Journal Description
ChemEngineering
ChemEngineering
is an international, peer-reviewed, open access journal on the science and technology of chemical engineering, published bimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Chemical) / CiteScore - Q2 (General Engineering )
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 32.8 days after submission; acceptance to publication is undertaken in 6.1 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.8 (2023);
5-Year Impact Factor:
2.6 (2023)
Latest Articles
Robust Enhanced Auto-Tuning of PID Controllers for Optimal Quality Control of Cement Raw Mix via Neural Networks
ChemEngineering 2025, 9(3), 52; https://doi.org/10.3390/chemengineering9030052 - 20 May 2025
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Ensuring efficient long-term quality control of the raw mix remains a priority for the cement industry, supporting initiatives to lower the CO2 footprint by incorporating significant amounts of alternative fuels and raw materials in clinker production. This study presents an effective method
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Ensuring efficient long-term quality control of the raw mix remains a priority for the cement industry, supporting initiatives to lower the CO2 footprint by incorporating significant amounts of alternative fuels and raw materials in clinker production. This study presents an effective method for creating a robust auto-tuner for proportional–integral–differential (PID) controller control of the lime saturation factor (LSF) of the raw mix using artificial neural networks (ANNs). This auto-tuner, combined with a previously studied robust PID controller, forms an integrated system that adapts to process changes and maintains low long-term variance in LSF. The ANN links each of the three PID gains to the process dynamic parameters, with the three ANNs also interconnected. We employed the Levenberg–Marquardt method to optimize the ANNs’ synaptic weights and applied the weight decay method to prevent overfitting. The industrial implementation of our control system, using the auto-tuner for 16,800 h of raw mill operation, shows an average LSF standard deviation of 2.5, with fewer than 10% of the datasets exceeding a standard deviation of 3.5. Considering that the measurement reproducibility is 1.44 and assuming a low mixing ratio of the raw meal in the silo equal to 2, the LSF standard deviation in the kiln feed approaches the analysis reproducibility, indicating that disturbances in the raw meal largely diminish in the kiln feed. In conclusion, integrating traditional, well-established tools like PID controllers with newer advanced techniques, such as ANNs, can yield innovative solutions.
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Open AccessArticle
Waste-to-Energy Potential of Petroleum Refinery Sludge, Statistical Optimization, Machine Learning, and Life Cycle Costs Models
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Seyyed Roohollah Masoomi, Mohammad Gheibi, Reza Moezzi, Kourosh Behzadian, Atiyeh Ardakanian, Farzad Piadeh and Andres Annuk
ChemEngineering 2025, 9(3), 51; https://doi.org/10.3390/chemengineering9030051 - 16 May 2025
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Sludge management in petroleum refineries is a costly and complex challenge, posing environmental risks and health hazards for humans. This study explores sludge incineration as a viable energy recovery method, using a case study from an Iranian refinery. Analysis of 15 sludge samples
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Sludge management in petroleum refineries is a costly and complex challenge, posing environmental risks and health hazards for humans. This study explores sludge incineration as a viable energy recovery method, using a case study from an Iranian refinery. Analysis of 15 sludge samples via bomb calorimetry revealed an average heat value of 3100 kcal/kg, which declines with increased moisture content, while higher chemical oxygen demand (COD) enhances energy yield. Over five years, 4000 tonnes of accumulated sludge presented an energy potential of 12,400 Gcal. Statistical modeling, including polynomial regression and response surface methodology (RSM), mapped sludge storage profiles and predicted calorific values based on COD and moisture variations. The results indicate anaerobic digestion at greater depths reduces organic matter, lowering energy potential. Differential scanning calorimetry (DSC) analysis confirmed key thermal transitions, supporting sludge incineration as an effective waste-to-energy strategy. Implementing this approach within a circular economy framework can optimize refinery waste management while reducing pollution, though proper combustion byproduct control is essential for sustainability and regulatory compliance.
