Pub Date : 2025-12-26DOI: 10.1016/j.dche.2025.100287
Balázs Palotai , Gábor Kis , Tibor Chován , Ágnes Bárkányi
Surrogate-based flowsheet model calibration is a critical extension of using flowsheet models in Digital Twin (DT) systems. However, maintaining accurate surrogates over time is increasingly challenging, especially when models are deployed in real-time or near-real-time environments, where continuous changes in the physical systems can lead to model drift. To address this challenge, this study introduces a novel online learning–inspired framework to support the continuous maintenance of surrogate-based model calibration. This methodology bridges the gap between offline surrogate development and adaptive model maintenance. By embedding the surrogate in an online learning loop, the framework enables continuous calibration while minimizing reliance on resource-intensive flowsheet simulations. When applied to an industrial flowsheet calibration case, the approach reduced the number of direct calibration steps by up to 94% while preserving global model accuracy. The proposed method offers a scalable, automated, and resilient solution for maintaining surrogate and flowsheet model performance in dynamic industrial environments.
{"title":"Online learning supported surrogate-based flowsheet model maintenance","authors":"Balázs Palotai , Gábor Kis , Tibor Chován , Ágnes Bárkányi","doi":"10.1016/j.dche.2025.100287","DOIUrl":"10.1016/j.dche.2025.100287","url":null,"abstract":"<div><div>Surrogate-based flowsheet model calibration is a critical extension of using flowsheet models in Digital Twin (DT) systems. However, maintaining accurate surrogates over time is increasingly challenging, especially when models are deployed in real-time or near-real-time environments, where continuous changes in the physical systems can lead to model drift. To address this challenge, this study introduces a novel online learning–inspired framework to support the continuous maintenance of surrogate-based model calibration. This methodology bridges the gap between offline surrogate development and adaptive model maintenance. By embedding the surrogate in an online learning loop, the framework enables continuous calibration while minimizing reliance on resource-intensive flowsheet simulations. When applied to an industrial flowsheet calibration case, the approach reduced the number of direct calibration steps by up to 94% while preserving global model accuracy. The proposed method offers a scalable, automated, and resilient solution for maintaining surrogate and flowsheet model performance in dynamic industrial environments.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100287"},"PeriodicalIF":4.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.dche.2025.100286
Mohammad Gheibi , Seyyed Roohollah Masoomi , Mohammad Eftekhari , Martin Palušák , Daniele Silvestri , Miroslav Černík , Stanisław Wacławek
Due to the complexity and the need for automation in adsorption systems, this study develops a decision-making model using 20 Machine Learning Algorithms (MLAs), Response Surface Methodology (RSM), and lab tests. Inputs include pH, metal type, concentration, adsorbent mass, and time; output is removal percentage for process control. The best-performing models (Tree Random Forest: TRF, lazy Instance-Based K: IBK, and Function Multilayer Perceptron: FMLP) achieve high accuracy for prediction of Removal Percentage (RP) of heavy metals with >0.92 correlation coefficient and >0.8 recall/precision indicators. As a novelty of this study, a decision-making model for the heavy metal adsorption process onto waste materials is developed for the first time using the Knowledge Flow (KF) platform in WEKA software, incorporating real-time data to improve process control and operational efficiency. The results demonstrated that the cation type and pH, with a P-value < 0.001, are the most significant factors affecting the RP. To achieve maximum RP, the pH should be set to 5, and the adsorbent amount should be in a range of 8.67-10 g L-1, regardless of the initial concentration and type of ions (Pb2+, Mn2+, and Co2+). Then, evaluating MLAs showed that TRF, with a correlation coefficient exceeding 0.96, performs best for predicting RP, potentially reaching 0.99 at a split percentage around 80%. TRF uses real-time experimental data in water treatment systems to anticipate RP up to 98.75% and to activate alarms for RP below 80% using the KF principle.
