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Journal

Chemical Product and Process Modeling

Edited by: Rahmat Sotudeh-Gharebagh , Navid Mostoufi and Jamal Chaouki

Language: English

First published: May 1, 2006

Publication Frequency: 6 issues per year

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About this journal

Objective
Chemical Product and Process Modeling (CPPM) is a quarterly journal that publishes theoretical and applied research on product and process design modeling, simulation and optimization. Thanks to its international editorial board, the journal assembles the best papers from around the world on to cover the gap between product and process. The journal brings together chemical and process engineering researchers, practitioners, and software developers in a new forum for the international modeling and simulation community. Editors represent top engineering institutions across the globe, such as the University of Tehran, the Ecole Polytechnique de Montreal, Rutgers, Indian Institute of Technology, the Technical University Hamburg, Jordan University of Science and Technology, Dalhouse University, University Politehnica of Bucharest, University College London, Auburn University, Universidade do Minho, National University of Singapore, University of Paderborn, University of Lapeenranta, University of Pannonia, The City College of New York, Indian Institute of Technology Roorkee, Texas A&M University at Qatar, Texas A&M University at Qatar, Politecnico of Milano, University of Wollongong, Norwegian University of Science and Technology, CANMET Energy Technology Centre, University of Saskatchewan, Universitat Politecnica de Catalunya, Ecole Nationale Supérieure en Génie des Technologies Industrielles, ENSGTI, University of Western Ontario, Aristotle University of Thessaloniki and Centre for Research and Technology, IFP Energies nouvelles, University of Calgary, Abo Akademi University, Technical University Hamburg, University of Auckland, UCLA School of Public Health and the University of Buenos Aires.

Topics

equation oriented and modular simulation
optimization technology for process and materials design, new modeling techniques
shortcut modeling and design approaches
performance of commercial and in-house simulation and optimization tools
challenges faced in industrial product and process simulation and optimization
computational fluid dynamics
environmental process, food and pharmaceutical modeling
topics drawn from the substantial areas of overlap between modeling and mathematics applied to chemical products and processes

Article formats
Editorial Notes, Research Articles, Reviews

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the premier forum for research on product and process design modeling and simulation
theoretical and applied approach to the subject-matter
the best papers from around the world on modern modeling, design, simulation and optimization techniques
international editorial board

History

Content available since 2006 (Volume 1, Issue 1)

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AI-based prediction and interpretation of column head temperature in a continuous distillation: a case study on methylcyclohexane separation

September 29, 2025

Mohammed El Jattioui, Imad Manssouri, Houssame Limami

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This study explores the application of artificial intelligence (AI) to predict the column head temperature in a continuous distillation process designed to separate methylcyclohexane from a binary mixture containing 23 % methylcyclohexane by mass. Several AI models were developed and evaluated, using key operational parameters such as heating power, reflux ratio, feed rate, pressure drop, and boiler temperature as input features. The column head temperature served as the target variable, representing the performance of the distillation system. Among the models tested, the Decision Tree Regressor achieved the best performance, with a mean absolute error (MAE) of 0.0090, a root mean square error (RMSE) of 0.0258, and a coefficient of determination R 2 of 0.9572. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis was applied, revealing that reflux ratio and boiler temperature are the most influential variables. These results demonstrate the model’s effectiveness in predicting the normal operation of an automated continuous distillation column. Furthermore, the model shows potential for real-time implementation, offering a promising approach for online monitoring and fault detection in industrial distillation processes.
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CFD modeling and simulation of catalytic pyrolysis of heavy oils in a tapered fluidized bed reactor

September 25, 2025

Chellappandian Gowtham, Jagannathan T. Kalathi

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A fluidized bed reactors (FBRs) have been widely used for catlytic cracking, combustion, gasification, pyrolysis and other applications. However, to improve the performance of FBRs, a better understanding of its flow behaviour is required, especially when multiphases are present. In this research work, we have studied the hydrodynamics and performance of FBR for the catalytic pyrolysis of heavy oil into lighter fractions using a Computational Fluid Dynamics (CFD) approach. The eight-lump kinetic model was used to model the pyrolysis of heavy oil. The effect of riser geometry on the pyrolysis was investigated using a 2D transient Eulerian and the granular flow models. The fluid flow behaviour in tapered-in and tapered-out reactors (risers) for two different tapering angles (1° and 2°), conventional cylindrical reactor and pyrolysis at two different temperatures (600  ° C and 700  ° C) are studied, and the results are compared. The yield of pyrolysis products from the cylindrical riser is validated using previous mathematical models and experimental results from the literature. The results of the present CFD model for the cylindrical riser are in concert with the experimental results reported in the literature. The yields of light olefins, ethene, propene and butene are 48 wt%, 18 wt%, 34 wt%, respectively, at 700° as higher temperature favours a better yield of pyrolysis products. The same CFD model is extended to study the tapered riser geometries, and the simulation results support that the tapered-in geometry favours the pyrolysis, resulting in the higher conversion of gas oil compared to cylindrical riser due to increased residence time of solids (catalysts) and hence better contact with the fluid phase for the reactions.
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Development of an empirical model for microwave assisted CO₂ stripping/solvent recovery

September 23, 2025

Shree Vidhya Ramamoorthy, Ambedkar Balraj

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With global CO 2 emissions continuing to rise, finding efficient and scalable carbon capture solutions is more important than ever. Among available technologies, post-combustion carbon capture (PCCC) using solvents like monoethanolamine (MEA), piperazine (PZ), and potassium carbonate (K 2 CO 3 ) remains one of the most practical and widely adopted methods. Traditional regeneration methods, such as steam stripping, are often energy-intensive and limit process efficiency. This study explores the use of microwave (MW) heating as a promising alternative for solvent regeneration, aiming to reduce energy use and improve system performance. Experiments were designed using Taguchi’s L 27 orthogonal array, testing the effects of input power, regeneration time, solvent load, and solvent type. Results showed that PZ had the highest stripping rate, while K 2 CO 3 offered the best diffusivity. The stripping rate increased linearly with input power due to enhanced electromagnetic energy transfer. An optimum regeneration time of 15 min and solvent load of 40 g provided maximum stripping performance. An empirical model was developed to predict CO 2 stripping efficiency, incorporating stripping rate, diffusivity, nominal input power, rich and lean carbon loading, density, viscosity, surface tension, initial, final and average temperatures, molecular weight of solvent and CO 2 , change in mass after experiment, regeneration time, initial and final pH considered to develop the model. The correlation matrix and dimensional analysis were employed to formulate dimensionless groups influencing stripping efficiency and least square method was utilized to develop the model. t -test as a statistical validation technique confirmed model’s reliability at 98.9 % accuracy and 95 % confidence level signifying excellent predictive capability.
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A strategy for enhancing system reliability and grid integration using bagging-driven ensemble framework

September 18, 2025

Chao Li

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Fuel cell energy systems have a potential as a clean and efficient power generation method, but suffer a high cost of production and engineering of fuel cells, multi-faceted operating principles, loss of performance over time, and vulnerability under the changing operation. Therefore, accurate predictions of energy output from fuel cell systems are critical for performance optimization, ensuring stable integration into the energy grids, and improving overall economic viability. This work presents advanced research on predictive modeling for fuel cell system energy forecasting, with a focus on increasing the accuracy and reliability of energy forecasting. The main innovation of this work is the use of the Bootstrap Aggregation (Bagging) ensemble farmwork that aggregate the outputs of multiple models such as Support Vector Regression, Long Short-Term Memory, Recurrent Neural Networks, and AdaBoost to mitigate individual model biases, reduce variance, and enhance generalization performance. The findings indicates that the Bagging algorithm performed excellently in improving the accuracy of energy output from fuel cell predictions by exploiting the benefits of multiple learners. It shows the best R 2 of 0.984352, with lowest MAE and RMSE equal to 17.00 and 23.65 on training set meaning that it perfectly fitted the predicted and actual values. Notably, Bagging continues with good generalization in the test set having the lowest MAE and RMSE equal to 44.60 and 60.77 and the highest R 2 value of 0.888772.
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Temporal instability analysis of power-law fluid jets in quiescent air under symmetric (varicose) perturbations

September 17, 2025

Karthick Sundaresan, Dineshwar Murugan, Sharan Kabaleeswaran

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Power-law fluids, such as paints, silica suspensions, and polymers, are frequently employed in industrial processes such as spray painting, agricultural spraying, and fuel injection, which disintegrate liquid sheets into smaller droplets. Their flow characteristics are distinct and intricate because of the absence of fixed viscosity. In contrast to Newtonian fluids, power-law fluid atomization is significantly influenced by the non-linear relationship between shear stress and shear rate. With an emphasis on the interaction of fluid rheology, sheet dynamics, and instability processes, this work explores the primary breakup of power-law liquid sheets. Using linear stability analysis, a theoretical attempt has been made to capture the primary breakup characteristics of these liquids in stagnant air under the influence of symmetric(varicose) mode of interfacial instability. Combining the continuity equation, linearised momentum equation, and the relevant boundary conditions, the dispersion relation for predicting the stability of the tested mode has been arrived. The stability variables namely the temporal growth rate, the breakup frequency, and the critical wave number were determined. On plotting the primary breakup variables in the dimensionless form, regression functions have attained a superior correlation coefficient for both shear-thinning and shear-thickening liquids.
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Optimizing resilience and sustainability: a tri-level energy management framework for multi-energy microgrids with power-to-hydrogen technology

September 17, 2025

Mingyu Li

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This paper proposes a novel tri-layer energy management system (EMS) architecture for community microgrids, consisting of a Primary Community Microgrid (PCMG) layer, multiple Microgrid (MG) layers, and individual prosumer layers. The framework is designed to enhance coordination, autonomy, and efficiency in distributed energy systems. A dual-level optimization strategy is adopted: day-ahead planning at the PCMG level and real-time scheduling at the MG and prosumer levels. Each layer operates under a distinct objective function – cost minimization, profit maximization, and utility satisfaction while maintaining inter-layer communication and decision consistency. A simulation-based case study is conducted using realistic parameters to evaluate the framework’s performance under various operating scenarios. The validation demonstrates the framework’s effectiveness in improving energy distribution, enhancing prosumer participation, and reducing overall operational costs. Comparative results with a conventional single-layer EMS model are presented using graphical and numerical analyses, confirming the proposed framework’s advantages in flexibility, scalability, and energy efficiency.
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Enhancing model accuracy in lithium-ion battery crystal system development

September 17, 2025

Lin Wang

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One of the most significant contemporary energy storage technologies is lithium-ion batteries, and the performance and stability of such batteries are closely associated with the constituent minerals’ crystal structure. In this work, machine learning is applied for cattregorizing lithium-ion batteries into three major crystal systems: monoclinic, orthorhombic, and triclinic. The performance comparison of several machine learning models, namely Support Vector Classifiers, Nu-support Vector Classification, K-Nearest Neighbors, Adaptive Boosting, Extra Trees, and the optimized Extra Trees model, POA-ET, which is achieved via applying Pelican Optimization Algorithm, are considered in this investigation. Among those, the POA-ET emerges as the best and yields high performances in terms of several key metrics regarding the test and training data sets. The model’s reliability is further validated through further analysis, demonstrating its robustness and generalizability. Spacegroup, Nsites, and Volume respectively are the most important features according to feature importance analysis, significantly impacting the predictions. These results not only demonstrate the predictive capability of the machine learning models but also give insights into the factors underlying lithium-ion battery material performance. The presented work bridges computational modeling with practical applications and thus opens an avenue for creating high-performing lithium-ion batteries in order to aid in the world’s shift to sustainable and clean energy systems.
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Tuning of PID controllers for pure integrating and unstable time delay systems

September 12, 2025

Hemalatha Sarisa, Padma Sree R

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Designing a Proportional-Integral-Derivative (PID) controller for pure integrator with time delay and unstable first order plus time delay systems by extending the method proposed by Mikhalevich et al. (Mikhalevich SS, Baydali SA, Manenti F. Development of a tunable method for PID controllers to achieve the desired phase margin. J Process Control 2015;25:28–34). In this paper, the same method is applied for pure integrating systems with time delay and achieved good response for servo problem. Later the method is applied to unstable systems by stabilizing the unstable system in inner loop with proportional controller and tuning a PID controller for the stabilized system in the outer loop. The servo and regulatory performance of the designed controllers is compared with the recently reported literature methods for various transfer function models.
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Antibacterial activity trends in g-C₃N₄/CuO nanocomposites: the crucial interplay of CuO ratios and zeta potential

