Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.4
Journals
ChemEngineering
Sections
New Advances in Chemical Engineering
share announcement

New Advances in Chemical Engineering

A topical collection in ChemEngineering (ISSN 2305-7084).

Viewed by 30149

Editors

Dr. Iuliana Deleanu

E-Mail Website
Collection Editor
Faculty of Chemical Engineering and Biotechnology, National University of Science and Technology “Politehnica” Bucharest, 1-7 Polizu Street, 011061 Bucharest, Romania
Interests: environmental engineering; biomaterials; mathematical modeling; chemical and biochemical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriela Olimpia Isopencu

E-Mail Website
Collection Editor
Faculty of Chemical Engineering and Biotechnology, University Politehnica of Bucharestdisabled, Bucuresti, Romania
Interests: unit operation in chemical and biochemical engineering; industrial microbiology; bioreactors; waste and wastewater treatment; active bio/nanomaterials
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

In this Topical Collection, we aim to collect new developments and trends in the chemical engineering field.

“What is chemical engineering?” This is a simple question. The answer, however, is not that simple. There are hundreds of definitions of chemical engineering—some intuitive, some more sophisticated. Many of them mention fundamental sciences, materials sciences, interdisciplinarity, multifunctionality, performance in processes, operations, or manufacturing.

Therefore, through this Topical Collection, you are invited to emphasize, modify, reorganize, redesign, or improve this amazing field of chemical engineering. Topics of interest include but are not limited to petroleum products and the petrochemical industry, process intensification, biorefineries, energy and sustainability, carbon capture, zero waste, the circular economy, etc.

Dr. Iuliana-Mihaela Deleanu
Dr. Gabriela Olimpia Isopencu
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ChemEngineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • green manufacturing
  • process intensification
  • advanced separations
  • mathematical modelling of chemical processes
  • artificial intelligence in chemical engineering
  • wastes and by-products valorization
  • innovative materials
  • carbon capture

Published Papers (13 papers)

Download All Papers

2025

Jump to: 2024

45 pages, 7780 KB  
Article
Mathematical and Neuro-Fuzzy Modeling of a Hollow Fiber Membrane System for a Petrochemical Process
by Bryand J. Garcia-Sigales, Jose A. Ruz-Hernandez, Jose-Luis Rullan-Lara, Alma Y. Alanis, Mario Antonio Ruz Canul, Juan Carlos Gonzalez Gomez and Francisco J. Romero-Sotelo
ChemEngineering 2025, 9(6), 115; https://doi.org/10.3390/chemengineering9060115 - 22 Oct 2025
Viewed by 554
Abstract
This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), [...] Read more.
This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), and temperature-dependent viscosity; species permeances are treated as constant for model validation. After validation, a post-validation parametric exploration of permeance variability is carried out by perturbing the methane (CH4) permeance by one decade up and down. From an initial set of 18 variables, 4 key parameters were selected through rigorous statistical analysis (Pearson correlation, variance inflation factor (VIF), and mean absolute error (MAE)); likewise, other physical criteria have been considered: permeance, retentate volume, retentate pressure, and retentate viscosity. Trained with 70% of the simulated data and validated with the remaining 30%, the model achieves a coefficient of determination (R2) close to 0.999 and a root mean square error (RMSE) below 8 × 10−8 m3/h in predicting the methane volume in the retentate, effectively responding to both steady and dynamic fluctuations. The combination of first-principles modeling and adaptive learning captures both steady-state and dynamic behavior, positioning the approach as a viable tool for real-time analysis and supervisory control in petrochemical membrane operations. Full article
►▼ Show Figures