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(This article belongs to the Special Issue Innovative Approaches for the Environmental Chemical Engineering)
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Decoloration of Waste Cooking Oil by Maghnia Algerian Clays via Ion Exchange and Surface Adsorption
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Abdelhak Serouri, Zoubida Taleb, Alberto Mannu, Chahineze Nawel Kedir, Cherifa Hakima Memou, Sebastiano Garroni, Andrea Mele, Oussama Zinai and Safia Taleb
ChemEngineering 2025, 9(3), 50; https://doi.org/10.3390/chemengineering9030050 - 16 May 2025
Abstract
The purification of waste cooking oils (WCOs) through clay-based adsorption is an established recycling method, yet the relationship between clay composition and adsorption efficiency remains an area of active research. The aim of the present research work was to assess the performance of
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The purification of waste cooking oils (WCOs) through clay-based adsorption is an established recycling method, yet the relationship between clay composition and adsorption efficiency remains an area of active research. The aim of the present research work was to assess the performance of Maghnia bentonite in WCO decoloration and to gain information about the specific refining process. Thus, natural bentonite from the Maghnia region (Algeria) was investigated as an adsorbent for WCO refining for biolubricant production. The adsorption efficiency was evaluated under different conditions, achieving up to 70% decolorization at 10 wt% clay after 4 h of treatment. Structural characterization of the bentonite before and after adsorption was conducted using FT-IR spectroscopy, powder X-ray diffraction (XRD), and X-ray fluorescence (XRF) to assess compositional and morphological changes. FT-IR analysis confirmed the adsorption of organic compounds, XRD indicated minor alterations in interlayer spacing, and XRF revealed ion exchange mechanisms, including a reduction in sodium and magnesium and an increase in calcium and potassium. Adsorption kinetics followed a pseudo-second-order model, with desorption effects observed at prolonged contact times. The pHPZC of 8.3 suggested that bentonite adsorption efficiency is enhanced under acidic conditions. The high decoloration capacity of Maghnia bentonite, combined with the availability and the low cost of the material, suggests a possible industrial application of this material for WCO refinement, especially in lubricant production.
Full article
Open AccessArticle
CFD Simulation and Design of Non-Newtonian Fluid Polymer Grinding Pump Under Turbulent Flow
by
Hong Du, Chenxi Wang, Jian Zhang, Xianjie Li, Xiujun Wang, Xuecheng Zheng and Xin He
ChemEngineering 2025, 9(3), 49; https://doi.org/10.3390/chemengineering9030049 - 8 May 2025
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The performance of the grinding pump, a device for crushing and stretching conventional polymers, is mainly affected by its stage number, diameter, and tooth count. In this paper, Fluent software was utilized, employing the Eulerian model in conjunction with non-Newtonian fluid models (such
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The performance of the grinding pump, a device for crushing and stretching conventional polymers, is mainly affected by its stage number, diameter, and tooth count. In this paper, Fluent software was utilized, employing the Eulerian model in conjunction with non-Newtonian fluid models (such as the power-law model and Bingham plastic model) and turbulence models (like the k-ε model) to establish a model for CFD (Computational Fluid Dynamics) simulations. These simulations analyzed the turbulence characteristics of non-Newtonian fluids in grinding mixing pumps, as well as the basic performance of the pumps, including pressure, velocity, viscosity, and volume fraction distributions. The effects of different structural parameters (stage number, pump diameter, and tooth count) on the instant dissolving effect of polymers were compared, and the optimal structure was determined. Based on pressure profile, velocity profile analysis, and polymer distribution simulation results, the optimal grinding mixing pump was found to have three stages, with a diameter of d = 140 mm and 60 teeth yielding the best grinding effect. Increasing the stage number and pump diameter can improve the grinding and mixing effect, but an excessively large pump diameter can reduce it. Changes in tooth count have a minor impact on viscosity but affect distribution uniformity.
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Influence of Substrate Concentrations on the Performance of Fed-Batch and Perfusion Bioreactors: Insights from Mathematical Modelling
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John J. Fitzpatrick, Fionn O'Leary, Ali Hill, James Daly, Fergal Lalor and Edmond P. Byrne
ChemEngineering 2025, 9(3), 48; https://doi.org/10.3390/chemengineering9030048 - 6 May 2025
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Fed-batch and perfusion bioreactors are commonly used in biopharmaceutical production. This study applies mathematical models to investigate the influence of substrate concentration in the media added ( ), operating substrate concentration in the bioreactor ( ), and bioreaction time on
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Fed-batch and perfusion bioreactors are commonly used in biopharmaceutical production. This study applies mathematical models to investigate the influence of substrate concentration in the media added ( ), operating substrate concentration in the bioreactor ( ), and bioreaction time on the performance of both bioreactors. The performance parameters are titer, productivity, product yield, wasted substrate, and mean product residence time. The difference between the substrate concentration in the media and the operating substrate concentration has a major impact on performance parameters. For a fixed , operating at higher values of is more beneficial to both fed-batch and perfusion performance. Higher productivities are obtained in perfusion, and mean product residence times are shorter. Furthermore, perfusion can obtain titers comparable to fed-batch when operated at similar substrate concentrations. All this suggests that perfusion is more advantageous. It is advantageous to operate the bioreactors over a longer bioreaction time. However, for fed-batch bioreactors, there exists an optimal time after which there is a major progressive reduction in productivity.