September 12, 2025

Gaurav Mahnot Jain, Sravya Kolluru, Lingala Eswaraditya Reddy, Dhruv Gollapudi, Bala Krishna Inguva, Ganesh Botla, Gubbala V. Ramesh

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In recent times as antibiotic resistance grows, given opportunity to find new ways to kill germs. In this research work, the potential of g-C 3 N 4 /CuO nanocomposite as an antibacterial agent has been investigated. Using the natural properties of Graphitic carbon nitride (g-C 3 N 4 ) and the known antimicrobial properties of copper oxide (CuO), the discovery of the composite was demonstrated against bacterial strains (Staphylococcus and Pseudomonas) in this study. The antibacterial performance of the composite was found to be dependent on the relative concentration of CuO. The zeta potential is another critical parameter that plays a vital role in the stability of the nanoparticles in solution is explored. This detailed analysis opens up the possibility of using g-C 3 N 4 /CuO nanocomposites with optimal CuO concentrations as effective antibacterial agents against multi-drug-resistant pathogens.
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Green synthesis of fluorescent carbon dots from watermelon peel waste for sensing applications

September 3, 2025

Ganesh Botla, Praveen BVS, Mallaiah Mekala

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Carbon dots (CDs) are versatile nanomaterials valued for their fluorescence, biocompatibility, and potential in bioimaging and drug delivery. Current synthesis methods, such as hydrothermal or microwave techniques, often require specialized equipment and energy-intensive processes, limiting their accessibility. This study presents an efficient, sustainable approach to synthesizing CDs from watermelon peel, an abundant waste biomass, using a round-bottom flask. Watermelon peel was sun-dried, crushed, and boiled with distilled water (1:10 w/v) for 5 h, followed by centrifugation. The resulting CDs exhibited blue fluorescence under a fluorometer, indicative of small particle sizes (<10 nm), and a UV-vis absorption peak at 250 nm, consistent with π-π* transitions of conjugated C=C and C=O bonds. This method leverages simple equipment and a renewable precursor, offering a cost-effective, eco-friendly alternative to conventional techniques. The CDs’ optical properties suggest suitability for biomedical applications, though further optimization (e.g., quantum yield, size distribution) is needed. This work highlights the potential of waste-derived CDs, contributing to sustainable nanotechnology development.
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Fundamental feature-based assessment of water quality through optimal enhanced models

September 3, 2025

Peng Xu, Jidong Zhang

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High water quality (WQ) is of the essence to human health and environmental sustainability. The resultant effects are viewed in industrial and agricultural productivity and the drinking water supply. In light of the recent trend of water-based tourism and recreational activities, it is now more than ever relevant to commit to stringent enforcement of the regulations on WQ. Compliance with these norms will ensure that the security of drinking water, agricultural productivity, and industrial processes is maintained accordingly. It can predict water quality by finding patterns within different water parameters by analyzing past and present data. The given study explains the use of Support Vector Classification (SVC), Gradient Boost Classification (GBC), and Random Forest Classification (RFC) that have been optimized using Fitness Dependent Optimizer Algorithm (FDOA) and Cheetah Optimization Algorithm (COA), respectively, to improve their accuracy in projection. Considering the results of evaluation metrics for models, the GBCO, SVCO, and RFCO models registered 0.992, 0.966, and 0.944 values of Accuracy, while the GBC, SVC, and RFC single models achieved 0.956, 0.933, and 0.904 values. Implementing the optimization algorithms, especially the COA, enhanced the Accuracy of single models as 3.6 %, 3.3 %, and 4 % for GBC, SVC, and RFC.
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Hybrid AI-based forecasting of green hydrogen and solar power using AdaBoost-BWOA

September 3, 2025

Zhaiaibai Ma, Ye Wang

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There is a massive, accelerating restructuring of the global energy sector as it attempts to meet the pivotal need to address climate change and earn a sustainable future. Electrolysis, using renewable energy to create green hydrogen, is critical as we move to sustainability. However, reliable prediction of hydrogen production based on the solar sources remains problematic. This paper recommends a breakthrough hybrid framework based on machine learning that combines Adaptive Boosting (AdaBoost) and the Black Widow Optimization Algorithm (BWOA) for forecasting Hydrogen Production Rate (HPR) and Photovoltaic Cell Power (PPV) in the proposed solar-powered system, considering meteorological data from Abu Dhabi. The model was designed and evaluated using standardized data in an 80:20 train-test ratio as preparation and evaluation. The R 2 of the AdaBoost-BWOA model was outstanding at 0.998 for HPR and 0.998 for PPV, and RMSE value was as low as 11.282 and 2,114.667, respectively. The proposed best model (AdaBoost-BWOA) outperformed other hybrid approaches, such as MLP-BWOA, LGB-BWOA, DT-BWOA, PSO-AdaBoost, GA-AdaBoost, and DE-AdaBoost by providing significant improvements in both RMSE and R 2 values. The refined system also estimated a maximum hydrogen yield of 542.91 kg per hour in Summer. This performance validates the reliable predictive capabilities of the model that are crucial for maximizing hydrogen production in solar systems and for practical purposes in the technologies of sustainable energy.
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Screening of anti-carcinogenic properties of phytocompounds from Moroccan Marrubium vulgare for treating breast and prostate cancer through in silico approaches

September 2, 2025

Amal Bouribab, Ammar A. Razzak Mahmood, El Mehdi Karim, Meriem Khedraoui, Lamiae El Bouamri, Abdelkbir Errougui, Samir Chtita

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Breast and prostate cancers are among the most prevalent cancers worldwide, with current treatments often limited by reduced efficacy and adverse side effects. In search of safer and more effective alternatives, Marrubium vulgare L., a medicinal plant traditionally used for its therapeutic benefits, was investigated for its anticancer potential. This study employed a comprehensive in silico approach combining virtual screening, molecular docking, ADMET prediction, DFT calculations, and molecular dynamics simulations to evaluate the interaction of 56 phytochemicals from M. vulgare with two cancer-related targets: the androgen receptor (prostate cancer) and β -tubulin (breast cancer). Docking analysis revealed six compounds with high affinity for the androgen receptor, achieving binding scores between −11.4 and −8.3 kcal/mol. Similarly, eight compounds showed stronger interactions with β -tubulin than the standard drug paclitaxel, with binding energies ranging from −7.4 to −6.1 kcal/mol. Among these, compounds L31, L39, L43, and L46 displayed favorable toxicity profiles, with no predicted toxic effects. Notably, molecular dynamics simulations confirmed the stability of L31 as a potent androgen receptor inhibitor, while L39 and L46 showed promising interactions with β -tubulin, and DFT calculations provided additional insights into the electronic properties of the lead compounds. Overall, this study highlights the promising anticancer potential of phytoconstituents from Moroccan M. vulgare as candidates for further in vitro and in vivo evaluation.
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A linear model for LNG transport via existing pipeline and re-gasification terminals

August 25, 2025

Majed M. A. Munasser, Sabla Y. Alnouri, Abdelbaki Benamor

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This paper presents a linear programming (LP) model for optimizing the transportation of liquefied natural gas (LNG) through existing regasification terminals and Europe’s cross-border pipeline network. While pipelines offer fixed, efficient, and direct transport over land, LNG shipping provides flexibility and scalability, particularly for regions lacking pipeline connectivity. The model is demonstrated through five different case studies. Each case involves the identification of optimal LNG supply strategies from Qatar to the European Union under Russian gas disruption scenarios of 25 %, 50 %, and 75 % (equivalent to 34, 68, and 102 MTPA, respectively). The results show that the model prioritizes deliveries to destinations with available regasification capacity and low transport costs, with key importers including Turkey, Italy, Spain, and northern corridor countries. Annual network costs range from $8.1–8.8 billion for a 25 % disruption to $21–24 billion for a 75 % disruption, with liquefaction as the largest cost component. Strategic supply routes via southern, northern, and Baltic corridors were identified to enhance EU energy security, though fully replacing Russian gas would require contract adjustments and infrastructure agreements within the EU. The findings highlight the importance of integrating LNG shipping with pipeline capacity and fostering partnerships with intermediary countries.
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Intelligence predictions: enhancing bubble column efficiency with advanced machine learning (ML) approaches

August 1, 2025

Chonglin Fan, Qing Hu

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In chemical, petrochemical, and biochemical sectors, bubble columns are essential for facilitating critical reactions like methanation, oxidation, and hydrogenation. Gas hold-up is one of the most important operational parameters that has a significant impact on the performance and efficiency of these multiphase reactions. In order to improve the design and operational efficiency of bubble columns, it is imperative that the gas hold-up be precisely estimated. Creating a sophisticated predictive model with machine learning techniques is a promising solution for obtaining accurate and dependable estimations because of the intricate relationships and the unpredictable impact of numerous factors on gas hold-up. Therefore, this study explores the efficiency of four deep learning models, including the Recurrent Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit in gas hold-up estimation. This study utilizes the Harris Hawk Optimization algorithm for hyperparameter fine-tuning and enhancing the model’s performance. The optimized Recurrent Neural Network model achieves markable results, attaining a test correlation coefficient of 0.995, root mean square error of 0.005 alongside a mean absolute error of 0.0054. Additionally, this study demonstrate that liquid height is the most influential variable in controlling gas hold-up by sensitivity indices approaching 1, which suggests that even slight variations in liquid height can lead to significant changes in gas retention.
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Utilizing a Naïve Bayes model for gasification process prediction: enhancing efficiency and sustainability

July 25, 2025

Meixiang Wang

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The presented study introduces an advanced predictive modeling framework designed to predict CH 4 and C 2 H n yields in the gasification process, in which a method is traditionally employed to convert carbonaceous feedstocks into industrially valuable gases. Accurate prediction of gas yields is essential for improving operational efficiency and promoting environmental sustainability. The proposed approach seeks to advance gasification through machine learning models that suggest improved efficiency, cost-effectiveness, and precision. Such models support real-time decision-making and adaptability across diverse operational conditions, while also elevating safety by minimizing dependencies on conventional experimentation and expanding the scope of controllable parameters. The Naïve Bayes algorithm is employed to model complex relationships between influential input variables and gas production outcomes. To further boost predictive accuracy, the NB model is integrated with two metaheuristic optimizers, comprising the Weevil Damage Optimization Algorithm (WDOA) and the Crystal Structure Algorithm (CSA). These integrated approaches significantly elevate model precision and facilitate process optimization. Among the developed models, the CSA-enhanced version (NBCS) delivered the maximum performance and achieved a training R 2 of 0.981 for CH 4 and 0.976 for C 2 H n . More importantly, test R 2 values of 0.978 for CH 4 and 0.955 for C 2 H n confirmed the NBCS model’s strong generalization capability, outperforming the baseline NB model, which yielded test R 2 values of 0.949 and 0.955, respectively. In terms of statistical metrics, including MSE and RMSE, both NBWD and NBCS models outlined substantial improvements over the standalone NB model in predicting gasification outputs.
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Optimized photocatalytic performance of TiO₂ nanoparticles synthesized via Cymbopogon Citratus leaf extract

July 21, 2025

Sravani Sameera Vanjarana, Jyothi Thadaveni, Sravya Reddy Sandugari, Balaji Rao Are, Rakesh Kethavath, Ashok Kumar Kusuma, Chitti Babu Nalluri

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In this research work, Titanium dioxide (TiO 2 ) nanoparticles have been prepared by eco-friendly green synthesis technique using Cymbopogon Citratus leaf extract which are rarely used. The prepared particles are annealed at 450 °C and stored for further use. The citrol present within the extract acts as reducing and capping agent for nanoparticles resulting efficient and eco-friendly TiO 2 nanoparticles. The synthesized nanoparticles are characterized by using X-ray diffraction (XRD), Field emission scanning electron microscopy (FESEM), Energy Dispersion Spectroscopy (EDS) and Fourier transform infrared spectroscopy (FTIR) analysis. The XRD reveals that the particles are in rutile and anatase phase. The agglomerated particle had spherical morphology according to FESEM analysis. The photocatalytic degradation of Brilliant green dye of different concentrations for different irradiation time intervals and photo catalyst dosages under UV conditions were tested. Response surface methodology (RSM) with Central composite design was optimized by different operational parameters such as dye concentration, photocatalyst dosage, and irradiation time. The engineered nanoparticles exhibited excellent photocatalytic properties for the treatment of textile waste waters.
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Enhancing power consumption prediction in steel manufacturing with hybrid classification and optimization models

