journal: Chemical Product and Process Modeling

Impact Factor: 1.3

Zeitschrift

Chemical Product and Process Modeling

Herausgegeben von: Rahmat Sotudeh-Gharebagh , Navid Mostoufi und Jamal Chaouki

Sprache: Englisch

Erstveröffentlichung: 1. Mai 2006

Erscheinungsweise: 6 Hefte pro Jahr

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Über diese Zeitschrift

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

Geschichte

Content available since 2006 (Volume 1, Issue 1)

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Fundamental feature-based assessment of water quality through optimal enhanced models

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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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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

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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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Multi-aspect evaluation of integrated ethane liquefaction processes: economic, energy, and thermodynamic analysis for transport applications

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A linear model for LNG transport via existing pipeline and re-gasification terminals

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Robust decentralized sliding mode control for the Tennessee-Eastman Process: a plant-wide approach

15. August 2025

Amin Khatami, Hooman Fatoorehchi, Ehsan Maani Miandoab

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Prediction of CO₂ mole fraction via CO₂CPU process using different machine learning algorithms

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

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MHD Powell–Eyring fluid flow with Cattaneo–Christov heat and mass flux over a stretching sheet

4. August 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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Thermodynamics modeling of heat and mass transfer dynamics in drying of prekese pod: quality retention

1. August 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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Intelligence predictions: enhancing bubble column efficiency with advanced machine learning (ML) approaches

1. August 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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Statistical analysis of emergency response optimization for hazardous chemical processes in industrial facilities

1. August 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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Development of an optimized fractional-order controller featuring dead-time and disturbance compensation

1. August 2025

Prashant Garg, Brijendra Mishra

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This work highlights a unique control scheme tailored for time-delayed systems under disturbances, blending a fractional filter, dead-time adjustment, and a disturbance compensator. The controller is designed using the Internal Model Control (IMC) approach and optimized through the Snake Optimization (SO) algorithm. This control scheme is applied to practical problems, such as heat exchanger systems and First Order Integrating Plus Delay Time (FOIPDT) process. Integral Absolute Error (IAE) and Integral Square Error (ISE) are key metrics for analyzing closed-loop control performance relative to existing methods. The scheme effectively enhances the closed-loop response by improving IAE values and minimizing control efforts needed to accomplish the preferred output. The Riemann surface principle is utilized for stability analysis. Disturbance rejection and robustness tests show significant improvements in closed-loop response, as illustrated in the numerical simulations.
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The inertia effects in rheodynamic lubrication of an externally pressurized thrust bearing using Herschel-Bulkley lubricant with sinusoidal injection

1. August 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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Multi-objective optimization of temperature profile using reinforcement learning in batch crystallization process

31. Juli 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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Biodiesel production from waste cooking oil using Indion-140 catalyst: optimization studies

31. Juli 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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Utilizing a Naïve Bayes model for gasification process prediction: enhancing efficiency and sustainability

25. Juli 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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Comparative analysis of heat transfer enhancement in vortex generators using nanofluids under turbulent flow conditions

22. Juli 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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Advanced prediction of CO₂ emissions and energy consumption in high-performance concrete manufacturing

22. Juli 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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Modeling the CO and CO₂ output in the gasification process using optimized hybrid machine learning models

22. Juli 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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Optimized photocatalytic performance of TiO₂ nanoparticles synthesized via Cymbopogon Citratus leaf extract

21. Juli 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

18. Juli 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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Fuzzy TOPSIS optimization of heat transfer rate of magnetized radiative Casson nanofluid over a stretching sheet

17. Juli 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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Influence of surface properties and concentration of NiO nanoparticles towards the decoloration of textile dye reactive black 5

17. Juli 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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Use of membrane distillation for selective recovery of chemicals like ethyl alcohol: a critical review

17. Juli 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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Visible emissions through upconverting nanodots of NaYF₄:Er³⁺; Yb³⁺ nanocrystals: a brief study in their upconversion mechanism and energy transfers

14. Juli 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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CFD analysis of heat transfer and friction factor characteristics of nanofluid flow in microtubes

14. Juli 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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Classification of water quality based on aesthetic and chemical parameters