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Open AccessArticle
Steady-State Simulation of a Fixed-Bed Reactor for the Total Oxidation of Volatile Organic Components: Application of the Barkelew Criterion
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Philippe M. Heynderickx and Joris W. Thybaut
ChemEngineering 2025, 9(3), 46; https://doi.org/10.3390/chemengineering9030046 - 30 Apr 2025
Abstract
A steady-state tubular reactor for total oxidation reaction under typical industrial conditions in the removal of volatile organic components (VOC) is described using a one-dimensional heterogeneous reactor model with intraparticle diffusion, using a fully developed Langmuir–Hinshelwood reaction rate expression. The effectiveness factor, accounting
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A steady-state tubular reactor for total oxidation reaction under typical industrial conditions in the removal of volatile organic components (VOC) is described using a one-dimensional heterogeneous reactor model with intraparticle diffusion, using a fully developed Langmuir–Hinshelwood reaction rate expression. The effectiveness factor, accounting for these intraparticle diffusion limitations, is calculated with a generalized Thiele modulus. The actual inclusion of this factor shows that higher operational reactor temperatures can be possible, since this diffusion limitation restricts the heat production inside the catalyst particle. Special attention is given to the outlet concentration of propane, taken as the model VOC, and runaway criteria, reported in the literature, are evaluated. Furthermore, the well-known Barkelew criterion (to evaluate runaway for exothermic reactions) is implemented for practical and safe reactor design. This work identifies that the critical couples populating the Barkelew diagram are positioned lower (up to a 50% difference, compared to Barkelew’s original report), so that operation of the reactor under higher hydrocarbon molar inlet fractions is possible while maintaining safe performance.
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(This article belongs to the Special Issue Advances in Catalytic Kinetics)
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Microwave-Mediated Extraction of Critical Metals from LED E-Waste
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Athanasios B. Bourlinos, Christina Papachristodoulou, Anastasios Markou, Nikolaos Chalmpes, Emmanuel P. Giannelis, Dimitrios P. Gournis, Constantinos E. Salmas and Michael A. Karakassides
ChemEngineering 2025, 9(3), 47; https://doi.org/10.3390/chemengineering9030047 - 29 Apr 2025
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This study introduces a microwave-assisted technique for extracting critical minerals from LED electronic waste. The process begins with microwave irradiation, which thermally decomposes the LED’s plastic lens into a brittle, charred residue. During this stage, the LED chip undergoes deflagration—being rapidly ejected from
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This study introduces a microwave-assisted technique for extracting critical minerals from LED electronic waste. The process begins with microwave irradiation, which thermally decomposes the LED’s plastic lens into a brittle, charred residue. During this stage, the LED chip undergoes deflagration—being rapidly ejected from the reflective cavity and becoming embedded within the decomposed lens material. Consequently, the chip is encapsulated in the resulting charred residue. This composite, consisting of the charred lens and the LED chip, can be easily separated from the metallic pins (Fe, Ni, Ag), which remain almost undamaged. Subsequent calcination of the charred material in air exposes the materials making up the LED chip, which contain critical metals (e.g., Ga, As, In, Y, Au). These metals are then extracted through a two-step acid leaching process involving aqua regia followed by hot concentrated hydrochloric acid, yielding them in potentially recoverable forms. The synergistic effect of microwave irradiation and acid treatment achieves an average extraction efficiency of 96% for critical metals. Notably, this approach enables complete and loss-free recovery of the LED chip, offering a practical and efficient solution for LED e-waste recycling.
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Open AccessArticle
Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach
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Gabriel Marín Díaz
ChemEngineering 2025, 9(3), 45; https://doi.org/10.3390/chemengineering9030045 - 28 Apr 2025
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Ensuring high levels of quality and efficiency is essential for compliance with ISO standards in chemical manufacturing. Traditional methods, such as Statistical Process Control (SPC) and Six Sigma, often lack adaptability and fail to offer interpretable insights. This study proposes a hybrid quality
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Ensuring high levels of quality and efficiency is essential for compliance with ISO standards in chemical manufacturing. Traditional methods, such as Statistical Process Control (SPC) and Six Sigma, often lack adaptability and fail to offer interpretable insights. This study proposes a hybrid quality control model based on Explainable Artificial Intelligence (XAI), integrating fuzzy C-means clustering (FCM), machine learning (ML), and Fuzzy Inference Systems (FISs) to enhance defect prediction and interpretability in industrial environments. The approach uses fuzzy clusters to segment production batches, improving the understanding of process variability. A supervised ML model (XGBoost) is trained on historical data to predict defect probabilities, while an explainable FIS refines the final assessment using expert-defined rules. XAI techniques (SHAP and LIME) offer transparency and insight into the decision-making process. Experimental validation using a real-world white wine dataset, evaluated in terms of accuracy and interpretability, shows that the proposed model outperforms traditional approaches in both predictive performance and transparency. The results demonstrate the effectiveness of combining unsupervised clustering, predictive analytics, and fuzzy reasoning in an Industry 4.0 framework. This study provides a scalable and adaptable solution for real-time quality control in chemical manufacturing, improving decision support systems and enabling automated and explainable quality assessments.
