, monthly global and diffuse irradiance data, shading-constrained array layout, land-use modeling, and economic evaluation to determine location-specific optimal tilt configurations. Unlike conventional fixed-tilt or energy-only optimization approaches, the proposed framework explicitly incorporates inter-row shading constraints and land-use efficiency into power-cost-based tilt optimization. The framework was applied to multiple geographically distinct locations across Pakistan, representing different climatic regions. The results show that power cost is highly sensitive to tilt angle and exhibits a clear minimum at moderate inclinations. For Lahore and Islamabad, the average annual power cost at a 0° tilt angle was 4.3475 $/kW and 4.4128 $/kW, respectively, decreasing to 3.3596 $/kW and 3.266 $/kW at a 40° tilt angle, before increasing to 7.6390 $/kW and 6.5197 $/kW at 90°. For RYK and Karachi, the cost declined from 3.309 $/kW and 2.8189 $/kW at 0° to 2.7138 $/kW and 2.4707 $/kW at a 30° tilt angle, before rising sharply at steeper inclinations. Overall, the study confirms that monthly or seasonally adjusted tilt angles provide a superior balance between energy generation and power cost compared with fixed-tilt systems, enabling location-specific and cost-effective PV system design for large-scale deployment in Pakistan. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 809 KB

Open AccessProceeding Paper

Evaluation of Copper Extraction on Low-Grade Oxide Ores Using Column Heap Leaching

by Itumeleng Christopher Kohitlhetse and Johanna Letsoalo

Eng. Proc. 2025, 117(1), 61; https://doi.org/10.3390/engproc2025117061 - 11 Mar 2026

Viewed by 415

Abstract

Heap leaching is an economically favourable hydrometallurgical technique extensively employed in the mining industry for extracting valuable metals such as copper from low-grade ore deposits. This method renders a cost-effective solution for processing ore that would otherwise be considered uneconomical for conventional extraction [...] Read more.

Heap leaching is an economically favourable hydrometallurgical technique extensively employed in the mining industry for extracting valuable metals such as copper from low-grade ore deposits. This method renders a cost-effective solution for processing ore that would otherwise be considered uneconomical for conventional extraction techniques. This study investigates the efficiency of copper recovery from different particle size fractions of low-grade oxide ores that have undergone a crushing stage. Hydrochloric acid was used as a lixiviant in column heap leaching experiments to study the effect of particle size on copper extraction recovery. The experiments were conducted using column leach setups with dimensions of 150 mm in diameter and 2 m in height. Crushed ore samples, ranging in particle size from 25 mm down to 1.8 mm, were divided into 5 kg aliquots and loaded into the columns, with a total mass of approximately 40 kg per test. Leaching was performed over a period of 16 days using an acid concentration of 200 g/L. The results demonstrated promising copper recoveries. One sample achieved a copper extraction rate of 75% within 16 days, with maximum acid consumption reaching 23 kg/ton over 15 days. Another sample yielded a comparable copper recovery of 74% under the same timeframe but required a higher acid consumption rate of 30 kg/ton. Moreover, the consistent linear increase in copper recovery throughout the leaching period suggests minimal interference from pregnant solution robbing impurities in the ore that consumes the lixiviant. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1373 KB

Open AccessProceeding Paper

Model Predictive Control of a Data-Driven Model of a Medium-Temperature Cold Storage System

by Adesola Temitope Bankole, Muhammed Bashir Mu’azu, Habeeb Bello-Salau and Zaharuddeen Haruna

Eng. Proc. 2025, 117(1), 62; https://doi.org/10.3390/engproc2025117062 - 12 Mar 2026

Viewed by 368

Abstract

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is [...] Read more.