July 18, 2025

Bo Gu

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In the steel industry, the propensity of byproduct gas flow fluctuation is a critical factor in energy scheduling. The financial success of the steel industry will greatly benefit from an accurate forecast of its future tendencies. Manufacturers began to focus more on the needs of the client than on the product. Energy-intensive businesses like steel manufacturing are also affected by it. It might be difficult for mass customization firms to determine a product’s overall cost with accuracy. Moreover, energy accounts for 20 %–40 % of the expenditures associated with steel goods. Growing the selection of products may inevitably result in a loss of sustainability. This study investigates the prediction of power consumption in the steel industry by employing Linear Discriminant Analysis Classification (LDAC) and Decision Tree Classification (DTC). To improve the predictive accuracy of these foundational models, two advanced optimizers were utilized: The Electrostatic Discharge Algorithm (EDA) and the Political Optimizer (PO). These optimizers were systematically integrated with the base models to develop innovative hybrid models. Specifically, the LDAC model coupled with EDA forms the LDED framework, the LDAC model combined with PO constitutes the LDPO framework, the DTC model integrated with EDA results in the DTED framework, and the DTC model combined with PO creates the DTPO framework. The DTPO model performs exceptionally well in the Accuracy metric value at the Test section (value of 0.931), while the DTC and DTED models perform similarly well (both with a value of 0.911). In the Precision metric value at the Test section (value of 0.932), the DTPO model performs the best, and the LDED model performs the worst (value of 0.857).
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Optimization of iron (III) oxide nanoparticles production from natural waste for use in wastewater applications

June 19, 2025

Manjakuppam Malika, Munnure Rakshith Reddy, Bheesetty Surya, Manne Akash Yadav, Shriram S. Sonawane

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Research on environmentally friendly synthesis using natural waste materials has gained momentum due to the increasing demand for sustainable nanomaterials. This study proposes a green synthesis approach for Fe 2 O 3 nanoparticles using an extract from Golden Shower Flower as a natural stabilizing agent. The environmental impact of this bio-based synthesis is assessed through a Life Cycle Assessment (LCA) framework, following ISO 14040-44 standards, and is compared to conventional chemical methods. Key findings indicate that this approach significantly reduces environmental impact, including climate change potential (0.002022 kg CO 2 ), fossil depletion (3.17E-07 kg oil eq.), and human toxicity levels, making it a more sustainable alternative. However, potential environmental trade-offs exist due to the energy consumption required for extract preparation and purification. To further optimize the synthesis process, a response function model was developed using Central Composite Design (CCD), achieving a high R 2 value of 0.998, confirming strong predictive accuracy. A confirmatory experiment using optimized conditions – 0.03 M precursor concentration, 80.5 min of ultrasonication, and 550 °C calcination – resulted in the production of Fe 2 O 3 nanoparticles with an average size of approximately 15 nm. The synthesized Fe 2 O 3 nanoparticles exhibited excellent performance in wastewater treatment, offering a dual environmental benefit by reducing waste while improving water purification solutions. The uniform particle size distribution and magnetic properties reinforce the potential of waste-derived nanomaterials in driving sustainable nanotechnology. Additionally, they emphasize the importance of scalability, process optimization, and industrial integration in realizing a circular economy-driven approach.
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Dehydration of lean gas from carbon black production technology: process design scheme and techno-economic assessment

June 19, 2025

Ramesh Paduthalam, Suresh Krishnan

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In pyrolysis, liquid hydrocarbons are partially combusted with air, followed by water quenching, which produces carbon-black particle aggregation and lean gas. The lean gas consists of H 2 , CO, CO 2 , and C 2 H 4 constituents, and dominant water vapor and nitrogen contents. The study synthesizes process design schemes based on dew point techniques to dehydrate lean gas economically and calculates the process’s payback period. A chart was prepared to determine the dew point temperature of gas mixtures at different operating pressures and water vapor compositions. Two process design schemes were proposed to achieve up to 98 % dehydration of lean gas. The Techno-Economic Analysis (TEA) was employed to assess the economic viability of various process schemes by estimating capital expenditures (CAPEX), operational expenditures (OPEX), and the payback period. The process is capable of producing dry gas (fuel gas) at a cost as low as $0.20 per kilogram. Overall, the process design scheme offers an environmentally friendly, sustainable, and cost-effective solution for dehydrating wet lean gas, making it suitable for long-distance pipeline transportation.
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Empirical modelling of physicochemical properties of carbon capture solvent-aqueous 2-amino-2-methyl-1-propanol (AMP)

June 18, 2025

Soundharya Balasubramanian, Shree Vidhya Ramamoorthy, Ambedkar Balraj, Srinivas Vetriselvan

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This study delves into the physicochemical properties of aqueous AMP solutions, examining how varying concentrations and temperatures affect their behavior. Understanding these properties is crucial for optimizing AMP’s use in various industrial applications, from pharmaceuticals to food processing. Our findings reveal that density ( ρ ) decreases from 1.095 g/ml at 10 wt% and 283 K to 1.014 g/ml at 50 wt% and 333 K, showcasing significant thermal expansion effects. We also observed that the excess molar volume contracts, particularly at higher concentrations, indicating stronger solute–solvent interactions. Viscosity ( μ ) increases from 1.71 mPa s at 10 wt% and 283 K to 6.78 mPa s at 50 wt% and 283 K, while it decreases with temperature, highlighting how molecular motion enhances flow. Additionally, surface tension ( σ ) drops from 46.95 mN/m at 10 wt% and 283 K to 37.27 mN/m at 50 wt% and 333 K, reflecting the disruption of cohesive forces among water molecules. Our statistical analysis confirms the importance of concentration in shaping these properties, with high R 2 values ( ρ = 96.67 %, μ = 95.72 %, σ = 86.19 %) and parity charts demonstrating impressive model accuracy, exceeding 99 % for density and 95 % for viscosity.
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Collimated infrared transceiver for identifying bubble and slug regimes

May 29, 2025

Sowndarya Kesavan, Lisha Santhanasekar, Srinivasan Packirisami, Venkatesan Muniyandi

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Two-phase flow regime identification is crucial for designing and ensuring the safe operation of boilers, condensers, evaporators, and oil transportation pipelines. Infrared (IR) transceivers offer a cost-effective two-phase flow characterization approach. The divergence of emitted IR rays in a conical volume limits their sensitivity. This study addresses this limitation by introducing a bi-convex lens to collimate the IR irradiation, focusing on a specific flow cross-section. Experiments are done in a 3 mm diameter glass tube where micro-bubble, bubble plume, and slug flow regimes occur. The regimes using collimated IR transceivers are identified, and the comparison is made with conventional non-collimated systems. The results indicate that the collimated IR transceiver system has superior identification characteristics than the non-collimated system for specified flow regimes. The lens-based approach may be valuable for identifying and reconstructing two phase flow regimes.
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Research on the connection between sustainable rural economic growth and the usage of chemicals in rural areas

April 16, 2025

Yin Xiang, Yamei Lv

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With the growing demand for agricultural productivity worldwide, the extensive use of pesticides in rural areas has emerged as a significant strategy for raising crop yields. However, excessive chemical use has resulted in a number of social and environmental issues, including water pollution, soil erosion, and health hazards, all of which have an impact on the long-term viability of rural economies. Through theoretical analysis and field investigation, this work methodically investigates the intricate relationship between the use of chemicals in rural areas and sustainable economic growth. The study’s findings demonstrate that while chemicals increase agricultural productivity temporarily, their prolonged usage weakens the services provided by rural ecosystems, which has a detrimental effect on economic growth.
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Intelligent analysis of laboratory equipment testing data based on BERT

March 31, 2025

Yanbin Zhang, Dan Liu, Yang Su, Limin Du, Xiaoyu Ge, Xinsheng Gong, Jinshun Zhou

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Review of laboratory test data is a management focus for compliance laboratories. Using BERT-based intelligent analysis to analyze the laboratory instrument test data can assist laboratory testing personnel to quickly and accurately obtain the target data. This paper, with high-performance liquid chromatography (HPLC) as an example, applies big data technology and machine learning algorithm to the intelligent analysis and application of instrument test data by reading the output files in combination with the historical data of laboratory management system and business system, to realize the automatic acquisition of instrument test data, which reduces the laboratory labor cost, improves the laboratory test efficiency, optimizes the laboratory test management, and improves the quality control level.
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Design and performance study of acoustic packaging materials for biomimetic coupled sound absorption structure in automobiles

March 10, 2025

Wei Ye, Yunhai Ma

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This article studies the application of biomimetic triangular non-smooth surface materials in automotive acoustic packaging and their effects on sound absorption and insulation performance. Firstly, the application of Statistical Energy Analysis (SEA) in predicting high-frequency vibration noise in automobiles was discussed. Due to the limitations of the acoustic finite element method in predicting high-frequency noise, the SEA method has gradually been widely applied and continuously improved in domestic and foreign research. This article establishes SEA models for the front panel and floor. It analyzes the performance of different acoustic packaging materials through simulation, focusing on the sound absorption and insulation effects of biomimetic non-smooth surface materials. The experimental results show that the acoustic packaging material with a three-layer structure has excellent sound absorption and insulation performance in the high-frequency range. Specifically, the acoustic packaging of the cowl panel and floor adopts a three-layer material structure composed of organic fiber, molded felt, polyurethane foam and sound insulation felt. The simulation results show that the PU foam material with a bionic triangular wedge surface has a sound absorption coefficient of 0.983 at 1,000 Hz, significantly better than the traditional smooth surface material. In addition, the sound insulation performance of the front panel and floor increased by 2.9 dB and 48.1 dB, respectively, at 5,000 Hz.
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Chemical conversion of rural biomass waste: effective resource utilization and rural economic development

January 21, 2025

Xiang Yin

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The rapid progress of Artificial Intelligence (AI) offers promising solutions to address the challenges of building a sustainable and environmentally conscious industry, a critical step towards achieving an ecological civilization. Environmental financing drives a low-carbon, circular, and eco-friendly economy, facilitating industrial growth and societal advancement. However, the precise relationship between environmental funding and the development of the green industry has not been thoroughly explored. This research analyses the coordinated development between environmental financing and the green industry, specifically focusing on China’s Yangtze River Economic Region. A comprehensive evaluation model was constructed utilizing data from 11 provinces and autonomous regions, combining cluster finance and economic efficiency models. The findings reveal increasing financial groups across different provinces and cities along the Yangtze River in the economic zone, accompanied by a convergence of urban ecological and economic efficiency. This paper proposes measures to enhance the ecological and financial efficiency of the Yangtze River economic zone, including strengthening regional financial and economic cooperation and promoting the construction of an ecological civilization.
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Comparative analysis of heat transfer enhancement in vortex generators using nanofluids under turbulent flow conditions

July 22, 2025

Yanfang Yu, Mengqiong Zhang, Huibo Meng, Zhiying Han, Junni Wang

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To address industrial challenges in thermal efficiency equipment, current research emphasizes optimizing system designs and employing efficient heat transfer fluids. In this paper, the turbulent heat transfer performance of nanofluids in a TG-type vortex generator is investigated by numerical simulations using the mixture model at Re = 2,600–17,000 operating conditions. Gap-Diameter ratio ( G /D) are investigated in order to select a vortex generator structure with the optimal performance (Best performance when G /D = 1.2). Then the impact of nanofluid type, concentration, and CNT diameter on heat transfer was quantified. It was found that the Nusselt number of Vortex generators was by 1.75–2.44 times higher than that of the empty tube. The nanofluids Al 2 O 3 –H 2 O, SiC–H 2 O and CNT–H 2 O achieved Nu avg increases of 1.04–1.17, 1.30–1.46 and 5.36–5.98 times over water, respectively. By increasing the Fc and suppressing the S T , CNT can reduce total irreversible losses while achieving efficient heat transfer. In addition, the comprehensive heat transfer performance of single-walled carbon nanotubes (SWCNT) is better than that of multi-walled carbon nanotubes (MWCNT). MWCNT = 50 nm, MWCNT < 10 nm and SWCNT exhibited PEC enhancements of 4.03–5.07, 4.48–5.70, and 6.92–9.07 times over water, respectively. This study provides valuable insight into the optimization of heat exchangers and other industrial thermal systems.
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Prediction of CO₂ mole fraction via CO₂CPU process using different machine learning algorithms

August 14, 2025

Somayeh Mansouri, Farhad Shahraki, Jafar Sadeghi, Esmaeil Koohestanian, Mohammad Reza Sardashti Birjandi