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(This article belongs to the Special Issue New Advances in Chemical Engineering)
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Open AccessArticle
Lagrangian for Real Systems Instead of Entropy for Ideal Isolated Systems
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Nikolai M. Kocherginsky
ChemEngineering 2025, 9(3), 44; https://doi.org/10.3390/chemengineering9030044 - 24 Apr 2025
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The Second Law of Thermodynamics states that entropy S increases in a spontaneous process in an ideal isothermal and isolated system. Real systems are influenced by external forces and fields, including the temperature field. In this case, only entropy is not enough, and
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The Second Law of Thermodynamics states that entropy S increases in a spontaneous process in an ideal isothermal and isolated system. Real systems are influenced by external forces and fields, including the temperature field. In this case, only entropy is not enough, and we suggest using a new function, , which is analogous to the Lagrangian in classical mechanics. It includes total potential energy but instead of mechanical kinetic energy, includes the product ST, and the system always evolves towards increasing this modified Lagrangian. It reaches an equilibrium when total potential force is balanced by both entropic and thermal forces. All forces have the same units, Newton/mol, and may be added or subtracted. For condensed systems with friction forces, it is a molecular transport velocity, and not acceleration, which is proportional to the acting force. Our approach has several advantages compared to Onsager’s non-equilibrium thermodynamics with its thermodynamic forces, which may have different units, including 1/T for energy transport. For isolated systems, the description is reduced to Second Law and Clausius inequality. It easily explains diffusion, Dufour effect, and Soret thermodiffusion. The combination of electric, thermal, and entropic forces explains thermoelectric phenomena, including Peltier–Seebeck and Thomson (Lord Kelvin) effects. Gravitational and entropic forces together inside a black hole may lead to a steady state or the black hole evaporation. They are also involved in and influenced by solar atmospheric processes.
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Open AccessArticle
CrS2 Supported Transition Metal Single Atoms as Efficient Bifunctional Electrocatalysts: A Density Functional Theory Study
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Ying Wang
ChemEngineering 2025, 9(3), 43; https://doi.org/10.3390/chemengineering9030043 - 23 Apr 2025
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Transition metal dichalcogenides (TMDs) are recognized for their exceptional energy storage capabilities and electrochemical potential, stemming from their unique electronic structures and physicochemical properties. In this study, we focus on chromium disulfide (CrS2) as the primary research subject and employ a
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Transition metal dichalcogenides (TMDs) are recognized for their exceptional energy storage capabilities and electrochemical potential, stemming from their unique electronic structures and physicochemical properties. In this study, we focus on chromium disulfide (CrS2) as the primary research subject and employ a combination of density functional theory (DFT) and first-principle calculations to investigate the effects of incorporating transition metal elements onto the surface of CrS2. This approach aims to develop a class of bifunctional single-atom catalysts with high efficiency and to analyze their catalytic performance in detail. Theoretical calculations reveal that the Au@CrS2 single-atom catalyst demonstrates outstanding catalytic activity, with a low overpotential of 0.34 V for the oxygen evolution reaction (OER) and 0.37 V for the oxygen reduction reaction (ORR). These results establish Au@CrS2 as a highly effective bifunctional catalyst. Moreover, the catalytic performance of Au@CrS2 surpasses that of traditional commercial catalysts, such as Pt (0.45 V) and IrO2 (0.56 V), suggesting its potential to replace these materials in fuel cells and other energy applications. This study provides a novel approach to the design and development of advanced transition metal-based catalytic materials.
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Open AccessArticle
Silver-Based Catalysts on Metal Oxides for Diesel Particulate Matter Oxidation: Insights from In Situ DRIFTS
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Punya Promhuad, Boonlue Sawatmongkhon, Thawatchai Wongchang, Ekarong Sukjit, Nathinee Theinnoi and Kampanart Theinnoi
ChemEngineering 2025, 9(3), 42; https://doi.org/10.3390/chemengineering9030042 - 22 Apr 2025
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Diesel particulate matter (DPM) represents a deleterious environmental contaminant that necessitates the development of effective catalytic oxidation methodologies. This research delineates a comparative analysis of silver-supported metal oxide catalysts (Ag/Al2O3, Ag/TiO2, Ag/ZnO, and Ag/CeO2), with
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Diesel particulate matter (DPM) represents a deleterious environmental contaminant that necessitates the development of effective catalytic oxidation methodologies. This research delineates a comparative analysis of silver-supported metal oxide catalysts (Ag/Al2O3, Ag/TiO2, Ag/ZnO, and Ag/CeO2), with an emphasis on the effects of silver distribution and the metal-support interaction on the oxidation of DPM. An array of characterization techniques including XRD, HRTEM, XPS, H2-TPR, TEM, GC-MS, TGA, and in situ DRIFTS was employed. The novelty of this study resides in elucidating the oxidation mechanism through a tripartite pathway and recognizing Ag0 as the predominant active species involved in soot oxidation. The Ag/Al2O3 catalyst demonstrated superior catalytic performance, achieving a reduction in the ignition temperature by more than 50 °C, attributable to the optimal dispersion of Ag nanoparticles and a balanced metal-support interaction. Conversely, an excessive interaction observed in Ag/ZnO resulted in diminished catalytic activity. The oxidation of DPM transpires through the volatilization of VOCs (<300 °C), the oxidation by reactive oxygen species, and the combustion of soot (>300 °C). This investigation offers significant contributions to the formulation of highly efficient silver-based catalysts for emissions control, with a particular focus on optimizing Ag dispersion and support interactions to enhance catalytic efficacy.