4. Juli 2025

Fengmin Cheng

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Consumers evaluate water quality mostly on the basis of its aesthetically acceptable qualities, which include flavor, odor, and appearance. The aesthetic acceptability of water is greatly impacted by the seven heavy metal characteristics: calcium, sodium, potassium, sulfate, magnesium, total hardness, and chloride. These parameters, in turn, have a profound effect on variables such as flavor and odor. The present paper attempts to enhance the level of prediction regarding water quality with the aid of machine learning algorithms. Accordingly, Extra Tree Classification (ETC) was applied to the projection of water quality parameters. In addition to this base model, the Subtraction-Average-Based Optimization and the Horned Lizard Optimization Algorithm were utilized to optimize its level of prediction accuracy. This strategic integration resulted in new hybrid model types. The ETC model, in combination with SABO, is the ETSA framework, which, with HLOA, integrates into the ETHL framework. During Testing for the Accuracy metric, the highest score was 0.986 from the ETHL model, while the ETSA model attained 0.972 and yielded the second best. In the Testing phase, concerning the Precision metric, the best performing model was, once again, the ETHL model with 0.986, while the ETC model developed the weakest performance with a value of 0.970. Future research could enhance water quality prediction through advanced hybrid models, real-time monitoring, deep learning, and validation across diverse water bodies and parameters.
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Optimization of iron (III) oxide nanoparticles production from natural waste for use in wastewater applications

19. Juni 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

19. Juni 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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Predicting crude unit failures and production impact using lagging maintenance indicators in oil refineries

18. Juni 2025

Sadiq Ibrahim Almogargesh

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Oil refineries operate in environments of high pressure and temperature. Therefore, maintaining the availability of these assets is a challenge to the maintenance teams. The paper deals with the development of maintenance key performance indicators (KPIs), considering both leading (post-failure) and lagging metrics for maintenance performance. Traditionally, these KPIs are looked upon in isolation; possible relationships between them are not considered. The present study investigated the relationships between various lagging maintenance KPIs relating production rate to equipment availability. The study was conducted under the refinery crude unit, which is generally considered one of the most important parts of any oil refinery. A regression model was developed that empirically relates equipment availability with the production rate. This was further extended to an empirical equation correlating equipment availability and downtime duration with various downtime factors. The results indicate that the availability of equipment has a high correlation with the production rate. This relationship is driven by factors of downtime, which have been sorted according to duration and type. These equations greatly support the management decisions in the development of maintenance strategies. In addition, the research quantifies the costs related to each factor of downtime, which can be applied in formulating future maintenance strategies.
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Empirical modelling of physicochemical properties of carbon capture solvent-aqueous 2-amino-2-methyl-1-propanol (AMP)

18. Juni 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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Raising pros and cons of falling film and packed column for absorption of NH₃ by a NH₃–H₂O solution

2. Juni 2025

Licianne Pimentel Santa Rosa, Karen Valverde Pontes

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The refrigeration absorption cycle is a mature technology to obtain refrigerant effect at low temperatures, and the absorber unit plays a key role on the cycle efficiency. Falling liquid films and packed columns are usually employed in refrigeration cycle with ammonia and water. This study addresses the pros and cons of each equipment, comparing both in terms of absorption capacity, equipment cost and cooling demand by mathematical modelling and simulations. The packed absorption column is modeled in Aspen Plus, and the falling film, by an equation-oriented approach in Matlab. The results indicate that the falling film ensures a uniform coolant distribution, enhancing ammonia absorption efficiency. Consequently, for the same absorption capacity, the falling film enables nearly 80 % reduction in the required volume of the column. However, this space-saving advantage comes at the cost of a 51 % increase in capital and installation expenses. Therefore, the falling film offers a more compact unit but, when space limitation is not an issue, absorption columns might take advantage.
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Determination of hydrogen production in power plant using predictive machine learning methods