Figure 1

31 pages, 1511 KB  
Article
Economic Evaluation During Physicochemical Characterization Process: A Cost–Benefit Analysis
by Despina A. Gkika, Nick Vordos, Athanasios C. Mitropoulos and George Z. Kyzas
ChemEngineering 2025, 9(5), 95; https://doi.org/10.3390/chemengineering9050095 - 2 Sep 2025
Viewed by 1143
Abstract
As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a [...] Read more.
As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a simplified approach for evaluating the effects of the implementation of prevention investments in regard to working with nanomaterials on a lab scale. The evaluation is based on modeling the benefits (avoided accident costs) and costs (safety training), as opposed to an alternative (not investing in safety training). Each scenario analyzed in the economic evaluation reflects a different level of risk. The novelty of this study lies in its objective to provide an economic assessment of the benefits and returns from safety investments—specifically training—in a chemical laboratory, using a framework that integrates qualitative insights to explore and define the context alongside quantitative data derived from a cost–benefit analysis. The Net Present Value (NPV) was evaluated. The results of the cost–benefit analysis demonstrated that the benefits exceed the cost of the investment. The findings from the sensitivity analysis highlight the significant influence of insurance benefits on safety investments in the specific case study. In this case study, the deterministic analysis yielded a Net Present Value (NPV) of €280,414.67, which aligns closely with the probabilistic results. The probabilistic NPV indicates 90% confidence that the investment will yield a positive NPV ranging from €283,053 to €337,356. The cost–benefit analysis results demonstrate that the benefits outweigh the costs, showing that with an 87% training success rate, this investment would generate benefits of approximately €6328 by preventing accidents in this study. To the best of the researchers’ knowledge, this is the first study to evaluate the influence of safety investment through an economic evaluation of laboratory accidents with small-angle X-ray scattering during the physicochemical characterization process of engineered nanomaterials. The proposed approach and framework are relevant not only to academic settings but also to industry. Full article
►▼ Show Figures

Figure 1

21 pages, 12700 KB  
Article
Optimization of Developed TiO2 NWs-Fe2O3 Modified PES Membranes for Efficient NBB Dye Removal
by Mouna Mansor Hussein, Qusay F. Alsalhy, Mohamed Gar Alalm and M. M. El-Halwany
ChemEngineering 2025, 9(4), 82; https://doi.org/10.3390/chemengineering9040082 - 1 Aug 2025
Cited by 1 | Viewed by 858
Abstract
Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO2 NWs-Fe2O3), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. [...] Read more.
Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO2 NWs-Fe2O3), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO2 NWs-Fe2O3 has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m2·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO2 NWs-Fe2O3-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO2 NWs-Fe2O3 membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment. Full article
►▼ Show Figures

Figure 1

27 pages, 2690 KB  
Article
Adsorption of Methylene Blue on Metakaolin-Based Geopolymers: A Kinetic and Thermodynamic Investigation
by Maryam Hmoudah, Rosanna Paparo, Michela De Luca, Michele Emanuele Fortunato, Olimpia Tammaro, Serena Esposito, Riccardo Tesser, Martino Di Serio, Claudio Ferone, Giuseppina Roviello, Oreste Tarallo and Vincenzo Russo
ChemEngineering 2025, 9(4), 79; https://doi.org/10.3390/chemengineering9040079 - 25 Jul 2025
Cited by 1 | Viewed by 1171
Abstract
Metakaolin-based geopolymers with different molar ratios of Si/Al were synthesized and utilized as an efficient adsorbent for the removal of methylene blue (MB) as a model cationic dye from aqueous solution. Various analytical techniques were employed to characterize the synthesized geopolymers. The influence [...] Read more.
Metakaolin-based geopolymers with different molar ratios of Si/Al were synthesized and utilized as an efficient adsorbent for the removal of methylene blue (MB) as a model cationic dye from aqueous solution. Various analytical techniques were employed to characterize the synthesized geopolymers. The influence of the main operation conditions on the adsorption kinetics of MB onto the geopolymer was examined under various operating conditions. Results showed a significant maximum MB adsorption capacity at the temperature of 30 °C for all four types of geopolymers studied (designated as A, B, C, and D) up to 35.3, 23.6, 25.5, and 19.0 mg g−1, respectively. The corresponding order of Si/Al ratio was A < C < B < D. Adsorption kinetics was so fast and reached equilibrium in 10 min, and the experimental results were described using the adsorption dynamic intraparticle model (ADIM). The equilibrium data for MB removal was in agreement with the Langmuir isotherm. Full article
►▼ Show Figures

Figure 1

29 pages, 6667 KB  
Article
Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach
by Gabriel Marín Díaz
ChemEngineering 2025, 9(3), 45; https://doi.org/10.3390/chemengineering9030045 - 28 Apr 2025
Cited by 2 | Viewed by 1866
Abstract
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 [...] Read more.
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. Full article
►▼ Show Figures