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is needed to maintain food safety and quality. This study presents model predictive control of a data-driven medium-temperature cold storage system using subspace system identification techniques. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold, and the compressor rack. The data of the expansion valve, suction pressure, compressor capacity, heat transfer rate, and ambient temperature were taken as inputs, while the data of the air and goods temperatures were taken as outputs based on expert knowledge. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 h showed that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 °C and 5 °C that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable, and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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16 pages, 1173 KB

Open AccessProceeding Paper

Electrochemical Synthesis of Ortho- and Para-Hydroxybenzoic Acids Using CO₂: Experimental and Simulation-Based Optimization

by Bekzod Eshkulov and Ruzimurod Jurayev

Eng. Proc. 2025, 117(1), 63; https://doi.org/10.3390/engproc2025117063 - 13 Mar 2026

Viewed by 409

Abstract

The electrochemical conversion of CO₂ into value-added aromatic carboxylic acids represents an emerging route for carbon utilization. This work investigates the regioselective electrochemical synthesis of ortho- and para-hydroxybenzoic acids (o-HBA and p-HBA) from CO₂ using a stirred batch cell, supported by [...] Read more.

The electrochemical conversion of CO₂ into value-added aromatic carboxylic acids represents an emerging route for carbon utilization. This work investigates the regioselective electrochemical synthesis of ortho- and para-hydroxybenzoic acids (o-HBA and p-HBA) from CO₂ using a stirred batch cell, supported by a phenomenological Aspen Plus (version 12) model to assess process-level behavior. Experiments conducted at −1.2 V vs. Ag/AgCl, 3 atm CO₂, and 50 °C achieved yields of 58.4 ± 2.1% for o-HBA and 40.2 ± 1.6% for p-HBA, with a combined selectivity of 64.8%. Faradaic efficiencies were 76.2% (o-HBA) and 66.8% (p-HBA). A complete carbon balance, including dissolved inorganic carbon species, was established, demonstrating a single-pass CO₂ conversion of 42.6% and an overall conversion of 74.8% when the recycle loop was considered. Aspen Plus simulations based on ELECNRTL(Electrolyte Non-Random Two-Liquid model) thermodynamics and RYield fitting reproduced qualitative trends but underpredicted yields (21% and 9% for o- and p-HBA, respectively), reflecting the limitations of non-kinetic modeling. Sensitivity analyses confirmed that both electrolysis temperature and electrolyte concentration substantially influence yields and purity. This work provides reproducible electrochemical data, process-level mass balances, and a validated phenomenological simulation framework for future scale-up studies. Full article

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11 pages, 1166 KB

Open AccessProceeding Paper

Advances in MOF Fabrication Techniques: Tuning Material Properties for Specific Applications

by Deepanjali Bisht, Satya, Tahmeena Khan and Seema Joshi

Eng. Proc. 2025, 117(1), 64; https://doi.org/10.3390/engproc2025117064 - 13 Mar 2026

Viewed by 912

Abstract

Metal–organic frameworks (MOFs), a class of porous crystalline materials, consists of metal ions or clusters coordinated to organic linkers. The unique features of MOFs such as exceptionally high surface area, chemical versatility, and tunable porosity make them highly suitable for several applications, including [...] Read more.

Metal–organic frameworks (MOFs), a class of porous crystalline materials, consists of metal ions or clusters coordinated to organic linkers. The unique features of MOFs such as exceptionally high surface area, chemical versatility, and tunable porosity make them highly suitable for several applications, including gas storage, drug delivery, catalysis, and sensing. Various synthesis techniques, including solvothermal, hydrothermal, microwave-assisted, mechanochemical, electrochemical, and sonochemical methods, have been used for the fabrication of MOFs. The selection and optimization of synthesis technique significantly influence the fundamental framework structure, the existence of defects, the available active sites, and the effectiveness of MOFs in special applications. This study focuses on advances in MOF fabrication techniques and examines their role in tuning the key properties of MOFs for targeted applications. The insights of this work may guide researchers in selecting or designing appropriate fabrication strategies for application-specific development of MOFs. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 835 KB

Open AccessProceeding Paper

Chemical Equilibrium and Kinetics in the Self-Catalytic Reactions of 2,2,2-Trifluoroacetic Acid with Alcohols

by Egor V. Lupachev, Andrei V. Polkovnichenko, Anastasia N. Belova and Tatiana V. Chelyuskina

Eng. Proc. 2025, 117(1), 65; https://doi.org/10.3390/engproc2025117065 - 12 Mar 2026

Viewed by 351

Abstract

This study presents the first experimental data on the chemical equilibrium and kinetics of two self-catalytic reactions of trifluoroacetic acid (TFA) with 2,2,2-trifluoroethanol (TFE-ol) and TFA with propan-1-ol (P-ol), in which TFA acts simultaneously as a reactant and a catalyst. The results show [...] Read more.