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This research discusses the application of decision trees, Adaboosting, random forests, machines that support vectors, and k-nearest neighbor classifiers and gradient boosting in predicting CO 2 ’s mole fraction from flue gases of oxyfuel’s combustion emitted from the power plant. First of all, a training and test dataset was developed using the different variables. Then, a total of 491 simulations were performed, and the mole fraction of CO 2 was examined. Important features were detected by SHAP. Results showed that the RF algorithm enjoyed a great CO 2 mole fraction ability to predict and displayed the very best ability for generalization and most reliable prediction precision among all four, with an accuracy of 97 %. After that, LIME was used to explain the results of the RF algorithm. Out of the various variables studied, the pressure of the multistage compressor had the highest effect on the CO 2 mole fraction.
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Using different numbers of helical obstacles for promoting heat transfer in the tubes

August 21, 2025

Kawthar A. AL-Shami, Mohsen H. Fagr

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This study investigated the enhancement of heat transfer in turbulent air flow within a horizontal circular tube containing helical obstacles by conducting a detailed numerical analysis. Various parameters were explored, including number of helical obstacles; 1, 2, 3 and 4, helix ratio ( y = H/D); 2, 3 and 4. Air was selected as the working fluid with Reynolds number range of 10,000 ≤ Re ≤ 60,000 to assess performance under varying flow velocities under constant heat flux along the tube surface. The cases were designed and solved using ANSYS Fluent, and k- ω turbulence model was used. The achieving results showed increasing in Nusselt number 82 % over that of plain tube. However, this was accompanied by a content raise in the friction factor. Despite this increased flow resistance, all the obtained thermal performance factors from the analyzed scenarios were greater than unity, with the highest being 1.56 achieved at n and y of 4 and 2 respectively. The configuration of four helical obstacles, n = 4, and helix ratio y = 2 showed the highest thermal enhancement at Re = 10,000. These results indicated that multiple helical obstacles, especially in configurations with lower helix ratios, significantly boosted heat transfer efficiency.
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Construction and application of degenerate solutions of nonlinear wave equations

November 3, 2025

Dongfang Li

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Nonlinear wave equations (NWEs) enjoy widespread applications in many fields, and their solutions are usually comparatively complicated to obtain. Nevertheless, degenerate solutions could reflect the inherent laws as special solutions with degenerate properties only under certain conditions. Consequently, this paper discusses the problems in constructing degenerate solutions to nonlinear wave equations, along with their applications. In this paper, the problem of constructing degenerate solutions to the NWE was numerically studied using the finite difference method. The KdV model was adopted, along with the establishment of appropriate initial and boundary conditions. Space and time in the equation can be discretized into a calculative difference equation by applying the finite difference method. Error analysis and verification of Von Neumann stability were performed to analyze the stability and propagation characteristics of the degenerate solution. The results of error analysis show that with the reduction of the time step from 0.01 s to 0.001 s and the space step from 0.1  m to 0.01 m, the root mean square error is 0.0005. Through in-depth research on the construction of degenerate solutions, this paper opens new vistas for theoretical development regarding the NWE. It provides theoretical support for numerical simulations and experimental design in related application fields.
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Thermodynamics modeling of heat and mass transfer dynamics in drying of prekese pod: quality retention

August 1, 2025

James C. Ehiem, Promise C. Chijoke, Nneoma E. Obasi, Ndubisi A. Aviara

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This study aims to evaluate the drying kinetics, model the heat and mass transfer processes, and optimize drying conditions for prekese pods to preserve their nutritional and phytochemical qualities. Laboratory experiments conducted at 45 °C, 60 °C, and 70 °C revealed that the drying behavior closely follows the Pages and Midilli-Kucuk models, with R 2 values exceeding 0.99. Effective diffusivity (Deff) ranged from 5.00 to 6.25 × 10 −8  m 2 /s, and activation energy (Ea) was calculated at 0.95 kJ/mol. Energy consumption varied across temperatures, recorded as 227,328.6 J at 45 °C, 272,413.2 J at 60 °C, and 260,307.1 J at 70 °C, with corresponding specific energy consumption (QSEC) values of 9,605.3 J/kg, 11,510.26 J/kg, and 10,998.74 J/kg. Thermodynamic analysis based on Gibbs free energy indicates that optimal drying conditions can enhance product quality while minimizing energy use. The findings provide a foundation for designing efficient drying protocols that maintain the nutritional and phytochemical integrity of prekese pods .
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Optimizing oil and gas production forecasting using the subtraction-average-based optimizer

November 14, 2025

Tieming Zhang, Lihong Zhao

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The application of machine learning algorithms in forecasting oil and gas production rates is critical for optimizing resource extraction and improving operational efficiency. Accurate predictions of future production rates enable operators to maximize yields, minimize downtime, and optimize resource management. This study proposes a new evolutionary Subtraction-Average-Based Optimizer (SABO), which updates searchers’ positions in the search space using the subtraction average of searcher agents. The SABO algorithm is integrated into each machine learning model such as Decision Trees, Random Forest, Extra Trees, Adaptive Boosting, Bagging, and Categorical Boosting, for performance improvement, and the resultant hybrid models are compared and examined using the test cases of the benchmark functions and the real-world dataset. The best performance belonged to the highest R 2 of 0.9327 and lowest RMSE of 2.0785 and MAE of 0.4665 for SABO-Bagging in the training set, and the test dataset recorded the best performance by the R 2 of 0.9267 and lowest RMSE of 2.2157 and MAE of 0.4558. These findings indicate that the SABO-Bagging hybrid model is the best performing model for the prediction of oil and gas production rates, and it has a competitive advantage compared to other machine learning methods for operational optimization in the energy industry.
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Enhancing solar dryer performance through computational fluid dynamics in forced convection conditions

May 6, 2025

Bhirendra Kumar, Sewan Das Patle, Satish Kumar Dewangan

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Solar drying technology has emerged as a pivotal contributor to sustainability in agriculture and various industrial sectors by harnessing solar energy for energy-efficient product dehydration. This research paper explores the effectiveness of Computational Fluid Dynamics (CFD) simulations in enhancing the design and operation of solar dryers, with a particular focus on scenarios involving forced convection. Solar dryers find versatile applications in agriculture, food processing, and industrial operations, propelling the expansion of research in this field. The core objective of this study is to computationally predict critical parameters within solar dryers operating under forced convection, including temperature distribution, velocity distribution, heat transfer coefficient variations, and humidity distribution. By leveraging CFD, this research seeks to shed light on the specific impacts of key variables, notably the solar dryer’s aspect ratio and the application of forced convection. The ultimate goal is to determine the optimal combination of these variables to maximize solar dryer performance. The findings of this study are expected to advance our understanding of how CFD simulations can be employed to enhance energy efficiency and product quality in solar drying processes. Furthermore, this research contributes to the ongoing efforts to develop sustainable and eco-friendly solutions for product dehydration in agriculture and industry, aligning with the global pursuit of a greener and more efficient future.
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Multi-objective optimization of temperature profile using reinforcement learning in batch crystallization process

July 31, 2025

Suneet Dhanasekaran, Karthika Shanmugam, Saravanathamizhan Ramanujam

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The temperature profile is a critical parameter in industrial batch cooling crystallization processes which directly impacts the crystal size distribution (CSD) and subsequent down streaming operations. Conventional cooling approaches, including natural and linear cooling, often fail to achieve optimal results. Linear cooling may trigger excessive primary nucleation by crossing metastable limits, while natural cooling leads to inconsistent nucleation and growth, resulting in smaller and non-uniform crystals. Hence, optimized cooling profiles are used to achieve the desired target CSD. This study employs reinforcement learning (RL) to optimize the temperature profile for batch cooling crystallization for two systems: paracetamol in water and potassium dihydrogen phosphate (KDP) in aqueous solution. RL dynamically adjusts the cooling trajectory by iteratively interacting with a process model, aiming to maximize mean crystal size while minimizing the coefficient of variation. A comprehensive mathematical model incorporating population balance equations, mass balance, and energy balance is developed to simulate the crystallization process. For the paracetamol system, the RL-optimization strategy resulted in a 107.5 % increase in mean crystal size compared to the natural cooling profile. In the case of the KDP system, a 12 % increase in mean crystal length is achieved relative to a linear cooling profile, along with a significant reduction in the coefficient of variation, indicating improved crystal size uniformity. The optimization results obtained using RL are also compared with that from a genetic algorithm for both cases, and RL demonstrated superior performance. This work underscores the potential of RL in advancing broader applications in chemical process optimization.
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In Silico target identification and drug discovery for 5-proFAR isomerase inhibitors in critical fungal pathogens

October 20, 2025

Hemantha Mani Kumar Chakravarthi Chanda, Hari Priya Narra, Sudheer Kumar Katari

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Fungal infections, especially in immunocompromised individuals, are emerging as a significant global health threat, contributing to high morbidity and mortality rates. The growing resistance of pathogens like Cryptococcus neoformans , Aspergillus fumigatus , Candida albicans and Candida auris coupled with limited treatment options, underscores the urgent need for improved diagnostics and antifungal therapies. Utilizing a comprehensive comparative proteomic approach, we identified the critical role of 1-(5-phosphoribosyl)-5-[(5-phosphoribosylamino) methylideneamino] imidazole-4-carboxamide (5-proFAR) isomerase in Amino acid transport metabolism, a vital pathway for fungal growth and virulence. Through advanced in silico techniques, including molecular docking, molecular dynamics simulations (MDS), and Principal Component Analysis (PCA), we screened potential inhibitors and identified Mugineic acid and 9-amino-2-deoxy-2,3-dehydro-n-acetyl-neuraminate as the most promising candidates, displaying favorable binding affinity, stability, and strong interactions with key residues within the enzyme’s active site. These compounds exhibited low RMSD and RMSF values, indicative of high conformational stability, making them ideal candidates for further experimental validation. Our findings suggest that targeting 5-proFAR isomerase offers a broad-spectrum, resistance-evading therapeutic strategy that could significantly mitigate the global burden of fungal infections, particularly in immunocompromised individuals, thus paving the way for the development of next-generation antifungal drugs to combat the growing challenge of multidrug-resistant fungal pathogens.
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Visible emissions through upconverting nanodots of NaYF₄:Er³⁺; Yb³⁺ nanocrystals: a brief study in their upconversion mechanism and energy transfers

July 14, 2025

Monami Das Modak, Pradip Paik, Mallaiah Mekala

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Here we report the formation of upconverting nanodots of Er 3+ /Yb 3+ codoped NaYF 4 nanocrystals confining diameter as calculated to be 3.4 ± 0.15 nm and are evolved as single crystalline particles with monodisperse particle size distribution. These nanodots are obtained with appearance of major cubic phases and minor prehoxagonal phases via thermal decomposition heat treatment. The bright green visible emissions for 2H 11/2 –4I 15/2 /4S 3/2 –4I 15/2 energy transfers resulting from a three-photon excitation process, are obtained under 980 nm laser diode excitation sources, employed with an inexpensive diode laser with a maximum power density of about 150 mW/cm 2 . The synthesized nanodots are well characterized by high resolution transmission electron microscopy (HRTEM), X-ray diffraction study (XRD), Raman spectroscopic analysis, fluorescence spectroscopy for confirming their sizes, shapes; crystallinity; phases present and emissions respectively. Additionally, we also report the vis-vis photoluminescence (PL-study) under 450 nm excitations followed by two-photon excitation process. These nanocrystals are efficient enough to be dispersed in nonpolar organic solvents and could be of a great potential candidate for using as imaging probes or in fluorescent labels.
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Multi-aspect evaluation of integrated ethane liquefaction processes: economic, energy, and thermodynamic analysis for transport applications

August 29, 2025

Nguyen Van Nguyen, Vahid Pirouzfar

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Ethane liquefaction processes vary widely in energy consumption, capital investment, and technical complexity, especially in low-capacity production. The refrigeration cycles, equipment arrangement, and liquefaction facilities are important factors influencing the efficiency and performance of the technology. In this study, a case study is performed to evaluate several ethane liquefaction processes and to control the related energy consumption levels while reducing capital investment. To this end, six methods and technologies are proposed to produce liquefied ethane as case studies. Evaluation of the technical conditions, thermodynamic analysis, and cost estimation was done using Aspen HYSYS, Refprop, and Aspen Capital Cost Estimator software, respectively. Based on the results of the case studies, it can be concluded that Cases 4 and 6 are more desirable than the others due to lower capital costs and reduced energy and operating costs. In Case 4, it should be noted that cooling and liquefaction down to −34 °C are achieved at pressures above 9.5 bar. The results indicate that the estimated Total Annualized Cost (TAC) for Cases 1 to 6 are 4,375,639; 4,403,845; 4,738,296; 4,629,148; 3,467,939; and 4,124,547 USD, respectively. Consequently, among the available cases, Case 6 is the best option for the liquefaction process due to its lower cost (126 USD/T). This case operates with a propane refrigeration cycle and three main cold boxes to execute the liquefaction process.
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Digital twins implementations in the chemical industry with a focus on soda ash production: a literature review