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Open AccessArticle
Environmental Dispersion of Toxic Effluents from Waste Polyethylene Fires: Simulations with ALOFT
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Giulia De Cet and Chiara Vianello
ChemEngineering 2025, 9(2), 41; https://doi.org/10.3390/chemengineering9020041 - 17 Apr 2025
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In recent years, the Italian peninsula has frequently been affected by fires in waste storage facilities, both accidental and malicious. Waste storage activities must comply with a series of regulations that require the employer to carefully assess the risks associated with the operation
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In recent years, the Italian peninsula has frequently been affected by fires in waste storage facilities, both accidental and malicious. Waste storage activities must comply with a series of regulations that require the employer to carefully assess the risks associated with the operation of the plant. All prevention and protection measures must be taken to reduce the risk of fires in order to safeguard both people and the environment. In addition, with new regulations coming into force in November 2022, efforts are being made to regulate waste treatment and storage facilities in terms of fire safety. This work presents simulations of the dispersion into the environment of toxic effluents produced during a polyethylene fire at a storage site, with the aid of dedicated software. Simulations were carried out using ALOFT, varying the parameters of the simulations (e.g., the burnt area, environmental characteristics, and toxic effluent investigated). In total, 24 simulations were carried out to investigate the emissions of particulate matter and volatile organic compounds in the case of polyethylene fires. The simulations showed that atmospheric stability class and wind speed had a significant impact on the dispersion. The proposed methodology can be applied both in the risk assessment and emergency phases and, eventually, as a valuable tool in post-accident analysis.
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Open AccessArticle
Recycling of Walnut Shell Biomass for Adsorptive Removal of Hazardous Dye Alizarin Red from Aqueous Solutions Using Magnetic Nanocomposite: Process Optimization, Kinetic, Isotherm, and Thermodynamic Investigation
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Vairavel Parimelazhagan, Palak Sharma, Yashaswini Tiwari, Alagarsamy Santhana Krishna Kumar and Ganeshraja Ayyakannu Sundaram
ChemEngineering 2025, 9(2), 40; https://doi.org/10.3390/chemengineering9020040 - 11 Apr 2025
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Dye wastewater poses significant risks to human health and aquatic ecosystems, necessitating efficient remediation strategies. This study developed a magnetic Fe2O3 nanocomposite (MNC) derived from phosphoric acid-treated walnut shell biomass carbon to remove Alizarin red S (AR) dye from polluted
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Dye wastewater poses significant risks to human health and aquatic ecosystems, necessitating efficient remediation strategies. This study developed a magnetic Fe2O3 nanocomposite (MNC) derived from phosphoric acid-treated walnut shell biomass carbon to remove Alizarin red S (AR) dye from polluted water. Characterization techniques confirmed the nanocomposite’s mesoporous structure, superparamagnetic properties (61.5 emu/g), and high crystallinity. Optimization using Response Surface Methodology (RSM) revealed a maximum adsorption efficiency of 94.04% under the following optimal conditions: A pH of 2, AR dye concentration of 85 mg/L, adsorbent dose of 1.5 g/L, and particle size of 448.1 nm. Adsorption followed pseudo-second-order (PSO) kinetics (R2 = 0.9999) and Langmuir isotherm models (R2 = 0.9983), with thermodynamic studies indicating spontaneous and endothermic chemisorption. The intra-particle diffusion model, Bangham, and Boyd plots describe the adsorption process, and external boundary layer diffusion of AR dye molecules in the aqueous phase limits the adsorbate removal rate. Regeneration tests demonstrated reusability over three cycles, with a desorption efficiency of 50.52% using 30 mM HCl. The MNC exhibited a maximum adsorption capacity (Qmax) of 115.35 mg/g, outperforming other adsorbents, making it an efficient and sustainable alternative solution for AR dye removal from water bodies.