This study presents the first experimental data on the chemical equilibrium and kinetics of two self-catalytic reactions of trifluoroacetic acid (TFA) with 2,2,2-trifluoroethanol (TFE-ol) and TFA with propan-1-ol (P-ol), in which TFA acts simultaneously as a reactant and a catalyst. The results show that at an initial molar ratio of TFA/P-ol = 1/9 in the temperature range from 30 to 80 °C and the reaction rate coefficient k₁ changes over the course of the reaction. Notably, for the TFA/TFE-ol system (at an initial reactant ratio of TFA/TFE-ol = 1/9 and 5/5 and a temperature of 50 °C), the reaction rate coefficient k₁ remains constant. A new model describing the kinetics of the reaction with a variable rate coefficient is proposed, which accurately fits the experimental data for the TFA/P-ol reaction. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 1583 KB

Open AccessProceeding Paper

Enhancement of Dynamic Microgrid Stability Under Climatic Changes Using Multiple Energy Storage Systems

by Amel Brik, Nour El Yakine Kouba and Ahmed Amine Ladjici

Eng. Proc. 2025, 117(1), 66; https://doi.org/10.3390/engproc2025117066 - 17 Mar 2026

Viewed by 291

Abstract

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems [...] Read more.

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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13 pages, 492 KB

Open AccessProceeding Paper

Modeling and Control of Nonlinear Fermentation Dynamics in Brewing Industry

by Mirjalol Yusupov, Jaloliddin Eshbobaev, Zafar Turakulov, Komil Usmanov, Dilafruz Kadirova and Azizbek Yusupbekov

Eng. Proc. 2025, 117(1), 67; https://doi.org/10.3390/engproc2025117067 - 17 Mar 2026

Viewed by 451

Abstract

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The [...] Read more.

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state-space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 h under optimized input profiles. The combined FLC–MPC scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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16 pages, 12583 KB

Open AccessProceeding Paper

Measuring Air Pollution in Populated Areas Using Sensors Installed on Vehicles and Drones

by András Molnár, Saidumarkhon Saidakhmadov, Azizbek Kamolov and Botir Usmonov

Eng. Proc. 2025, 117(1), 68; https://doi.org/10.3390/engproc2025117068 - 16 Mar 2026

Viewed by 407

Abstract

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal [...] Read more.

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 929 KB

Open AccessProceeding Paper

Development and Performance Evaluation of a Modified Separator for Enhanced Natural Gas Decontamination

by Akhror Uzokov, Rakhmatulla Muradov, Abdulaziz Bakhtiyorov, Tolib Turayev and Adham Norkobilov

Eng. Proc. 2025, 117(1), 69; https://doi.org/10.3390/engproc2025117069 - 19 Mar 2026

Viewed by 340

Abstract

Natural gas streams extracted from production wells often contain undesirable components such as water vapor, gas condensate, and solid particulates. These impurities reduce fuel quality and damage downstream equipment through corrosion, fouling, and foaming. This study presents the development and field-scale evaluation of [...] Read more.