November 3, 2025

Sławomir Szczeblewski, Jacek Gębicki

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The implementation of digital twins, which serve as digital representations of physical industrial installations and their associated industrial processes, is a rapidly evolving technology aligned with the core principles of Industry 4.0 and 5.0. A digital industrial installation enables the collection of data from the real environment for the purpose of reproducing, validating, and simulating both the current and predictive behavior of physical production systems. The aim of this systematic literature review is to provide a comprehensive overview of digital twin technology in the context of the challenges and limitations associated with its implementation in the chemical industry. By employing the method of systematic literature review, the study identified areas of real implementations in the chemical industry, describing both the software applied and the types of processes optimized through the use of digital twins. The findings demonstrate that digital twins represent an innovation within the chemical domain and constitute dynamically evolving methods for process analysis.
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Harnessing nature and algorithms for green energy: the electrochemical property prediction of activated bamboo using data mining and metaheuristic tuning techniques

November 7, 2025

Chao Pan, Yanshu Liu

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The application of Machine Learning (ML) models coupled with metaheuristic optimization algorithms represents a potentially powerful development in the field of predictive modeling, as it relates to sustainable energy materials. In this study, the electrochemical performance of biomass material from bamboo for energy storage applications is explored, focusing on the prediction of power density. The central objective is to enhance model accuracy with a novel hybrid model of Kernel Extreme Learning Machine (KELM) and Slime Mould Algorithm (SMA), Sunflower Optimization (SFO), and Social Ski Driver (SSD). The optimal predictive performance was achieved with KELM-SFO with a test Root Mean Square Error (RMSE) of 10,421.05, Mean Absolute Error (MAE) of 4,654.07, and R-squared (R 2 ) of 96.2 %. Early and fast plateauing of the SFO algorithm’s convergence curve indicated stable, early-stage optimization. In addition to filling a significant knowledge gap in ML-incorporated materials modeling, this work opens the door for future research on deep learning techniques, adaptive hybrid optimization algorithms, and real-time experimental validations to improve the electrochemical prediction efficiency in energy storage systems inspired by biomass.
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A hybrid intelligent framework for energy consumption classification of mechanical equipment in the steel industry based on machine learning and metaheuristic optimization

December 1, 2025

Daming Liu

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This study presents a robust intelligent hybrid classification model to effectively classify energy consumption levels in the steel industry, thereby improving productivity and contributing to lower energy costs. By evaluating production variables, operational variables, and historical energy use data, the model produces a deep understanding of energy consumption patterns and a framework to make better decisions in achieving energy savings. The proposed approach highlights the combination of Quadratic Discriminant Analysis (QDA) and Decision Tree Classifier (DTC) classifiers with two advanced metaheuristic algorithms, Greylag Goose Optimization (GGO), and Manta Ray Foraging Optimization (MRFO) to optimise the parameters in respective classifiers and enhance the predictive ability of the classification system. The study employed a dataset of 10,000 samples containing nine inputs and one output, across 200 iterations. Experiments demonstrated that the hybrid DTC-GGO (DTGG) outperformed all the other finalist comparison systems with an overall training accuracy of 97.7 %. The lowest training accuracy with the QDA configuration was only 85.4 %. Compared to baseline models, the hybrid models yielded improvements of up to 12.3 % in classification accuracy and reduced misclassification rates. The research demonstrates several contributions including a novel intepretive hybrid framework, combining advanced machine learning classification with bio-inspired optimisation, demonstrates superior classification and computational efficiency, assesses the possibility of the ML model model in a cloud-based environment for smart grid integration, assesses the ability to train and develop deployments models in the future, and can be scaled to a broader industrial context. This integrated strategy offers a practical, high-performance solution to real-time energy management, supporting the transition toward more intelligent and sustainable industrial operations.
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Adsorptive remediation of bromocresol green dye in an aqueous solution using acid activated carbon adsorbent

October 10, 2025

Minar Mohamed Lebbai, Mullai Venthan, Muhammed Ameen, Naren Mahalingam, Pandurangarao Kolusu, Susmita Das, Bhuvaneshwari Soundarajan

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In this study, sawdust-derived activated carbon (SDAC) was synthesized from rubberwood sawdust through phosphoric acid activation followed by carbonization. Characterization of SDAC before and after adsorption, using various analytical techniques, confirmed considerable changes on activated carbon’s surface. Batch adsorption studies were performed to evaluate the removal efficiency of bromocresol green (BCG) dye under varying conditions of initial BCG concentration (25–100 mg/L), SDAC dosage (0.25–1.0 g/L) and adsorption time (0–60 min). The results revealed that SDAC achieved a high removal efficiency of 99.7 % and a maximum capacity of 83.65 mg/g. The experimental data closely fit the Langmuir isotherm model, with a theoretical maximum capacity of 85.47 mg/g, and followed pseudo-second-order kinetic model, indicating a chemical adsorption process. These results confirm SDAC as a highly effective and sustainable adsorbent for dye remediation in aqueous environments.
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Fuzzy TOPSIS optimization of heat transfer rate of magnetized radiative Casson nanofluid over a stretching sheet

July 17, 2025

Shanmugapriya Marayanagaraj, Sundareswaran Raman, Augustine Wisely Bezalel, Abirami Thirupathy

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Nanofluids provide an innovative solution for heat transfer and energy efficiency, making them highly beneficial across various industries. Their superior thermal conductivity, improved heat dissipation, and optimized energy efficiency makes them well-suited for applications in automotive cooling, industrial heat exchangers, electronics, renewable energy systems, hyperthermia cancer treatment, and targeted drug delivery. This study aims to determine a numerical solution for the flow of a magnetized radiative Casson nanofluid (CNF) composed of Aluminium Oxide (Al 2 O 3 )/Ethylene Glycol (C 2 H 6 O 2 ) and Copper oxide (CuO)/Ethylene Glycol (C 2 H 6 O 2 ) over an unsteady stretching, considering its wide range of applications. The objective of this work is to formulate a mathematical model of the physical flow problem and numerically solve the governing equations using shooting method via MATLAB. Furthermore, the most influential parameter for optimizing the rate of heat transfer is identified using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The outcome of the influencing dimensionless parameters like unsteadiness parameter (S), stretching parameter (C), magnetic parameter (M), radiation parameter (Rd), Prandtl number (Pr), Eckert number (Ec) and heat generation parameter (He) are discussed via contour plots and tables. According to the study, an increase in radiation parameter (Rd), and heat generation parameter (He) result in increased temperatures. Additionally, the increase in the heat transfer coefficient is greater in CuO/C 2 H 6 O 2 nanofluid compared to Al 2 O 3 /C 2 H 6 O 2 nanofluid. Furthermore, Fuzzy TOPSIS analysis indicates that alternative A 20 represents the optimal best combination of physical parameters for enhancing the heat transfer rate for both Al 2 O 3 /C 2 H 6 O 2 and CuO/C 2 H 6 O 2 nanofluids.
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Advanced prediction of CO₂ emissions and energy consumption in high-performance concrete manufacturing

July 22, 2025

Xianghua Zhang, Yajuan Li, Bingjie Wang

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This study addresses the urgent need to reduce environmental impacts in the construction industry by improving the prediction of CO 2 emissions and energy consumption during the production of High-Performance Concrete (HPC). To tackle this challenge, we employed three machine learning (ML) regression models: K-Nearest Neighbor Regression (KNNR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting Regression (XGBR) to examine the complex patterns influencing sustainability metrics in HPC manufacturing. Each model was further enhanced using the Fitness Dependent Optimizer (FDO) to increase predictive accuracy and decrease error rates. We evaluated model performance through training, validation, and testing phases, focusing on two objectives: CO 2 emissions and energy usage. Among all the approaches tested, the KNNR and its hybrid version (KNFD) achieved the lowest Weighted Absolute Percentage Error (WAPE) of 0.002 for the energy prediction task. The XGBR and XGFD models followed close behind with WAPEs of 0.05 and 0.04, respectively, outperforming the GBR and GBFD models. Feature selection based on the F-statistic identified “cement” as the most crucial variable among the eight input features. These findings demonstrate the effectiveness of FDO-enhanced regression models in delivering accurate, low-error predictions, which are essential for sustainability-oriented initiatives in construction. This research establishes a solid foundation for data-driven strategies aimed at reducing environmental impacts in HPC production.
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Influence of surface properties and concentration of NiO nanoparticles towards the decoloration of textile dye reactive black 5

July 17, 2025

Ramesh Martha, Venugopal Rao Bakshi, Ravi Chander Maroju, Monami Das Modak, Santhosh Kumar Adpa, Kumara Swamy Gullapelli

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NiO nanoparticles (NPs) were synthesized at various quantities via a thermal decomposition route and analyzed using topographical and optical properties. The average size of NiO NPs is estimated to be between 100 nm and 50 nm, with an average surface roughness of 5.42 nm ± 12.62 nm. Image J analysis suggests that the regularity ratio (R) is between 0.12 % and 0.35 %. NiO NPs with a Ni concentration of 1 mM were able to eliminate 83 % of Reactive Black five during 60 min. Furthermore, when NiO NPs were synthesized at a temperature of 200 °C and at a higher concentration (1 M), they demonstrated excellent adsorption towards the decolorization of RB-5 (RB-5) and obeys pseudo zero order kinetics with values of −0.0106 h −1 , −0.0095 h −1 and −0.0021 h −1 .
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Techno-economic assessment of medical oxygen production: cryogenic vs. PSA technologies

November 5, 2025

Lina Benkirane, Tarik Chafik

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This work presents a techno-economic comparison of medical oxygen production using cryogenic air separation and pressure swing adsorption technology. Process simulations were performed with Aspen Plus and Aspen Adsorption, and cost estimation was carried out using Aspen Process Economic Analyzer. For a production capacity of 1,000 t/year, the cryogenic process required a capital investment of 15.67 million USD and annual operating costs of 5.12 million USD, yielding an annual cash flow of 4.88 million USD, a payback period of 3.21 years, a net present value of 2.84 million USD, and a return on investment of 155.9 %. The PSA unit, designed for 500 t/year, required a CAPEX of 9.07 million USD and an OPEX of 2.05 million USD/year, achieving an annual cash flow of 7.94 million USD, a payback of 1.14 years, an NPV of 21.05 million USD, and an ROI of 437.87 %. Sensitivity analysis confirmed the strong dependence of cryogenic plants on electricity prices, while PSA exhibited high resilience to cost fluctuations. Overall, PSA offers superior economic feasibility for decentralized oxygen supply, whereas cryogenic separation remains advantageous for large-scale, high-purity production.
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Biodiesel production from waste cooking oil using Indion-140 catalyst: optimization studies

July 31, 2025

Mallaiah Mekala, Prasad Babu Koorla, Ramesh Kola, Bhavani Pokala, Anwar Shaik, Sai Mani Yogesh Kosuru, Yashraj Delhiwala

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Heterogeneous transesterification of Waste Cooking Palm Oil (WCPO) to biodiesel over ion exchange resin solid acid catalyst, Indion-140 in a batch reactor and the optimization of the process conditions have been investigated. The reaction is conducted under various parameters like temperature in the range of 40 °C–80 °C, catalyst loading in the range of 1 g/cc – 5 g/cc based on volume of reaction mixture and mole ratios of reactants in the range of 1:1 – 1:10. The experimental design of the influencing parameters is modeled by using Central Composite Design (CCD) with Response Surface Methodology (RSM). The experiments are performed as per the combinations of influencing parameters designed by aforementioned CCD methodology. The experimental data used the power law regression in a quadratic model. An ANN was trained on the experimental data. ANOVA is used to test the regression model for biodiesel yield. The maximum biodiesel yield achieved was 86.63 % experimentally, while the model predicted a yield of 79.243 % under optimal conditions.
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Comparative analysis of polyphenols and flavonoids composition and their scavenging capacity for DPPH radicals in organic tea