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(This article belongs to the Special Issue Chemical Engineering in Wastewater Treatment)
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Open AccessArticle
Research of the Process of Obtaining Monocalcium Phosphate from Unconditional Phosphate Raw Materials
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Abibulla Anarbayev, Balzhan Kabylbekova, Zhakhongir Khussanov, Bakyt Smailov, Nurlan Anarbaev and Yevgeniy Kulikov
ChemEngineering 2025, 9(2), 39; https://doi.org/10.3390/chemengineering9020039 - 2 Apr 2025
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The article presents methods for processing low-grade phosphate raw materials from the Chilisay deposit using a circulation method to produce mineral fertilizers and feed monocalcium phosphate. A study was conducted on the process of obtaining high-quality monocalcium phosphate, and optimal parameters for the
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The article presents methods for processing low-grade phosphate raw materials from the Chilisay deposit using a circulation method to produce mineral fertilizers and feed monocalcium phosphate. A study was conducted on the process of obtaining high-quality monocalcium phosphate, and optimal parameters for the decomposition of low-grade phosphate raw materials were determined. Based on the research, it was established that for the decomposition of phosphate raw materials, phosphoric acid with a concentration of 36–42% P2O5 should be used; the recycle phosphoric acid rate should be 540–560% of the stoichiometric amount required for the formation of monocalcium phosphate (MCP); the decomposition temperature should be 95–100 °C; the decomposition duration should be 40–50 min; the filtration temperature of the insoluble residue should be 85–90 °C; the crystallization temperature of MCP should be 40–45 °C; and the crystallization duration should be 85–90 min. For the sulfation of the mother solution and the production of recycle phosphoric acid, sulfuric acid with a concentration of 86–93% H2SO4 should be used; the sulfuric acid rate should be 95–100% of the stoichiometric amount required for the decomposition of dissolved Ca(H2PO4)2. After drying the wet residue, monocalcium phosphate was obtained with the following composition: P2O5—55%, Ca—18.01%, H2O—4.0%, F—0.01%, As—0.004%, Pb—0.002%. The obtained monocalcium phosphate is used in agriculture as a mineral fertilizer and feed monocalcium phosphate.
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Open AccessArticle
Enhanced Efficiency of CZTS Solar Cells with Reduced Graphene Oxide and Titanium Dioxide Layers: A SCAPS Simulation Study
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Dounia Fatihi, Giorgio Tseberlidis, Vanira Trifiletti, Simona Binetti, Eleonora Isotta, Paolo Scardi, Abderrafi Kamal, R’hma Adhiri and Narges Ataollahi
ChemEngineering 2025, 9(2), 38; https://doi.org/10.3390/chemengineering9020038 - 1 Apr 2025
Abstract
Copper zinc tin sulfide (commonly known as CZTS) solar cells (SCs) are gaining attention as a promising technology for sustainable electricity generation owing to their cost-effectiveness, availability of materials, and environmental advantages. The goal of this study is to enhance CZTS SC performance
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Copper zinc tin sulfide (commonly known as CZTS) solar cells (SCs) are gaining attention as a promising technology for sustainable electricity generation owing to their cost-effectiveness, availability of materials, and environmental advantages. The goal of this study is to enhance CZTS SC performance by adding a back surface field (BSF) layer. SC capacitance simulator software (SCAPS) was used to examine three different configurations. Another option is to replace the cadmium sulfide (CdS) buffer layer with a titanium dioxide (TiO2) layer. The results demonstrate that the reduced graphene oxide (rGO) BSF layer increases the conversion efficiency by 25.68% and significantly improves the fill factor, attributed to lowering carrier recombination and creating a quasi-ohmic contact at the interface between the metal and semiconductor. Furthermore, replacing the CdS buffer layer with TiO2 offers potential efficiency gains and mitigates environmental concerns associated with the toxicity of CdS. The results of this investigation could enhance the efficiency and viability of CZTS SCs for future energy applications. However, it is observed that BSF layers may become less effective at elevated temperatures due to increased recombination, leading to reduced carrier lifetime. This study underlines valuable insights into optimizing CZTS SC performance through advanced material choices, highlighting the dual benefits of improved efficiency and reduced environmental impact.
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(This article belongs to the Special Issue New Advances in Chemical Engineering)
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Open AccessArticle
Drilling Optimization Using Artificial Neural Networks and Empirical Models
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Mohammed F. Al Dushaishi, Ahmed K. Abbas, Mortadha T. Al Saba and Jarrett Wise
ChemEngineering 2025, 9(2), 37; https://doi.org/10.3390/chemengineering9020037 - 31 Mar 2025
Cited by 1
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A key role of drilling optimization is reducing the cost and non-productive time (NPT) for drilling operations. The rate of penetration (ROP) directly affects the overall cost and cost per foot of drilling operations and could lead to significant cost savings or expenses.