Natural gas streams extracted from production wells often contain undesirable components such as water vapor, gas condensate, and solid particulates. These impurities reduce fuel quality and damage downstream equipment through corrosion, fouling, and foaming. This study presents the development and field-scale evaluation of a high-performance gas–liquid separator designed for the deep decontamination of natural gas. The proposed separator incorporates 30 suspended baffles arranged in three rows and an anti-foaming mesh to enhance phase separation and prevent liquid re-entrainment. Field experiments were conducted at the Somontepa gas field in Uzbekistan. Compared to the baseline industrial unit, the upgraded separator reduced gas condensate from 16.58 g/m³ to 0.725 g/m³, water from 4.84 g/m³ to 0.10 g/m³, and solid impurities from 1.20 g/m³ to 0.0058 g/m³. The foam height was lowered from 96.4 mm to 10.2 mm, and the average bubble diameter was reduced by over 60%. The design maintained low pressure drops and demonstrated stable operation under varying flow rates. Fractional analysis confirmed the quality of a recovered condensate suitable for downstream utilization. The proposed configuration offers substantial improvements in gas purification performance and economic efficiency. These results support the application of this separator design for high-contaminant natural gas streams in industrial gas processing facilities. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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8 pages, 1187 KB

Open AccessProceeding Paper

Dynamic Modelling of a Metal Hydride Reactor During Discharge Through Artificial Neural Network Regression

by Douw Faurie, Mikateko Manganyi, Kasturie Premlall, Andrei Kolesnikov and Mykhaylo Lototskyy

Eng. Proc. 2025, 117(1), 70; https://doi.org/10.3390/engproc2025117070 - 20 Mar 2026

Viewed by 288

Abstract

With hydrogen as a clean but hazardous energy carrier, solid-state hydrogen storage in the form of a metal hydride has come forth as a safe and low-pressure storage solution with competitive volumetric energy density. This paper reports the modelling of a metal hydride [...] Read more.

With hydrogen as a clean but hazardous energy carrier, solid-state hydrogen storage in the form of a metal hydride has come forth as a safe and low-pressure storage solution with competitive volumetric energy density. This paper reports the modelling of a metal hydride reactor during its discharge state using neural network regression. This was done by generating a validated finite element model of the reactor, which was then used to generate dynamic operational data based on the desired pressure outlet and heating fluid temperature as independent variables. The best-performing neural network model validation using the experimentally observed data achieved a regression coefficient of 0.99 and a mean squared error of less than 10⁻⁴. This predictive model, with further refinement, can be implemented to allow for predictive control, which has always been a challenge through conventional means due to the batch nature of the system. Moreover, the hydrogen concentration as stored in a solid-state measurement would be too expensive for industrial applications. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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5 pages, 972 KB

Open AccessProceeding Paper

The Use of Lanthanum-Based Intermetallic Compounds as a Catalyst in the Electrochemical Process of Ammonia Synthesis

by Sergey Nesterenko, Ilja Chernyshev, Irina Kuznetsova, Dmitry Kultin, Olga Lebedeva and Leonid Kustov

Eng. Proc. 2025, 117(1), 71; https://doi.org/10.3390/engproc2025117071 - 23 Mar 2026

Viewed by 232

Abstract

Functional materials based on LaCoSi and LaCuSi intermetallic compounds (IMC) were fabricated and tested in the electrocatalytic process of reducing nitrates to ammonia (NO₃RR). The method of arc melting in an argon atmosphere was used to synthesize the alloys. The synthesis [...] Read more.

Functional materials based on LaCoSi and LaCuSi intermetallic compounds (IMC) were fabricated and tested in the electrocatalytic process of reducing nitrates to ammonia (NO₃RR). The method of arc melting in an argon atmosphere was used to synthesize the alloys. The synthesis process is described and analyzed in detail, and the difficulties and advantages are shown. It has been established that when using an LaCuSi-based IMC–alloy as an electrocatalyst, the reduction of nitrates is the predominant reaction. On the contrary, for the LaCoSi (IMC)–alloy electrocatalyst, NO₃RR and the hydrogen evolution reaction (HER) occur simultaneously. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 1582 KB

Open AccessProceeding Paper

Study on the Suppression of Hydrogen Generation by Using Heated Simulated Incineration Ash and Water in an Ash Conveyor Environment

by Hideyuki Onodera, Ryoji Imai and Masahiro Sakai

Eng. Proc. 2025, 117(1), 72; https://doi.org/10.3390/engproc2025117072 - 7 Apr 2026

Viewed by 285

Abstract

Several hydrogen explosions have been reported in ash treatment facilities at municipal solid waste incineration plants, highlighting the need for effective suppression measures. This study focuses on controlling temperature changes during the conveyor transportation process as a method to reduce hydrogen generation from [...] Read more.