November 26, 2025

YunLong Qiu

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Polyphenols and flavonoids are abundant in herbal teas and are necessary in antioxidant defense. Although the black and green teas are well researched, there is little information on herbal teas. This paper examined the polyphenol and flavonoid composition of 20 organic herbal teas and their roles in antioxidant activity, in terms of DPPH radical inhibition. The Folin-Ciocalteu colorimetry and the aluminum nitrate colorimetry were used to measure total phenolics and flavonoids, whereas the DPPH, ABTS, and hydroxyl radical scavenging assays were used to measure antioxidant activity. There were strong correlations between antioxidant activity and phenolic content (DPPH: r = 0.876; ABTS: r = 0.928; hydroxyl: r = 0.905), with the highest activity observed in dandelion, kuding, mint, peony, and lotus leaf teas. The structure-activity relationships of flavonoid were investigated by means of Cu 2 + chelation, UV-visible spectroscopy, and DNA binding. The copper-flavonoid complexes exhibited redshifts of the UV and improved DNA binding (without strand breaks) suggesting structural changes that lead to bioactivity. Finally, polyphenols are the main source of antioxidant activity of herbal teas, and flavonoids contribute to the effects through metal binding and interaction with DNA. These findings justify the creation of effective herbal teas in the management of oxidative stress.
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Use of membrane distillation for selective recovery of chemicals like ethyl alcohol: a critical review

July 17, 2025

Yogesh S. Mahajan, Harshada R. Jadhav

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Separation, purification and recovery of chemicals has become an essential attribute of process industries. Process intensifications (PI) like reactive separations (RS), membrane reactors (MR) and advanced membrane separation techniques can be effectively used for this purpose. Membrane distillation (MD) has been used in applications like desalination of sea water, brackish water treatment and brine treatment. MD can also be used for recovery of volatile, nonvolatile chemicals with greater effectiveness and better economics. This review critically adjudges the application of MD for recovery of chemicals especially ethyl alcohol (ethanol, EtOH). Technical aspects like membrane configurations, membrane materials, comparison with other membrane separations, economic aspects and industrial applications are some of the key features of this review. Interplay of parameters and membrane performance is discussed. Challenges like long term sustainability, membrane fouling, scale up, hybrid processes are reviewed in considerable detail. Directions for further research are indicated.
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Recurrent neural network based modelling and temperature control of a bioreactor for ethanol production

February 10, 2025

Sathiya Panneerselvam, Venkatesh Pathakamuri, Thota K Radhakrishnan, Kalaichelvi Ponnusamy

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A bioreactor is a complex, nonlinear, and time-varying system that requires accurate mathematical models and superior control performance. Controlling the temperature has an enormous impact on the overall performance and efficiency of ethanol production. Data-driven modelling and deep learning techniques like recurrent neural networks capture temporal characteristics. Hence, in this study, a gated recurrent unit (GRU), a type of RNN is designed for the control of temperature in the bioreactor system based on a model-based control framework. The generated input-output data from the first principles model of the bioreactor is provided for the training of GRU network model. The GRU model is tuned by adjusting hyperparameters such as number of hidden layers, hidden units, and epochs for the minimization of the prediction error. Similarly, another GRU network is trained for use as a controller. To track the test setpoints and reject disturbances with minimum error, the controller network architecture is tuned. This procedure is repeated and compared with another popular RNN known as long short-term memory (LSTM) network as well as with bi-directional long short-term memory (Bi–LSTM) network also. Bi–LSTM based model shows the minimum root mean squared error (RMSE) i.e., 0.0076 whereas LSTM and GRU models shows 0.0085 and 0.0077 respectively. The servo and regulatory response of these network controllers are evaluated in terms of a standard performance measure such as integral squared error (ISE). The servo and regulatory response of these network controllers are evaluated in terms of a standard performance measure viz., integral squared error (ISE). In the servo control, the GRU based controller provides less ISE i.e., 37.02 whereas in LSTM, Bi–LSTM and IMC–PI based controller provides 39.66, 51.10 and 50.43 respectively. Similarly, in the case of regulatory response, the GRU based controller rejects the disturbance i.e., input flow rate effectively.
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A review on the synthesis, properties, and applications of Janus nanoparticles

August 15, 2025

Rallabandi Pattabhi Ramaraju, Vissamsetti Jairam, Mohammed Saleem Farhaan, BVS Praveen, Pachimatla Rajesh, Mechiri Sandeep Kumar

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Janus nanoparticles (JNPs) are distinguished by their dual-faced structure, where each side exhibits distinct physical, chemical, or functional properties. This asymmetry enables JNPs to perform multiple roles simultaneously, making them highly versatile for drug delivery, biosensing, bioimaging, and environmental remediation applications. Synthesis methods such as masking, self-assembly, phase separation, and selective surface modification allow precise control over JNP morphology and functionality, enabling tailored properties like amphiphilic surfaces, magnetic or fluorescent domains, and hybrid compositions. Characterization tools such as SEM, TEM, and XRD mapping are crucial for understanding their structural and compositional attributes, facilitating optimization for specific applications. In biomedicine, JNPs show promise in targeted drug delivery, bioimaging, and theranostics, combining diagnostic and therapeutic capabilities. In environmental engineering, they are effective in water decontamination and removing heavy metals from contaminated water. This review provides a comprehensive overview of synthesis strategies, characterization techniques, applications, and critical analysis of JNPs, highlighting recent advancements and future directions to overcome the challenges.
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Advances in composite and polymer flame retardants – a review on mechanism, properties and applications

October 22, 2025

Challapalli Sruthi, Doddi Jai Niketan, Pydimalla Madhuri, BVS Praveen, Raj Kumar Verma, Achuvelli Venkata Raghavendra Rao, Gowrishetty Srinivas

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Flame retardants are critical additives that enhance the fire safety of polymers and composites by reducing flammability, delaying ignition, and inhibiting flame spread. They function through diverse mechanisms, including radical quenching in the gas phase, char formation in the condensed phase, endothermic heat absorption, gas dilution, and intumescence. Based on chemical composition and mode of action, flame retardants are broadly classified into organic and inorganic types, each with distinct thermal, environmental, and compatibility profiles. Synergistic systems, combining multiple additives such as phosphorus–nitrogen, boron–metal hydroxide, and nanomaterial-enhanced formulations, offer improved fire resistance at lower loadings, preserving mechanical and processing properties. Thermal and mechanical impacts are key considerations, as high additive concentrations may compromise structural performance, necessitating optimized formulations. Substrate type, processing requirements, target performance, and evolving environmental and regulatory constraints guide application-specific selection. In the construction, transportation, electronics, and textile sectors, flame retardants enable compliance with stringent safety standards while meeting durability, weight, and functional requirements. Emerging trends focus on halogen-free, bio-based, and nanotechnology-enabled systems that balance fire performance with sustainability and reduced toxicity. This review examines flame-retardant classifications, mechanisms, performance influences, and sector-specific applications, highlighting advances in sustainable materials and multifunctional systems.
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Statistical analysis of emergency response optimization for hazardous chemical processes in industrial facilities

August 1, 2025

Babu Rao C. Gnanasekara, Thangallapalli Srinivas, Rajesh Pachimatla, A. V. Raghavendra Rao, Govindarajan Rajendran, Elangovan Ramkuppan

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This research explored the state of emergency preparedness at high-risk chemical manufacturing facilities across Tamil Nadu, India. Data were gathered through on-site inspections and collaboration with plant operators, focusing on responses to 100 key questions about readiness protocols. Using advanced statistical software, we analyzed the binary responses to evaluate the effectiveness of current emergency measures and identify critical shortcomings. The investigation emphasized several vital aspects, including the robustness of fire protection systems, funding for safety initiatives, uninterrupted power for communication networks, regular equipment testing, sound system clarity, certification compliance, hazard identification (HAZID) evaluations, fire load assessments, backup assembly areas, and reliable fire door operations. Regulatory oversight highlighted the importance of maintaining certified emergency protocols, ensuring ambulance availability, and establishing workplace health facilities. Our statistical findings revealed significant deficiencies in preparedness within Tamil Nadu’s chemical industry, offering actionable insights for improvement. With a confidence interval supporting broader applicability, the study underscores the urgent need for enhanced emergency planning to elevate safety performance across the region’s chemical sector.
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Polygeneration process design to recover waste cold energy in LNG regasification terminals: simulation-based optimization approach

October 27, 2025

Hrithika Ganta, Arnab Dutta

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Liquefied Natural Gas (LNG) has emerged as a key energy vector owing to its lower carbon footprint. Calorific value specifications at regasification terminals necessitates pre-processing of LNG prior to regasification. Cryogenic distillation for heavy hydrocarbon removal (HHR) from LNG to lower the calorific value utilizes a portion of the available LNG cold energy. However, substantial amount of cold energy remains unutilized in HHR process. To reverse this wastage, a polygeneration process encompassing Organic Rankine Cycle (ORC) for power generation along with diverse refrigeration strategies is proposed following the HHR process. A simulation-based optimization framework is developed to maximize the net present value (NPV) of the proposed polygeneration process and investigate its performance across different LNG feed conditions. Irrespective of LNG feed conditions, the proposed process is found to be technoeconomically feasible resulting in a positive NPV. The optimal process not only generates electricity but also refrigeration sufficient to cool 1,200 data center racks at − 10 ${-}10$  °C and a cryogenic warehouse of 2,000 m 3  at − 50 ${-}50$  °C. The polygeneration process recovers substantial amount of the remaining cold energy after the HHR process resulting in a CO 2 avoidance of 12.8 kg-CO 2 /ton-LNG, thereby significantly augmenting the technoeconomics and sustainability of LNG value chain.
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Assessment of CO₂ methanation integration in a power to gas system

November 10, 2025

Mohamed El Yazid, Achraf El Kasmi, Tarik Chafik

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E-methane production represents a sustainable approach to valorising CO 2 captured from energy-intensive industries. In this context, the methanation process is assessed with respect to energy efficiency by utilizing locally produced green hydrogen and captured CO 2 , supporting the transition to low-carbon energy systems while addressing the intermittency of renewable energy sources. This study evaluates the transition from laboratory-scale experiments to an industrial-scale e-methane production plant, leveraging surplus renewable energy, which is inherently subject to fluctuations. The studied plant operates with a methane output of 300 kg/h, achieving a CO 2 conversion rate of 88 % at 550 °C and 30 bar. Various plant configurations, reactor designs, and performance scenarios were assessed based on available bibliographic data. CO 2 methanation was carried out using a 0.5 wt% Ru/γ-Al 2 O 3 catalyst in a plug flow reactor. A modified LHHW kinetic model was found to provide the best fit to the experimental data. A sensitivity analysis was conducted to quantify the influence of key parameters, including temperature, pressure, catalyst mass, and reactant feed ratio, on reactor efficiency. The results provide valuable insights into process optimization, evaluating the potential of this approach for future industrial-scale development.
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CFD analysis of heat transfer and friction factor characteristics of nanofluid flow in microtubes

July 14, 2025

Gandhi Marikannan, Perarasu V. Thangavelu

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Nanofluids (NFs), a new type of nano-engineered colloidal suspension, consist of solid particles at the nanoscale suspended in a base liquid. In many applications, the impact of dispersing nanoparticles (NPs) on the thermal (hydrothermal) and hydraulic performance of traditional coolants is a significant issue. The current paper reports the results of numerical research on the MWCNT (Multi Wall Carbon Nano Tube) nanofluid flowing in a microtube, developing laminar flows under constant heat flux conditions. The numerical investigations are conducted for a Reynolds number range from 200 to 500, and a particle concentrations are 0.075 %, 0.125 %, 0.25 %. The effects of the Reynolds number on the friction factor and heat transfer coefficient in the microtube are computed using OpenFOAM software and compared satisfactorily with the experimental results of the literature. In order to evaluate performance improvements, the governing equations of fluid flow and heat transfer were discretised using the finite volume method, and nanofluid thermophysical properties were added. The results shows that while increasing the MWCNT volume percentage increased the friction factor, it also greatly improved heat transmission through higher thermal conductivity. The predictive accuracy of the model was demonstrated by its error percentages of ±12 % for friction factor predictions and below ±10 % for the heat transfer coefficient. This study confirms OpenFOAM as a useful tool for simulating advanced nanofluid behaviour in constrained flows and demonstrates the potential of MWCNT nanofluids in micro-scale heat transfer systems.
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Modeling the CO and CO₂ output in the gasification process using optimized hybrid machine learning models

July 22, 2025

Dandan Song, Tao Yang

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Gasification modeling involves developing and evaluating computational and mathematical frameworks to replicate and forecast the behavior of gasification processes. Among these frameworks, the Decision Tree (DT) model is particularly popular for its capacity to handle both categorical and numerical data while maintaining interpretability. This characteristic shed light on the key factors affecting gasification outcomes. This hybrid strategy harnessed the advantages of DTs for predictive modeling, along with enhancement methods to refine the gasification process. According to the performance metrics, the DTAO hybrid model consistently excelled compared to others, recording the highest R 2 values of 0.989 (CO) and 0.985 (CO 2 ), the lowest RMSE values of 0.996 (CO) and 0.797 (CO 2 ), as well as the lowest OBJ values of 0.935 (CO) and 0.793 (CO 2 ). This confirms its exceptional accuracy and reliability in gasification prediction.
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Quantum chemical and molecular modeling approaches for repurposing anticoagulants against SARS-CoV-2 main protease