30. Mai 2025

Leijuan Ma, Kai Yuan

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With increasing global warming, the focus on sustainable and eco-friendly energy solutions is raised. The production of hydrogen is identified to be extremely important due to its role in efforts to reduce greenhouse gas emissions, hence mitigating climate change. Predicting the amount of hydrogen production accurately in this context is essential. This requires developing and/or applying complex predictive tactics to improve hydrogen production related to reliability and efficiency in thermal power plants. Hydrogen production quantification in thermal power plants using advanced predictive machine learning (ML) tactics is the focus of this exploration. The CatBoost, Random Forest (RF), Long Short-Term Memory (LSTM), and Bootstrap aggregating tactics are used to project hydrogen production. On the other hand, the Firefly algorithm is utilized to fine-tune the hyperparameters of these schemes. Predictions are made on a simulated dataset collected from the recommended power plant, and the accuracy of the schemes’ predictions is assessed using different evaluation metrics. Regarding the results, it turns out that the Firefly-CB model performs very well, having near-perfect R 2 value of 0.9764, proving the capability of predicting hydrogen with small errors. Nevertheless, the Firefly-LSTM model performs poorly with the lowest R 2 = 0.5221 and highest errors during data test. The productivity of Firefly-CB is also compared with that of other advanced optimizers and ensemble schemes, in which Firefly-CB again reveals its strength in the recommended cycle’s hydrogen prediction. These findings from the study demonstrate that hybrid ML schemes can significantly improve the prediction of hydrogen production at power stations, contributing to efficient and sustainable energy management.
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Collimated infrared transceiver for identifying bubble and slug regimes

29. Mai 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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Data-driven support vector regression-based hybrid models for prediction of syngas production in the gasification process of biomass

29. Mai 2025

Ying Yang

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Biomass gasification represents a thermal and chemical interaction that transforms biomass substances contained in the reactor. Several interconnected factors influence the gasifier’s performance, including the fuel type, reactor design, and operational parameters. A comprehensive recognizing this operational approach is essential for varied consumers, such as individuals interested in the gasifier’s output, reactor manufacturers aiming to optimize their designs, or planners seeking the best-performing gasifier for specific fuel types. Extensive research and development efforts have been devoted to gasification, encompassing both experimental and computational approaches. Computational modeling tools offer significant advantages, enabling users to determine optimal reactor conditions without time-consuming and costly experimentation. Efficiently interpreting the embedded information in gasification modeling necessitates a systematic and logical analysis, a goal pursued in the present study. Data-driven Support Vector Regression (SVR) based hybrid schemes utilizing Giant Trevally Optimizer (GTO) and Smell Agent Optimization (SAO) were created in this investigation to determine the score of H 2 and N 2 concentration based on the various input parameters. A strong predictive performance of hybrid schemes, especially SVGT, was confirmed by a coefficient of determination ( R 2 ) of 0.994 and 0.999 in the case of yielded N 2 and H 2 estimation. The technique can produce reliable input data for appraisal of costs and benefits and life cycle ecological assessments, allowing politicians and financiers to make more transparent decisions.
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Response surface methodology optimization of dye adsorption by palm fatty acid distillate adsorbent

26. Mai 2025

Bashir Aderemi Hamzat, Fadina Amran, Muhammad Abbas Ahmad Zaini

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This work is aimed to optimize the methylene blue and congo red removal by adsorbent produced by palm fatty acid distillate. The adsorbent was characterized for surface morphology, surface functional groups, elemental composition and textural properties. The adsorption was studied at varying concentrations, contact times, temperatures, and solution pH. The Box-Behnken Design (BBD) combined with the Response Surface Methodology (RSM) was developed on the basis of experimental results to optimize the adsorption conditions. The ANOVA results indicate the high precision of the model to represent the adsorption data as the p-value was < 0.05. Also, the significant R 2 value of greater than 0.9 and large F-value show that the model is valid for the present work The results of the RSM optimization indicate that the optimal conditions to attain a 47.8 mg/g methylene blue capacity are concentration of 100 mg/L, temperature of 70 °C, contact time of 18 h and pH 6, while for congo red, the capacity of 189 mg/g was achieved under the concentration of 300 mg/L, temperature of 70 °C, contact time of 15 h and pH 6. Finally, statistical significance was shown for the linear model, and the current work demonstrates that the adsorbent can be utilized for adsorption of methylene blue and congo red.
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Enhancing solar dryer performance through computational fluid dynamics in forced convection conditions

6. Mai 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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Research on the connection between sustainable rural economic growth and the usage of chemicals in rural areas

16. April 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

31. März 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

10. März 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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Recurrent neural network based modelling and temperature control of a bioreactor for ethanol production

10. Februar 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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Chemical conversion of rural biomass waste: effective resource utilization and rural economic development

21. Januar 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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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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eISSN:

1934-2659

Sprache:

Englisch

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De Gruyter

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Erstveröffentlichung:

1. Mai 2006

Erscheinungsweise:

6 Hefte pro Jahr

eISSN: 1934-2659

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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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