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A key role of drilling optimization is reducing the cost and non-productive time (NPT) for drilling operations. The rate of penetration (ROP) directly affects the overall cost and cost per foot of drilling operations and could lead to significant cost savings or expenses. Traditionally, empirical ROP modeling is used to predict bit response or estimate ROP using nearby offset data. Due to the complexity and nonlinearity of ROP, data-driven modeling, such as machine learning (ML), became more attractive. The objective of this paper is to develop an ROP data-driven artificial neural network (ANN) model using drilling and formation data collected from three nearby wells. Additionally, drilling optimization was conducted and compared with traditional empirical ROP models. The advantages and disadvantages of both methods are discussed, and the direction of future data-driven modeling is highlighted. The data-driven ANN model demonstrated strong performance when compared to the field data. The ANN model showed an RMSE and R2 of 3.89 m/h and 0.93 for the training data and an RMSE and R2 of 4.16 m/h and 0.92 for the testing dataset. The sensitivity analysis showed that the ANN model predicted higher ROP than the empirical models in the selected interval. Due to the limited bit wear data compared to the operational parameters, coupled simultaneous data-driven and empirical modeling is believed to be the future direction for data-driven drilling optimization.
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Open AccessReview
Comparison of Dispersing Processes of Bio-Based and Synthetic Materials: A Review
by
Leah Jalowy, Dominik Nemec and Oguzhan Ilhan
ChemEngineering 2025, 9(2), 36; https://doi.org/10.3390/chemengineering9020036 - 26 Mar 2025
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The ever-growing environmental and sustainability awareness as well as the associated increased independence from petroleum has led to bio-based materials increasingly replacing synthetic, non-renewable materials in various applications, including food packaging, coatings, adhesives, and energy storage devices. Although bio-based materials offer advantages such
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The ever-growing environmental and sustainability awareness as well as the associated increased independence from petroleum has led to bio-based materials increasingly replacing synthetic, non-renewable materials in various applications, including food packaging, coatings, adhesives, and energy storage devices. Although bio-based materials offer advantages such as reduced toxicity and harmfulness for humans and the environment, as well as contributing to the conservation of important resources, these aspects are usually not sufficient for commercialization. Integrating bio-based materials into existing technologies is challenging due to inherent disadvantages, such as difficult processability and low moisture resistance, making it difficult to readily substitute them for synthetic materials. Consequently, surface modifications are often necessary to make bio-based materials suitable for the intended applications. This review highlights the critical role of processing methods in the successful substitution of synthetic materials with bio-based alternatives. While previous studies have primarily concentrated on material combinations and formulations of bio-based applications, often considering processing methods as secondary, this review explores the influence and importance of dispersion quality. It examines how varying dispersing methods and process parameters can impact the performance of bio-based materials, alongside addressing the specific requirements for both the materials and the dispersing processes. Furthermore, it focuses on bio-based dispersions based on lignin and polysaccharides, particularly in applications such as bio-based adhesives and binders for battery technologies. By addressing these aspects, this review aims to reveal existing research gaps and provide insights into optimizing the processing of bio-based materials for diverse applications.
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Open AccessArticle
Development of New Xanthan-Aldehyde/Gelatin Nanogels for Enhancement of Ibuprofen Transdermal Delivery: In-Vitro/Ex-Vivo/In-Vivo Evaluation
by
Yacine Nait Bachir, Ramdane Mohamed Said, Mohamed Lamine Abdelli, Walid Namaoui, Meriem Medjkane, Nouara Boudjema, Halima Meriem Issaadi and Elisabeth Restrepo Parra
ChemEngineering 2025, 9(2), 35; https://doi.org/10.3390/chemengineering9020035 - 20 Mar 2025
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The aim of this study was to prepare nanogels based on gelatin and xanthan-aldehyde for the enhancement of ibuprofen transdermal delivery. Firstly, the process of formulating nanogels using the reaction of Schiff’s base was optimized using experimental designs. Secondly, the structural characterization of
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The aim of this study was to prepare nanogels based on gelatin and xanthan-aldehyde for the enhancement of ibuprofen transdermal delivery. Firstly, the process of formulating nanogels using the reaction of Schiff’s base was optimized using experimental designs. Secondly, the structural characterization of nanogels was performed using laser particle size, zetometry, FTIR (Fourier Transform Infrared Spectroscopy), XRD (X-Ray Diffraction), SEM (scanning electron microscopy), and thermogravimetric analysis. Finally, the evaluation of pharmacological characteristics and formulation therapeutic efficacy were achieved using in vitro dissolution kinetics, ex vivo transdermal diffusion studies, and an evaluation of in vivo anti-inflammatory activity. The results of the experimental plan show that the formulations containing a ratio of 15:10 ibuprofen/polymer and a ratio of 1:2 gelatin/xanthan-aldehyde with a gelling time of 2 h exhibited the best results; the formulations showed a mean diameter of 179.9 ± 6.2 nm, a polydispersity index of 0.193, which confirms monodispersed particles, a zeta potential of 24.7 mV, denoting a high degree of particle stability, and an encapsulation rate of 93.78%. The FTIR spectroscopy analysis showed the formation of imine function in the nanogel, and scanning electron microscopy showed the globular and porous form of the formulation. The incorporation of ibuprofen into nanogels improved their in vitro dissolution kinetics and ex vivo transdermal diffusion. The incorporation of nanogels into a patch system for its in vivo anti-inflammatory activity has shown excellent efficiency with a percentage of edema inhibition at a dose of 25 mg and 50 mg of 38.77 ± 1.6% and 82.03 ± 9.03%, respectively, while the commercial reference gel presented inhibition values at a dose of 25 mg and 50 mg of 10.61 ± 1.71% and 37.03 ± 11.43%, respectively. Thus, the innovative pharmaceutical form of ibuprofen offers a promising model for enhancing drug bioavailability and therapeutic effects while reducing adverse effects.