Figure 1

19 pages, 6273 KB  
Article
Enhanced Efficiency of CZTS Solar Cells with Reduced Graphene Oxide and Titanium Dioxide Layers: A SCAPS Simulation Study
by 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
Cited by 1 | Viewed by 2031
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 [...] Read more.
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. Full article
►▼ Show Figures

Figure 1

20 pages, 17704 KB  
Article
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
Viewed by 1944
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 [...] Read more.
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. Full article
►▼ Show Figures

Figure 1

15 pages, 2449 KB  
Article
Improvement of Malagasy Traditional Earth Bricks by Using a Derivative of CNSL as a Binding Agent
by Volana Mifelana Holiarinala, Fenia Diane Ramiharimanana, Hermann Rafanoela, Stephanoel Randriatsarazaka, Raymond Razafimahatratra, Estelle Metay, Voahangy Vestalys Ramanandraibe and Marc Lemaire
ChemEngineering 2025, 9(2), 22; https://doi.org/10.3390/chemengineering9020022 - 21 Feb 2025
Cited by 1 | Viewed by 1529
Abstract
Traditional bricks are still the most widely used building material in Madagascar. Bricks are made from clay that is fired for weeks in open-air kilns (600–750 °C) by using rice husks, peat, charcoal, coal, and wood as fuels. This process contributes significantly to [...] Read more.
Traditional bricks are still the most widely used building material in Madagascar. Bricks are made from clay that is fired for weeks in open-air kilns (600–750 °C) by using rice husks, peat, charcoal, coal, and wood as fuels. This process contributes significantly to environmental pollution by emitting CO2 and particles. In addition, the intensive use of wood and charcoal is partly responsible for the deforestation that still taking place on the “Red Island”. The development of sustainable building materials is therefore of global interest. This research provided a solution by implementing the oxyacetic acid derivative of cashew nut shell liquid (CNSL) as a binder to reduce energy consumption in the preparation of earthen materials. This product was obtained from cashew nut waste and was used in a proportion of 5 to 15% with the red soil of Madagascar. The materials were formulated at a much lower temperature (60 °C) compared to the traditional process for 24 to 48 hours in a custom-designed mold. The material with 10% oxyacetic binder from CNSL was a compact, hard solid with higher mechanical properties, including a twice higher compressive strength (5.6 MPa compared to 2.2 MPa) and a higher tensile strength (2.2 MPa compared to 1.6 MPa). This material also had better water resistance after 2 months of immersion; traditional clay bricks absorbed 36.65% of the water, and the material with binder only absorbed 12.62%. This research demonstrates that the utilization of local agricultural waste as a binder is a viable strategy for reducing the carbon footprint of traditional building materials while significantly improving their physico-mechanical properties. Full article
►▼ Show Figures

Figure 1

17 pages, 5962 KB  
Article
A Case Study on Integrating an AI System into the Fuel Blending Process in a Chemical Refinery
by Abdul Gani Abdul Jameel
ChemEngineering 2025, 9(1), 4; https://doi.org/10.3390/chemengineering9010004 - 3 Jan 2025
Cited by 3 | Viewed by 5581
Abstract
Fuel blending plays a very important role in petroleum refineries, because it directly affects the quality of the end products, as well as the overall profitability of the refinery. This process of blending involves a combination of various hydrocarbon streams to make fuels [...] Read more.
Fuel blending plays a very important role in petroleum refineries, because it directly affects the quality of the end products, as well as the overall profitability of the refinery. This process of blending involves a combination of various hydrocarbon streams to make fuels that meet specific performance standards and comply with regulatory guidelines. For many decades, most refineries have been dependent on linear programming (LP) models for developing recipes for blending optimization. However, most LP models normally fail to capture the complex nonlinear interaction of blend components with fuel properties, leading to off-specification products that may necessitate re-blending. This work discusses a case study of a hybrid artificial intelligence (AI)-based method for gasoline blending based on a genetic algorithm (GA) combined with an artificial neural network (ANN). AI-based blending systems are more flexible and will enable the refineries to meet the product specifications regularly and result in cost reduction owing to the fall in quality giveaways. The AI-powered process discussed can predict, with much better accuracy, critical combustion properties of gasoline such as the Research Octane Number (RON), Motor Octane Number (MON), and Antiknock Index (AKI), compared to the classical LP models, with the added advantage of optimization of the blend ratio in real time. The results showed that the AI-integrated fuel blending system was able to produce fuel recipes with a mean absolute error (MAE) of 1.4 for the AKI. The obtained MAE is close to the experimental uncertainty of 0.5 octane. A high coefficient of determination (R2) of 0.99 was also obtained when the system was validated with a new set of 57 fuels comprising primary reference fuels and real gasoline blends. The study highlights the potential of AI-integrated systems in transforming traditional fuel blending practices towards sustainable and economically viable refinery operations. Full article
►▼ Show Figures