由于吸附系统的复杂性和自动化需求，本研究利用20种机器学习算法（MLAs）、响应面方法（RSM）和实验室测试开发了一个决策模型。输入包括pH值、金属类型、浓度、吸附剂质量和时间；输出是用于过程控制的去除百分比。表现最好的模型（Tree Random Forest: TRF， lazy Instance-Based K: IBK和Function Multilayer Perceptron: FMLP）在预测重金属去除率（RP）方面具有很高的准确性，相关系数为>；0.92，召回率/精度指标为>；0.8。作为本研究的新颖之处，本文首次利用WEKA软件中的知识流（Knowledge Flow， KF）平台构建了废弃物吸附重金属过程的决策模型，结合实时数据，提高了过程控制和操作效率。结果表明，阳离子类型和pH是影响RP最显著的因素，p值为<； 0.001。为了获得最大RP，无论初始浓度和离子类型（Pb2+、Mn2+和Co2+）如何，pH值应设为5，吸附剂用量应在8.67-10 g L-1范围内。然后，评估mla表明，TRF的相关系数超过0.96，对RP的预测效果最好，在80%左右的分割率下可能达到0.99。TRF使用水处理系统中的实时实验数据预测RP高达98.75%，并使用KF原理激活RP低于80%的警报。
{"title":"Smart control of heavy metal adsorption onto LDC wastes for Industry 4.0 applications","authors":"Mohammad Gheibi , Seyyed Roohollah Masoomi , Mohammad Eftekhari , Martin Palušák , Daniele Silvestri , Miroslav Černík , Stanisław Wacławek","doi":"10.1016/j.dche.2025.100286","DOIUrl":"10.1016/j.dche.2025.100286","url":null,"abstract":"<div><div>Due to the complexity and the need for automation in adsorption systems, this study develops a decision-making model using 20 Machine Learning Algorithms (MLAs), Response Surface Methodology (RSM), and lab tests. Inputs include pH, metal type, concentration, adsorbent mass, and time; output is removal percentage for process control. The best-performing models (Tree Random Forest: TRF, lazy Instance-Based K: IBK, and Function Multilayer Perceptron: FMLP) achieve high accuracy for prediction of Removal Percentage (RP) of heavy metals with >0.92 correlation coefficient and >0.8 recall/precision indicators. As a novelty of this study, a decision-making model for the heavy metal adsorption process onto waste materials is developed for the first time using the Knowledge Flow (KF) platform in WEKA software, incorporating real-time data to improve process control and operational efficiency. The results demonstrated that the cation type and pH, with a P-value < 0.001, are the most significant factors affecting the RP. To achieve maximum RP, the pH should be set to 5, and the adsorbent amount should be in a range of 8.67-10 g L<sup>-</sup><sup>1</sup>, regardless of the initial concentration and type of ions (Pb<sup>2+</sup>, Mn<sup>2+</sup>, and Co<sup>2+</sup>). Then, evaluating MLAs showed that TRF, with a correlation coefficient exceeding 0.96, performs best for predicting RP, potentially reaching 0.99 at a split percentage around 80%. TRF uses real-time experimental data in water treatment systems to anticipate RP up to 98.75% and to activate alarms for RP below 80% using the KF principle.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100286"},"PeriodicalIF":4.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.dche.2025.100284
Ghada Al Assi , Ali Raqee Abdulhadi , Rekha MM , Shaker Al-Hasnaawei , Subhashree Ray , Amrita Pal , Renu Sharma , Aashna Sinha , Mehrdad Mottaghi
The density behavior of TCM-based ionic liquids (ILs) mixed with ethanol forms the focus of this investigation, highlighting their distinctive physicochemical characteristics and industrial relevance. This research utilizes a suite of sophisticated machine learning methods, encompassing K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), Decision Tree (DT), Adaptive Boosting (AdaBoost), Artificial Neural Networks (ANN), Random Forest (RF), and Ensemble Learning (EL) to model and forecast density behavior with high precision. To ensure each predictive model operated at its highest capability, the tuning of their internal hyperparameters was carried out through the Coupled Simulated Annealing (CSA) optimization strategy. This global search method was chosen for its ability to efficiently traverse complex parameter spaces and avoid premature convergence. The learning algorithms were trained and validated on a curated experimental dataset consisting of 426 density observations, of which approximately eighty percent (341 samples) were dedicated to model training, while the remaining portion (85 samples) served as an independent test set for evaluating generalization performance. The study covers three TCM-based ionic liquids: [C1C2IM][TCM], [C1C2PYR][TCM], and [C1C2MOR][TCM], in mixture with ethanol, across varying mole fractions and temperatures. The modeling leveraged key input features including ionic liquid type, molar mass (g/mol), mole fraction, and temperature (K) which are crucial determinants of the density behavior. The Monte Carlo–based sensitivity evaluation revealed that the ionic liquid mole fraction exerted the strongest effect on the system, with IL type, molar mass, and temperature contributing in descending order of influence. Prior to developing any of the predictive models, the complete dataset was thoroughly examined to verify its consistency, reliability, and overall suitability for machine-learning–based analysis. After this verification stage, the trained models were benchmarked using multiple statistical criteria. Among all evaluated approaches, the convolutional neural network demonstrated the most superior predictive capability, reflected in its minimal RMSE values, near-unity R² scores, and the lowest AARE percentages in both the training and independent testing evaluations. These findings clearly confirm the remarkable ability of machine learning techniques particularly convolutional neural networks to precisely predict the density of mixtures containing TCM-based ionic liquids and ethanol. This method provides a strong, efficient, and economical substitute for conventional experimental methodologies, empowering scientists to predict density characteristics with enhanced assurance and decreased experimental effort.