Several hydrogen explosions have been reported in ash treatment facilities at municipal solid waste incineration plants, highlighting the need for effective suppression measures. This study focuses on controlling temperature changes during the conveyor transportation process as a method to reduce hydrogen generation from incineration ash. Experiments were conducted in which heated simulated incineration ash was immersed in water inside a vessel and allowed to cool naturally. The results show that higher water temperatures during immersion significantly decreased the amount of hydrogen gas generated. Specifically, it was found that hydrogen generation ceased when the simulated incineration ash was immersed in water at 90 °C and subsequently allowed to cool naturally. These findings suggest that temperature control may contribute to preventing hydrogen explosion hazards in ash handling systems. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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9 pages, 669 KB

Open AccessProceeding Paper

Predictive Modeling of Biodegradable Material Degradation Using Deep Learning with An Improved Regulatory and Liability-Aware Approach

by Sanjay Bang, Keya Purohit and Sreedhar Samarth

Eng. Proc. 2025, 117(1), 73; https://doi.org/10.3390/engproc2025117073 - 13 Apr 2026

Viewed by 325

Abstract

There has been a significant rise in the adoption of biodegradable materials across industries. Industries including packaging, healthcare, and consumer goods need an accurate prediction of their degradation behavior to support environmental sustainability and ensure regulatory compliance. Here, we propose a deep learning-based [...] Read more.

There has been a significant rise in the adoption of biodegradable materials across industries. Industries including packaging, healthcare, and consumer goods need an accurate prediction of their degradation behavior to support environmental sustainability and ensure regulatory compliance. Here, we propose a deep learning-based framework that helps predict the decomposition rates and the associated environmental impact of biodegradable materials under diverse physicochemical conditions. We train the neural network on historical data of material performance, environmental exposure, and microbiological interaction, and the model shows its generalizability for life cycle estimation. The model has also been associated with an attention layer that monitors compliance with regulatory frameworks that govern material safety, quality, and consumer transparency. Standards and frameworks, including (ISO and ISI standards), are integrated into this layer to ensure adherence to product liability guidelines. Domain-specific regulations have been used to finetune the predictive outputs, keeping the permissible limits on product labeling, shelf life, and environmental claims. The approach shows better predictive results and demonstrates compliance with the legal context, and the model evaluation improves alignment with compliance thresholds and verification of the predictive results on the material life. Here, we explore a multidisciplinary approach that makes use of the learning abilities of ML algorithms and aligns the model performance with ethical frameworks to ensure trust and monitoring, creating intelligent systems for sustainable material design to improve sustainability and reusability, following regulations. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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11 pages, 2596 KB

Open AccessProceeding Paper

Study on the Effect of Reactor Scale on Hydrogen Generation from Aluminum Alloy Powder and Water via Stirring

by Ryoji Imai, Rina Nishiyama, Yusuke Yamamoto and Masahiro Sakai

Eng. Proc. 2025, 117(1), 74; https://doi.org/10.3390/engproc2025117074 - 16 Apr 2026

Viewed by 309

Abstract

Hydrogen is a promising CO₂-free energy carrier, but conventional production from fossil fuels generates CO₂. This study explores an alternative—reacting waste-derived aluminum alloy powder with water under stirring to generate hydrogen, combining recycling with carbon-neutral hydrogen production. In a [...] Read more.