Figure 1

2024

Jump to: 2025

13 pages, 4399 KB  
Article
Photo-Oxidation of Various Organic Compounds, Including Pollutants, by Europium (III) in Fuel Cell Systems
by Felix Blind and Stefan Fränzle
ChemEngineering 2024, 8(6), 121; https://doi.org/10.3390/chemengineering8060121 - 1 Dec 2024
Viewed by 1561
Abstract
The ongoing anthropogenic climate crisis necessitates a reassessment of numerous technical domains, including the energy sector. An alternative to conventional fuel cells is provided by photo fuel cells, which possess at least one photoactive electrode (e.g., TiO2). However, it should be [...] Read more.
The ongoing anthropogenic climate crisis necessitates a reassessment of numerous technical domains, including the energy sector. An alternative to conventional fuel cells is provided by photo fuel cells, which possess at least one photoactive electrode (e.g., TiO2). However, it should be noted that such fuel cells are often constrained in terms of the range of potential fuels that can be utilized. Considering prior research on the distinctive photochemistry of europium, it was hypothesized hypothesis that a photocell based on the photo-oxidation of diverse organic compounds by trivalent europium might be theoretically feasible. As demonstrated in multiple experiments, it is feasible to construct and operate a fuel cell utilizing these diverse, straightforward substrates. In this context, peak powers of up to 14 μW have already been observed with the fuel cell described. It is noteworthy that an average electrical power of up to 6.28 μW was observed over a period of 168 h (7 days). Furthermore, it was demonstrated that simple alcohols (ethanol) could be completely oxidized with trivalent europium under suitable conditions. From various studies with different ethanol concentrations, it could be seen that a certain amount of water was needed to break down simple alcohols and organic compounds in general. Full article
►▼ Show Figures

Figure 1

30 pages, 5693 KB  
Review
Heavy Metal Pollution and Solutions for Its Control: General Aspects with a Focus on Cobalt Removal and Recovery from Aqueous Systems
by Tănase Dobre, Gabriela Olimpia Isopencu, Shaalan Bdaiwi Ahmed and Iuliana Mihaela Deleanu
ChemEngineering 2024, 8(6), 118; https://doi.org/10.3390/chemengineering8060118 - 18 Nov 2024
Cited by 1 | Viewed by 4725
Abstract
Heavy metal pollution is a worldwide and stringent concern following many decades of industrialization and intensive mining without (in some cases) consideration for environmental protection. This review aims to identify the existing and emerging techniques for heavy metals (HM) removal/recycling from water and [...] Read more.
Heavy metal pollution is a worldwide and stringent concern following many decades of industrialization and intensive mining without (in some cases) consideration for environmental protection. This review aims to identify the existing and emerging techniques for heavy metals (HM) removal/recycling from water and wastewater, with an emphasis on cobalt. Unlike many other heavy metals, cobalt has not been considered a detrimental element for the environment and human beings until recently. Thus, several methods and applicable techniques were evaluated to identify the best treatment approaches applicable to cobalt-polluted water and wastewater. The most feasible depollution methods adapted to the source, environment, and economic conditions were investigated and concluded. The operations and processes presented in this paper are conventional and innovative as well, including precipitation, membrane separation, with emphasis on ultrafiltration (UF) and nanofiltration (NF), but also reverse osmosis/forward osmosis (RO/FO), sorption/chemisorption processes, flotation/mechanical separation operations combined with coagulation/flocculation, photocatalysis, and electrochemical processes. For each one, depending on the frequency of use, physicochemical mechanisms and optimal operational conditions were identified to carry out successful cobalt removal and recovery from aqueous environments. Full article
►▼ Show Figures