{"title":"Simulation of TCM-based ionic liquids density behavior in a mixture with ethanol using machine learning approaches","authors":"Ghada Al Assi , Ali Raqee Abdulhadi , Rekha MM , Shaker Al-Hasnaawei , Subhashree Ray , Amrita Pal , Renu Sharma , Aashna Sinha , Mehrdad Mottaghi","doi":"10.1016/j.dche.2025.100284","DOIUrl":"10.1016/j.dche.2025.100284","url":null,"abstract":"<div><div>The density behavior of TCM-based ionic liquids (ILs) mixed with ethanol forms the focus of this investigation, highlighting their distinctive physicochemical characteristics and industrial relevance. This research utilizes a suite of sophisticated machine learning methods, encompassing K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), Decision Tree (DT), Adaptive Boosting (AdaBoost), Artificial Neural Networks (ANN), Random Forest (RF), and Ensemble Learning (EL) to model and forecast density behavior with high precision. To ensure each predictive model operated at its highest capability, the tuning of their internal hyperparameters was carried out through the Coupled Simulated Annealing (CSA) optimization strategy. This global search method was chosen for its ability to efficiently traverse complex parameter spaces and avoid premature convergence. The learning algorithms were trained and validated on a curated experimental dataset consisting of 426 density observations, of which approximately eighty percent (341 samples) were dedicated to model training, while the remaining portion (85 samples) served as an independent test set for evaluating generalization performance. The study covers three TCM-based ionic liquids: [C1C2IM][TCM], [C1C2PYR][TCM], and [C1C2MOR][TCM], in mixture with ethanol, across varying mole fractions and temperatures. The modeling leveraged key input features including ionic liquid type, molar mass (g/mol), mole fraction, and temperature (K) which are crucial determinants of the density behavior. The Monte Carlo–based sensitivity evaluation revealed that the ionic liquid mole fraction exerted the strongest effect on the system, with IL type, molar mass, and temperature contributing in descending order of influence. Prior to developing any of the predictive models, the complete dataset was thoroughly examined to verify its consistency, reliability, and overall suitability for machine-learning–based analysis. After this verification stage, the trained models were benchmarked using multiple statistical criteria. Among all evaluated approaches, the convolutional neural network demonstrated the most superior predictive capability, reflected in its minimal RMSE values, near-unity R² scores, and the lowest AARE percentages in both the training and independent testing evaluations. These findings clearly confirm the remarkable ability of machine learning techniques particularly convolutional neural networks to precisely predict the density of mixtures containing TCM-based ionic liquids and ethanol. This method provides a strong, efficient, and economical substitute for conventional experimental methodologies, empowering scientists to predict density characteristics with enhanced assurance and decreased experimental effort.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100284"},"PeriodicalIF":4.1,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.dche.2025.100285
Artur Brotons Rufes , Sergio Posada Pérez , Albert Poater
Density Functional Theory (DFT) has become the cornerstone of modern computational catalysis, providing a practical balance between accuracy and efficiency in describing molecular structure, bonding, and reactivity. This review presents a comprehensive overview of DFT methodology, from its quantum-mechanical foundations and basis-set construction to the hierarchy of exchange–correlation functionals defined by Jacob’s ladder. We discuss how DFT enables mechanistic elucidation of homogeneous and heterogeneous catalytic processes, highlighting benchmark studies that compare functional performance across representative reactions and transition states. Key interpretative tools, such as bond order analysis (Mayer, Wiberg, AIM/QTAIM), Natural Bond Orbital (NBO) theory, Energy Decomposition Analysis (EDA), and Non-Covalent Interaction (NCI) plots, are introduced as essential descriptors linking electronic structure to reactivity. The review also explores the integration of DFT with machine learning, microkinetic modeling, and automated reaction discovery, outlining recent advances toward predictive catalysis. Collectively, this work provides both conceptual and practical guidance for applying DFT to catalytic problems, emphasizing methodological awareness, descriptor-based interpretation, and emerging data-driven strategies for rational catalyst design. However, the main take-home message is that for DFT calculations, while in-depth methodological expertise is not essential, a clear comprehension of the theory’s practical application is crucial.