October 16, 2025

Hassan Nour, Nouh Mounadi, Bouchra Rossafi, Abdelkbir Errougui, Mohammed Bouachrine, Samir Chtita

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The SARS-CoV-2 pandemic has posed a global health emergency since 2019, driving continuous efforts to discover effective treatments. While vaccines and recommended medications exist, drug repurposing offers a cost-effective and efficient approach to identify new therapeutic options. Anticoagulants, previously used to mitigate hypercoagulability linked to SARS-CoV-2 infection, present promising candidates for repurposing as antiviral agents. This study combines molecular docking, molecular dynamics simulations, and Density Functional Theory (DFT) analysis to evaluate the inhibitory potential of six anticoagulant drugs against the SARS-CoV-2 main protease (Mpro). Molecular docking identified Warfarin and Fluindione as the most potent candidates, exhibiting binding affinities of −6.9 and −6.8 kcal/mol, respectively. Molecular dynamics simulations confirmed the stability of these drug-protease complexes in an aqueous environment, potentially disrupting viral replication. Additionally, DFT calculations provided insights into the electronic properties governing molecular reactivity and stability. Quantum descriptors, including HOMO-LUMO energy gaps, chemical potential, hardness, and electrophilicity, were evaluated to rationalize the docking interactions. The results indicate that Apixaban, characterized by the highest hardness and stability, exhibited robust electron-donating and accepting properties, while Fluindione demonstrated high electrophilicity, correlating with its reactivity. These findings offer a comprehensive computational framework for repurposing anticoagulants as SARS-CoV-2 inhibitors and warrant further experimental validation.
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The inertia effects in rheodynamic lubrication of an externally pressurized thrust bearing using Herschel-Bulkley lubricant with sinusoidal injection

August 1, 2025

Jayakaran Amalraj Isaac, Alexander Raymand Goudie, Jayakumar Jayaraman, Vidhuran Kumar Moorthy

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In challenging industrial environments, bearings must endure high-speed operations, heavy loads, and maintain high stiffness. This study investigates the synergistic impacts of fluid inertia forces, non-Newtonian behavior and sinusoidal injection of the Herschel-Bulkley lubricant in an externally pressurized circular thrust bearing. To sidestep complex computations, the momentum equation’s “inertia” term is estimated through the mean value average method across the film thickness. Additionally, a mathematical model for a Sinusoidal injection of the lubricant has been presented. Numerical computations have been conducted to determine the thickness of the core, velocity profile, film pressure, and load carrying capacity of the bearing for different values of Herschel-Bulkley number ( N ), Reynolds number (Re), Power-law index ( n ), and time, along with different amplitudes of sinusoidal lubricant injection. This study reports the influences of inertial forces and sinusoidal injection in fluid film pressure as well as the load carrying capacity owing to visco-plastic lubricants’ physical parameters at the considered bearing. These findings might be impactful to carry out the considerable heat transfer effects. Further, it will be helpful to continue the research with the converging bearing models.
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In-silico evaluation of triazole derivatives as antimicrobial agents against Pseudomonas aeruginosa

October 15, 2025

Ghizlan Maymoun, Hsaine Zgou, Meriem Khedraoui, Hind Lafridi, Abderahman Sabour, Adnane Hakem, Brahim El Houate, Samir Chtita

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The continuous resistance of Pseudomonas aeruginosa to conventional antibiotics due to biofilm formation necessitates the development of potent alternatives. In this study, the antibacterial activity of 21 triazole derivatives was evaluated through molecular docking against the lasR protein of P. aeruginosa (PDB ID: 3JPU). Among them, compounds G, N, and U exhibited high binding affinity. Further ADMET analysis identified compound N as the most promising candidate due to its favorable pharmacokinetic and pharmacodynamic properties, as well as its compliance with Lipinski’s rule. Molecular dynamics simulations confirmed its stability within the active site, while density functional theory (DFT) calculations, including molecular electrostatic potential (MEP), highlighted the triazole ring and amine group as key interaction sites. Additionally, the frontier molecular orbital (FMO) analysis supported the stability of compound N. These findings suggest that compound N is a strong candidate for further development as an antibacterial drug.
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Strategic assessment of lithium-ion battery degradation: a data-driven optimization approach

November 5, 2025

Lijuan Wang, Zhengyang Wang

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The performance and lifespan of energy storage systems, including those used for renewable energy storage and electric vehicles, are significantly hampered by the capacity degradation of lithium-ion batteries. For the best battery management, lowest associated maintenance costs, and longest battery life, an accurate estimation of this degradation is required. In this study, in order to improve performance through hyperparameter tuning, the Lichtenberg Optimization Algorithm (LOA) is used to estimate the capacity degradation of lithium-ion batteries using machine learning (ML) techniques. To choose the best, the different ML models were compared using various performance metrics. Among the models, the LOA-XGBoost with the highest values of R 2 (0.9384) and lowest values of MAE (0.0163), MSE (0.0041), RMSE (0.0640), and ME (0.8709) on testing database is the most efficient. The sensitivity analysis also demonstrated that environmental factors, such as the temperature of ambient and battery identification features like battery ID and test ID, contributed the most to degradation, while charge transfer resistance (Rct), traceable database reference (UID), and internal resistance (Re) contributed the least. This study shows how new optimization techniques, such as the Lichtenberg Optimization Algorithm, can be used to improve machine learning models and estimate battery capacity degradation more precisely.
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Enhancing energy efficiency with a photovoltaic-based multi-product system with integration of electrolysis and desalination

October 22, 2025

Qian Huang

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Photovoltaic (PV)-based multi-product systems are the most effective strategies for optimizing energy consumption and enhancing energy production efficiency, which integrates multiple energy production processes within a single framework for maximizing the utilization of available energy. This research aims to provide an inclusive analysis of the PV-based multi-product system performance, which integrates electrolysis, and desalination units for simultaneous generation of power, hydrogen, and freshwater. Therefore, the optimized Extreme Gradient Boosting (XGBoost) model was carefully developed and trained to ensure high levels of accuracy and precision in predicting the power, fresh water, and hydrogen outputs of the system under varying operational conditions. This optimized XGBoost model is achieved through the hyperparameter tuning by the HenryGas Solubility Optimization Algorithm, which ensures high levels of accuracy and precision. The day-by-day analysis of power and hydrogen generation demonstrated that in winter, a minimum PV power of 6,200 kW and a hydrogen mass flow rate of 65 kg were witnessed due to low solar radiation and short daylight hours. In contrast, summer and spring registered the highest PV power levels of 8,000 kW and 7,500 kW, respectively, and hydrogen mass flow rates of 75 kg due to high solar radiation and prolonged daylight hours, hence enhancing system performance.
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MHD Powell–Eyring fluid flow with Cattaneo–Christov heat and mass flux over a stretching sheet

August 4, 2025

Pudi Sreenivasa Rao, Chandrakala Panguluri

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This study investigates the influence of internal heat generation or absorption and both constructive and destructive chemical reactions on fluid flow. These effects are particularly significant in chemically reacting or dissociating fluids, where thermal energy exchange plays a crucial role. Such phenomena frequently arise in practical applications including chemical reactors, underground waste disposal, thermal management of semiconductor devices, and biomedical engineering processes such as targeted drug delivery involving reactive transport. The governing equations are highly nonlinear and coupled, making analytical solutions challenging. To overcome this complexity, a suitable numerical method is employed to obtain accurate solutions. Results for velocity, temperature, and concentration are illustrated graphically across varying parameter values. An increase in the heat and mass flux Deborah numbers (De 1 and De 2 ) leads to a reduction in velocity, while temperature and concentration increase with De 1 ; De 2 reduces both velocity and concentration. The skin friction coefficient (Cf), Nusselt number (Nu), and Sherwood number (Sh) exhibit growth with increasing De 1 , while De 2 enhances the Nusselt number at the wall.
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Mathematical modeling of submerged membrane adsorption hybrid system and parameter estimation for adsorption of cesium from radioactive wastewater implementing eco-friendly adsorbent

October 16, 2025

Talib M. Albayati, Wasan A. Muslim, Salam K. Al-Nasri, Issam K. Salih, Nisreen S. Ali, Mahir A. Abdulrahman, Ahmed A. Hadi, Khairi R. Kalash, Buthainah Ali Al-Timimi

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This study presents a comprehensive mathematical modeling and experimental investigation of a submerged membrane adsorption hybrid system (SMAHS) for the removal of cesium (Cs(I)) from radioactive wastewater. An eco-friendly adsorbent was synthesized and characterized for its adsorption performance. The homogeneous surface diffusion model (HSDM) model integrates surface diffusion as the dominant mass transfer mechanism and estimates key parameters, including the external mass transfer coefficient (K), surface diffusion coefficient (Ds), and membrane correlation coefficient (MCC). The pore and surface diffusion model (PSDM) is a comprehensive diffusion-based model that accounts for all diffusion processes in the solid phase. Experiments identified optimal operational conditions: pH 6.5, adsorbent dose 1.5 g/L, and contact time of 90 min, achieving over 95 % cesium removal. Ion selectivity tests demonstrated preferential adsorption of cesium in the presence of competing metal ions such as Na + , K + , and Ca 2+ . Regeneration studies showed that the adsorbent maintained over 85 % removal efficiency after five adsorption-desorption cycles, indicating cost-effectiveness. Model validation against experimental data exhibited strong agreement ( R 2 = 0.98), confirming the model’s suitability for design and scale-up of hybrid treatment systems. This work provides new insights into adsorption kinetics, hybrid system optimization, and eco-friendly adsorbent applications for radionuclide wastewater remediation.
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Research on fine management of abnormal response mode of nuclear power plant in group reactor mode

October 27, 2025

Qiang Cui, Qian Wu, Hong Chen, Jie Xin

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This study presents a data-driven framework for controlling abnormal responses in multi-reactor nuclear power plants under cooperative operating conditions. By integrating multiple-source operational parameters – including thermal power, steam pressure, coolant flow rates, and structural pressure dynamics – the system effectively models and identifies transient safety risks, such as pressurised vessel damage, molten relocation, and steam leakage. A novel Asymmetric Multi-Classifier Learning (AMCL) approach is proposed to enhance classification accuracy in the presence of class imbalance and dynamic uncertainty. Experimental validation using internal iterative loss convergence curves, confusion matrices, and real plant incident recordings demonstrates the superiority of the proposed method over traditional STL, SMCL, and balanced-AMCL schemes. Furthermore, multi-scenario response graphs confirm the system’s adaptability across typical load disturbances and emergency conditions.
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Statistical analysis of 3D MHD Williamson ternary nanofluid flow and heat transfer behaviour over a bilinear horizontally stretching sheet with activation energy, heat generation and Soret–Dufour effects

December 1, 2025

Pavani Guntaka, M. Changal Raju

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This study investigates the non-transient three-dimensional MHD boundary layer behaviour of a non-Newtonian Williamson ternary nanofluid over a stretching horizontal sheet. The ternary nanofluid consists of Cu, TiO 2 , and Fe 3 O 4 nanoparticles suspended in propylene glycol base fluid. Key physical Parametric effects are considered involve a Magnetic field acting in the transverse direction, chemical reaction process influenced by activation energy, Soret and Dufour effects, and heat generation or absorption. To analyse the system, the governing set of non-linear PDEs for the flow, heat, and mass transport have been simplified into a system of ordinary differential equations by similarity transformations. These equations are numerically Solved through MATLAB’s boundary value solver, bvp4c with the shooting method. Furthermore, Response Surface Methodology (RSM) is used to determine parameter sensitivity and show the cumulative effects of key factors. The findings show that greater Weissenberg numbers enhance both tangential and transverse velocities due to viscoelastic characteristics. Increasing porosity improves flow penetration and momentum dispersion. Thermal radiation and heat source enhance fluid temperature, but the Dufour effect cools through concentration-induced heat transfer. Quantitative findings from RSM indicate up to 11.6 % improvement in tangential velocity and 9.8 % temperature enhancement with higher Weissenberg and radiation parameters, while concentration decreases by 7.4 % under stronger chemical reaction effects. Statistical analysis via RSM and ANOVA confirms excellent model predictability, with R 2 values exceeding 97 % for all responses, reinforcing the reliability of the results. This comprehensive model provides a realistic depiction of tri-hybrid nanofluid dynamics under coupled thermal, chemical, and electromagnetic impacts. It has important implications for advanced thermal systems, energy-efficient reactors, and magneto-thermal flow control technologies.
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Numerical investigation of wall temperature effects on non-equilibrium condensation in supersonic nozzle using Eulerian-Eulerian model