Figure 1

29 pages, 3209 KB  
Review
Reverse Polarity-Based Soil Electrokinetic Remediation: A Comprehensive Review of the Published Data during the Past 31 Years (1993–2023)
by Ahmed Abou-Shady and Heba El-Araby
ChemEngineering 2024, 8(4), 82; https://doi.org/10.3390/chemengineering8040082 - 15 Aug 2024
Cited by 14 | Viewed by 3404
Abstract
Soil restoration by exploiting the principles and basics of electrokinetic (EK) has been extended to involve several categories, such as electrokinetic remediation in soil (SEKR), soil consolidation, the prevention of soil pollution, reclaiming salt-affected soil, the dewatering/dryness of wet soils, water reuse, seed [...] Read more.
Soil restoration by exploiting the principles and basics of electrokinetic (EK) has been extended to involve several categories, such as electrokinetic remediation in soil (SEKR), soil consolidation, the prevention of soil pollution, reclaiming salt-affected soil, the dewatering/dryness of wet soils, water reuse, seed germination, sedimentation, etc. As an extension of our recently published review articles on the soil electrokinetic (SEK) process intensification/optimization, the present review illustrates the effect of a reverse-polarity mode (RPM) on the efficiency of the SEK. Based on several searches of six database search engines, we did not find any relevant reviews focused on SEK improvements using the RPM. The influences of the RPM are described by various features, including (a) pollutant removal (organic, inorganic, and mixed pollutants) and (b) integration with other processes (phyto/bioremediation and Fenton oxidation), geosynthetics (consolidation, stabilization, and sedimentation), SEK operation conditions, and soil properties. Most of the RPM studies have focused on the remediation of organic pollutants. Several benefits can be gained from applying the RPM, such as (a) controlling the soil’s temperature, pH, and moisture values at desirable levels, (b) reducing a large number of chemical additives, (c) high remediation efficiency, (d) maintaining the indigenous fungal community’s appropriate diversity and abundance, (e) a stable and higher electric current, (f) enhancing microbial growth, etc. However, the hindrances to applying the RPM are (a) reducing the electroosmosis flow, (b) relatively high energy consumption, (c) reducing the diversity of soil microbes with a prolonged experiment period, (d) providing oxygen for a microbial community that may not be desirable for anaerobic bacteria, etc. Finally, the RPM is considered an important process for improving the performance of the SEK, according to experimental endeavors. Full article
►▼ Show Figures

Figure 1

11 pages, 1540 KB  
Article
Predictive Modeling and Analysis of Cu–Be Alloys: Insights into Material Properties and Performance
by Mihail Kolev
ChemEngineering 2024, 8(4), 70; https://doi.org/10.3390/chemengineering8040070 - 10 Jul 2024
Cited by 1 | Viewed by 2385
Abstract
Cu–Be alloys are renowned for their exceptional mechanical and electrical properties, making them highly sought after for various industrial applications. This study presents a comprehensive approach to predicting the compositions of various types of Cu–Be alloys, integrating a Random Forest Regressor within a [...] Read more.
Cu–Be alloys are renowned for their exceptional mechanical and electrical properties, making them highly sought after for various industrial applications. This study presents a comprehensive approach to predicting the compositions of various types of Cu–Be alloys, integrating a Random Forest Regressor within a machine learning (ML) framework to analyze an extensive dataset of chemical and thermo-mechanical parameters. The research process incorporated data preprocessing, model training and validation, and robust analysis to discern feature significance. Cluster analysis was also conducted to illuminate the data’s intrinsic groupings and to identify underlying metallurgical patterns. The model’s predictive power was confirmed by high R2 values, indicative of its capability to capture and explain the variance in the dataset for both testing (R2 = 0.99375) and training (R2 = 0.99858). Distinct groupings within the alloy data were uncovered, revealing significant correlations between composition, processing conditions, and alloy properties. The findings underscore the potential of ML techniques in advancing the material design and optimization of Cu–Be alloys, providing valuable insights for the field of material science. Full article
►▼ Show Figures

Figure 1

Back to TopTop