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Open AccessArticle
Soft Actor-Critic Reinforcement Learning Improves Distillation Column Internals Design Optimization
by
Dhan Lord B. Fortela, Holden Broussard, Renee Ward, Carly Broussard, Ashley P. Mikolajczyk, Magdy A. Bayoumi and Mark E. Zappi
ChemEngineering 2025, 9(2), 34; https://doi.org/10.3390/chemengineering9020034 - 18 Mar 2025
Abstract
Amid the advancements in computer-based chemical process modeling and simulation packages used in commercial applications aimed at accelerating chemical process design and analysis, there are still certain tasks in design optimization, such as distillation column internals design, that become bottlenecks due to inherent
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Amid the advancements in computer-based chemical process modeling and simulation packages used in commercial applications aimed at accelerating chemical process design and analysis, there are still certain tasks in design optimization, such as distillation column internals design, that become bottlenecks due to inherent limitations in such software packages. This work demonstrates the use of soft actor-critic (SAC) reinforcement learning (RL) in automating the task of determining the optimal design of trayed multistage distillation columns. The design environment was created using the AspenPlus® software (version 12, Aspen Technology Inc., Bedford, Massachusetts, USA) with its RadFrac module for the required rigorous modeling of the column internals. The RL computational work was achieved by developing a Python package that allows interfacing with AspenPlus® and by implementing in OpenAI’s Gymnasium module (version 1.0.0, OpenAI Inc., San Francisco, California, USA) the learning space for the state and action variables. The results evidently show that (1) SAC RL works as an automation approach for the design of distillation column internals, (2) the reward scheme in the SAC model significantly affects SAC performance, (3) column diameter is a significant constraint in achieving column internals design specifications in flooding, and (4) SAC hyperparameters have varying effects on SAC performance. SAC RL can be implemented as a one-shot learning model that can significantly improve the design of multistage distillation column internals by automating the optimization process.
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(This article belongs to the Special Issue New Advances in Chemical Engineering)
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Open AccessArticle
Performance Assessment of Novel Soda Ash Adsorbent Biogas Sweetening: Fixed Bed Studies, Adsorption Kinetics, and Adsorption Isotherms
by
Register Mrosso and Cleophas Achisa Mecha
ChemEngineering 2025, 9(2), 33; https://doi.org/10.3390/chemengineering9020033 - 17 Mar 2025
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The reliance on greenhouse gas-emitting unrenewable energy sources such as coal, natural gas, and oil, increases climate change. Transitioning to renewable energy, such as biogas, is crucial to reducing environmental degradation and global warming. The existence of impurities such as hydrogen sulfide hampers
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The reliance on greenhouse gas-emitting unrenewable energy sources such as coal, natural gas, and oil, increases climate change. Transitioning to renewable energy, such as biogas, is crucial to reducing environmental degradation and global warming. The existence of impurities such as hydrogen sulfide hampers the application of biogas. Utilizing natural resources for biogas purification is essential to improve access to clean energy for low-income communities. This study used soda ash derived from Lake Natron in Tanzania as a sorbent for H2S removal. Effects of sorbent mass, flow rate, and particle size were investigated. Experimental data were analyzed using kinetic models, adsorption isotherms, and breakthrough curves. Soda ash of 280 μm particle size, a flow rate of 0.03 m3/h, and a mass of 75 g demonstrated the best performance, achieving an efficiency of 94% in removal and a sorption capacity of 0.02 g per 100 g in five repeated cycles. Freundlich and Jovanovich’s isotherms match the data with n = 0.4 and Kj = 0.003, respectively. Adsorption kinetics were best described by the intra-particle model (kid = 0.14, c = 0.59 mg/g, and R2 = 0.972). A breakthrough analysis indicated that the Yoon–Nelson model provided the best fit with an R2 of 0.95. Soda ash from Lake Natron demonstrated great potential in biogas desulphurization, thus contributing to the production and access to clean energy.
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