Hydrogen is a promising CO₂-free energy carrier, but conventional production from fossil fuels generates CO₂. This study explores an alternative—reacting waste-derived aluminum alloy powder with water under stirring to generate hydrogen, combining recycling with carbon-neutral hydrogen production. In a 500 mL stirred reactor, the hydrogen yield using alloy powders was six times greater than in a 100 mL reactor, exceeding simple volume-based scaling. Excess hydrogen production is attributed to impeller-driven particle fragmentation. To quantify this, correlations between alloy mass–power input and hydrogen yield were examined, but no clear relationship was found. Future work will systematically analyze the effects of the reactor scale, stirring speed, and alloy mass on hydrogen generation. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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14 pages, 1747 KB

Open AccessProceeding Paper

Lipid Extraction from Microalgae Using Ionic Liquid as Solvent and Biodiesel Production

by Athulia Palamadathil and Beula Chandrika

Eng. Proc. 2025, 117(1), 75; https://doi.org/10.3390/engproc2025117075 - 18 May 2026

Viewed by 129

Abstract

This work focuses on the production of biodiesel from Nannochloropsis sp. microalgae. The study compares the effectiveness of conventional methods, such as Soxhlet extraction and the Bligh and Dyer method with a non-conventional method that uses an ionic liquid as solvent for lipid [...] Read more.

This work focuses on the production of biodiesel from Nannochloropsis sp. microalgae. The study compares the effectiveness of conventional methods, such as Soxhlet extraction and the Bligh and Dyer method with a non-conventional method that uses an ionic liquid as solvent for lipid extraction. The Bligh and Dyer method demonstrated superior efficiency with a lipid yield of 8.19% compared to Soxhlet extraction. Sample parameters were optimized in a maximum lipid yield of 18.29% at a sample volume of 91.9 mL, a duration of 7 min, and a power rate of 0.62 W. We further investigated the use of ionic liquids for lipid extraction from microalgae. We synthesized ionic liquids, and 1-(4 sulphonic acid) butyl-3-Methyl imidazolium hydrogen sulphate and 1-(4 sulphonic acid) butyl pyridinium hydrogen sulphate were characterized using FTIR, NMR and TGA. This method showed lipid extraction efficiency values of 10.7% and 0.402%. Subsequent transesterification of algal oil using 1-(4 sulphonic acid) butyl-3-Methyl imidazolium hydrogen sulphate yielded 19.82% biodiesel. FTIR analysis confirmed the presence of lipids and esters in the biodiesel, and the produced biodiesel met ASTM specifications, indicating its suitability for use as biofuel. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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10 pages, 2081 KB

Open AccessProceeding Paper

Advanced Oxidation Process for Decontamination of Tetracycline from Wastewater Using Immobilized Magnetite

by Padinchare Veettil Gayathri, Vincy Antoney, Divya Nair, Telsa Tenson, Sreelakshmi Manalody Goguldas, Sneha Vadassery and Thara Surendran

Eng. Proc. 2025, 117(1), 76; https://doi.org/10.3390/engproc2025117076 - 20 Apr 2026

Viewed by 48

Abstract

This study examines the solar-driven photocatalytic degradation of tetracycline hydrochloride in aqueous media using immobilized magnetite. The synthesized catalyst was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR). Photocatalytic experiments revealed that tetracycline degradation followed pseudo-first-order [...] Read more.

This study examines the solar-driven photocatalytic degradation of tetracycline hydrochloride in aqueous media using immobilized magnetite. The synthesized catalyst was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR). Photocatalytic experiments revealed that tetracycline degradation followed pseudo-first-order kinetics, achieving 48% removal within 30 min and nearly complete degradation (99%) after 120 min of solar irradiation. The addition of Fenton reagent significantly enhanced degradation efficiency by promoting the generation of reactive oxygen species. The formation of multiple degradation intermediates, along with a substantial reduction in chemical oxygen demand (COD) was observed. The immobilized magnetite catalyst exhibited good stability and reusability with minimal loss of activity over repeated cycles. Overall, the results demonstrate that magnetite-based solar photocatalysis is a cost-effective, efficient, and environmentally sustainable approach for treating TCT-contaminated wastewater. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)

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