October 27, 2025

Xueyao Zhang, Guodong Ma, Jiamei Sun

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Non-equilibrium condensation (NEC) commonly arises in various industrial applications, including steam turbines, supersonic nozzles, and aerospace propulsion systems. This phenomenon can significantly impair system efficiency and reliability by introducing abrupt phase transitions and associated energy losses. To mitigate its effects, several control methods have been proposed, among which surface heating stands out as a widely studied technique. In most existing research, surface heating has been implemented by applying a heat flux to the nozzle walls. In contrast, the present study investigates a different approach, wherein a constant-temperature wall is employed to introduce surface heating directly into the supersonic flow. The objective is to evaluate how this thermal boundary condition affects key two-phase flow parameters, including droplet size, condensed mass, nucleation rate, droplet growth rate, and liquid mass fraction (LMF). The numerical framework is validated using experimental data from the International Wet Steam Experimental Project (IWSEP) nozzle, ensuring the reliability of the predictions. Following validation, the study examines the influence of two wall temperatures – 400 °C and 800 °C – on nozzle performance and condensation behavior. Results indicate that heating the wall to 800 °C induces substantial changes in the internal flow structure and condensation characteristics. Specifically, the condensation shock shifts noticeably downstream, and both the peak nucleation rate and maximum LMF decrease sharply – by approximately 91 % and 6 %, respectively – compared to an adiabatic baseline. Additionally, the analysis reveals a clear relationship between wall temperature and mass flow rate. As the wall temperature increases, the mass flow rate decreases, with a reduction of about 3.86 % observed at 800 °C. Overall, these findings demonstrate that surface heating through wall temperature control offers an effective mechanism to influence and manage the behavior of NEC, with important implications for improving the efficiency and control of high-speed flow systems.
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Fluid characterization and splitting the heavy fraction by using a gamma distribution model and flash calculation based on EOS: a case study of one of the Iranian oil reservoirs

November 10, 2025

Kamyar Movagharnejad, Fatemeh Aali, Mahnam Ketabi, Fatemeh Hejazi, Nastaran Rasoolzadeh, Sara Gooran, Sayyed Ali Taheri

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This study explores advanced methods to improve the accuracy of hydrocarbon property predictions in oil reservoir fluids, utilizing novel data from an Iranian oil reservoir. The objectives include calculating molecular weights, specific gravity, and molar ratios of hydrocarbons (C11–C50) while evaluating the performance of models for critical properties and phase equilibria. Key approaches involve the gamma distribution and single carbon number (SCN) techniques for hydrocarbon characterization, alongside flash calculations using cubic and non-cubic equations of state, including Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Perturbed Chain-SAFT (PC-SAFT). Empirical data were generated using the Pederson regression method and compared with predictions from the gamma distribution function. Results indicate that the gamma distribution method reduces prediction errors by 50 %, with deviations of 5.19 % compared to 10.39 % for the SCN method. Among the equations of state, PC-SAFT achieved the highest accuracy, predicting vapor phase mole fractions with a deviation of 0.0172 % and gas and liquid densities with deviations of 22.125 % and 0.24 %, respectively. The novelty of this study lies in integrating unique field data from an Iranian reservoir with advanced modeling techniques, providing a reliable framework for reservoir fluid analysis. These findings contribute to optimizing oil recovery and improving hydrocarbon prediction accuracy.
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Magneto-thermal analysis of ternary nanofluids with ESHS and Joule heating

November 7, 2025

Chandrakala Panguluri, Srinivasa Rao Vempati

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This study examines mass and heat transfer in a permeable ternary nanofluid flow over a stretching sheet, considering the combined effects of a chemical reaction, Joule heating, an exponentially space-dependent heat source, and an inclined magnetic field. Three types of water-based nanofluids are analysed: mono (Cu), hybrid (Cu + Al 2 O 3 ), and ternary (Cu + Al 2 O 3 + Ag). The governing nonlinear partial differential equations are reduced using similarity transformations and solved numerically via MATLAB’s BVP4c method. The results reveal that ternary nanofluids exhibit superior thermal performance, with significantly higher temperature profiles compared to mono and hybrid nanofluids. The influence of key parameters is also investigated. Increased suction and velocity slip reduce thermal and concentration boundary layers, while higher Biot numbers and heat source intensity enhance temperature profiles. Additionally, Joule heating and magnetic field inclination intensify the heat transfer rate. These findings provide valuable insights for optimizing thermal systems in applications such as solar energy collectors, thermoelectric devices, and chemical processing industries.
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Robust decentralized sliding mode control for the Tennessee-Eastman Process: a plant-wide approach

August 15, 2025

Amin Khatami, Hooman Fatoorehchi, Ehsan Maani Miandoab

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This study introduces a robust decentralized multiloop single-input-single-output (SISO) control architecture for the Tennessee-Eastman Process (TEP) utilizing Sliding Mode Control (SMC) and Higher-order Sliding Mode Control (HOSMC). Sliding Mode Controllers are employed due to their robustness against uncertainties and disturbances, effectively managing TEP’s complex nonlinearities and dynamic challenges. However, the chattering problem remains a significant drawback of these controllers. Various techniques exist to mitigate this issue, with Higher-order SMC being one of the most effective methods. In this paper, both SMC and HOSMC are tested and compared with traditional PID controllers. To achieve optimal performance, the controllers’ coefficients are optimized using the Particle Swarm Optimization (PSO) technique. The methodology systematically designs production rate and inventory controls using HOSMC. The results demonstrated that, despite the increased complexity and the difficulty of implementation and parameter tuning in HOSMC, the output fluctuations did not significantly improve. This suggests that the proposed SMC method is simple yet effective in achieving optimal production rates and maintaining stability under significant disturbances, such as feed composition variability and reaction kinetics shifts. A notable feature of the system is its ability to autonomously override standard operations during severe disruptions, such as the complete loss of a reactant feed. Compared to other control methods, the SMC-based architecture exhibits superior accuracy, simplicity, and reliability in managing critical process variables, even under stringent operational constraints. This work underscores the potential of SMC as a practical and efficient solution for plant-wide control in complex chemical processes.

Special Issue in Honor of Professor Eugeny Kenig; Guest Editor: Ulf Brinkmann and Mark Piper

Special Issue in Honor of Professor Faïçal Larachi; Guest Editor: Professor Safia Hamoudi

Special Issue: CPPM special issue in honour of Professor W.Y. “Bill” Svrcek; Guest Editors: Brent R. Young and Ramin Bozorgmehry

Special Issue: CHEM-CONFLUX²⁰; Guest Editors: Dipesh S. Patle, Reza Zarghami and Sushil Kumar

Special Issue of 29th Symposium of Malaysian Chemical Engineers (SOMChE) 2016 - Process System Engineering, Guest Editors: Agus Saptoro, Jobrun Nandong, Mohd Azlan Bin Hussain, Syamsul Rizal Abd. Shukor

Special Issue: Selected Extended Papers from the 12th International Conference on Membrane Science and Technology (MST2015) Symposium on Modeling and Simulation, Guest Editors: Reza Zarghamia and Ahmad Fauzi Ismail

CAPE Forum 2011

WCCE8 2009

Chemeca 2008

CHEMPOR 2008

Papers from CITICCOMS 2007

Issue 1

Issue 3

Issue 2

Papers from Chemeca 2006

Issue 1

Chemical Product and Process Modeling publishes special and thematic issues focussed on important and emerging topics in the field of study. The journal has established a rigorous process to ensure that any special issue manuscripts follow the same high-quality standards and peer review processes as regular manuscripts. For further information on the journal’s peer review policy please see the "Instructions for Authors".

Recent Special Issues

Journal Impact Factor	1.3	2024, Journal Citation Reports (Clarivate, 2025)
5-year Journal Impact Factor	1.1	2024, Journal Citation Reports (Clarivate, 2025)
Journal Citation Indicator	0.17	2024, Journal Citation Reports (Clarivate, 2025)
CiteScore	2.2	2024, Scopus (Elsevier B.V., 2025)
SCImago Journal Rank	0.257	2024, SJR (Scimago Lab, 2025; Data Source: Scopus)
Source Normalized Impact per Paper	0.514	2024, CWTS Journal Indicators (CWTS B.V., 2025; Data Source: Scopus)

More information about journal rankings

Submission
You can easily submit your manuscript online. Simply go to...
http://mc.manuscriptcentral.com/dgcppm
...and you will be guided through the whole peer-reviewing and publishing process.

Your benefits of publishing with us

Ahead-of-print, online publication with short turnaround times
High quality manuscript processing through ScholarOne Manuscripts®
Optional open access publication
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
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Professional sales and marketing activities

Submission process

Before preparing a manuscript please have a look at our Instruction for Authors
Submission of your paper via our submission management tool
Peer review process (you will be guided through every step)
If accepted: you have the option to publish it open access

Please note

We encourage software developers and other practitioners to submit articles on specific topics to the journal
Articles published in CPPM must be of the highest scientific quality and originality. They are expected to be widely read and cited by all interested researchers and engineers in the field of chemical process and product simulation who, in turn, will have immediate access to high quality and relevant articles in their area
Manuscripts must be written in clear and concise English
Before submitting your article please have a look at our Ethical Guidelines and our Copyright Transfer Agreement
Once your article is accepted you have the option to publish it open access
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ page for authors or contact us at: cppm@degruyter.com

We look forward to receiving your manuscript!

Open Access

In this journal, authors have the option to publish their article under an open access license. Open access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints. Each article published under an open access license is subject to an Article Processing Charge of €2300.

Editorial Board
Jamal Chaouki, Ecole Polytechnique de Montreal, Canada, jamal.chaouki@polymtl.ca
Navid Mostoufi, University of Tehran, Iran, mostoufi@ut.ac.ir
Rahmat Sotudeh-Gharebagh, University of Tehran, Iran sotudeh@ut.ac.ir, cppm@degruyter.com

Associate Editors
Thomas A. Adams II, McMaster University, Canada tadams@mcmaster.ca
Mohd Azlan Bin Hussain, University of Malaya, Malaysia mohd_azlan@um.edu.my
Diego Barletta, Università di Salerno, Italy, dbarletta@unisa.it
Miryan Cassanello, Universidad de Buenos Aires, Argentina miryan@di.fcen.uba.ar
Mario R. Eden, Auburn University, USA edenmar@auburn.edu
Eugénio C. Ferreira, Universidade do Minho, Portugal ecferreira@deb.uminho.pt
Iftikhar Karimi, National University of Singapore, Singapore cheiak@nus.edu.sg
Eugeny Kenig, Paderborn University, Germany eugeny.kenig@upb.de
Surendra Kumar, Indian Institute of Technology Roorkee, India skumar@iitr.ac.in
Roberto Lemus-Mondaca, Universidad de Chile, rlemus@uchile.cl
Patrick Linke, Texas A&M University at Qatar, Qatar patrick.linke@qatar.tamu.edu
Iqbal Mujtaba, University of Bradford, UK, I.M.Mujtaba@bradford.ac.uk
Mark Nelson, University of Wollongong, Australia mnelson@uow.edu.au
Fernando Preto, CANMET Energy Technology Centre, Canada preto@nrcan.gc.ca
Luis Puigjaner, Universitat Politecnica de Catalunya, Spain luis.puigjaner@upc.edu
Seshagiri Rao Ambati, Indian Institute of Petroleum and Energy (IIPE), India; seshagiri.che@iipe.ac.in　
Jean-Michel Reneaume, Ecole Nationale Supérieure en Génie des Technologies Industrielles, ENSGTI, France jean-michel.reneaume@univ-pau.fr
Bill Svrcek, University of Calgary, Canada svrcek@ucalgary.ca
Jan Verstraete, IFP Energies nouvelles, France jan.verstraete@ifp.fr
Brent Young, University of Auckland, New Zealand b.young@auckland.ac.nz
Reza Zarghami, University of Tehran, Iran, rzarghami@ut.ac.ir
Edwin Zondervan, University of Twente, The Netherlands, e.zondervan@utwente.nl

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Journal information

Additional information

eISSN:

1934-2659

Language:

English

Publisher:

De Gruyter

Additional information

First published:

May 1, 2006

Publication Frequency:

6 issues per year

eISSN: 1934-2659

Audience(s) for this journal

chemical, process, food, pharmaceutical, oil and gas engineering researchers, practitioners, and software developers in a new forum for the international modeling and simulation community

Chemical Product and Process Modeling

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