{"title":"DFT in catalysis: Complex equations for practical computing applications in chemistry","authors":"Artur Brotons Rufes , Sergio Posada Pérez , Albert Poater","doi":"10.1016/j.dche.2025.100285","DOIUrl":"10.1016/j.dche.2025.100285","url":null,"abstract":"<div><div>Density Functional Theory (DFT) has become the cornerstone of modern computational catalysis, providing a practical balance between accuracy and efficiency in describing molecular structure, bonding, and reactivity. This review presents a comprehensive overview of DFT methodology, from its quantum-mechanical foundations and basis-set construction to the hierarchy of exchange–correlation functionals defined by Jacob’s ladder. We discuss how DFT enables mechanistic elucidation of homogeneous and heterogeneous catalytic processes, highlighting benchmark studies that compare functional performance across representative reactions and transition states. Key interpretative tools, such as bond order analysis (Mayer, Wiberg, AIM/QTAIM), Natural Bond Orbital (NBO) theory, Energy Decomposition Analysis (EDA), and Non-Covalent Interaction (NCI) plots, are introduced as essential descriptors linking electronic structure to reactivity. The review also explores the integration of DFT with machine learning, microkinetic modeling, and automated reaction discovery, outlining recent advances toward predictive catalysis. Collectively, this work provides both conceptual and practical guidance for applying DFT to catalytic problems, emphasizing methodological awareness, descriptor-based interpretation, and emerging data-driven strategies for rational catalyst design. However, the main take-home message is that for DFT calculations, while in-depth methodological expertise is not essential, a clear comprehension of the theory’s practical application is crucial.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100285"},"PeriodicalIF":4.1,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.dche.2025.100282
Amin Soleimani Mehr , Günter Scheffknecht , Reihaneh Zohourian , Jörg Maier , Markus Reinmoeller
Ammonia is increasingly recognized as a versatile hydrogen carrier and energy vector with the potential to decarbonize industrial processes and global energy trade. Among the emerging production routes, blue ammonia—derived from natural gas with integrated carbon capture and storage (CCS)—offers a transitional pathway toward low-carbon energy systems. This work presents the development and assessment of a hybrid ammonia production scheme capable of generating both blue and green ammonia within a single flexible framework. The proposed configuration couples a conventional methane-based synthesis loop equipped with a high-efficiency CO₂ capture system to a parallel renewable-driven synthesis loop, thereby enabling dynamic operation across fossil-based and renewable feedstocks. Process simulations demonstrate that, under a natural gas feed of 1660 metric tons per day (MTPD), the hybrid plant achieves a fuel-to-feedstock ratio of approximately 49 % and an overall CO₂ capture efficiency of up to 98.5 %. Captured CO₂ is compressed to 60 bar, allowing downstream utilization for enhanced oil recovery (EOR) or export via pipeline infrastructure. Beyond process performance, the study highlights the potential role of hybrid ammonia in supporting large-scale decarbonization strategies, strengthening energy security, and bridging the technological gap between blue hydrogen and renewable hydrogen production. In particular, the approach aligns with ongoing energy transition initiatives such as Germany’s Energiewende while offering a scalable solution for global low-carbon fuel supply chains.
{"title":"A dual-route ammonia process: Combining renewable and low-carbon pathways","authors":"Amin Soleimani Mehr , Günter Scheffknecht , Reihaneh Zohourian , Jörg Maier , Markus Reinmoeller","doi":"10.1016/j.dche.2025.100282","DOIUrl":"10.1016/j.dche.2025.100282","url":null,"abstract":"<div><div>Ammonia is increasingly recognized as a versatile hydrogen carrier and energy vector with the potential to decarbonize industrial processes and global energy trade. Among the emerging production routes, blue ammonia—derived from natural gas with integrated carbon capture and storage (CCS)—offers a transitional pathway toward low-carbon energy systems. This work presents the development and assessment of a hybrid ammonia production scheme capable of generating both blue and green ammonia within a single flexible framework. The proposed configuration couples a conventional methane-based synthesis loop equipped with a high-efficiency CO₂ capture system to a parallel renewable-driven synthesis loop, thereby enabling dynamic operation across fossil-based and renewable feedstocks. Process simulations demonstrate that, under a natural gas feed of 1660 metric tons per day (MTPD), the hybrid plant achieves a fuel-to-feedstock ratio of approximately 49 % and an overall CO₂ capture efficiency of up to 98.5 %. Captured CO₂ is compressed to 60 bar, allowing downstream utilization for enhanced oil recovery (EOR) or export via pipeline infrastructure. Beyond process performance, the study highlights the potential role of hybrid ammonia in supporting large-scale decarbonization strategies, strengthening energy security, and bridging the technological gap between blue hydrogen and renewable hydrogen production. In particular, the approach aligns with ongoing energy transition initiatives such as Germany’s Energiewende while offering a scalable solution for global low-carbon fuel supply chains.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100282"},"PeriodicalIF":4.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.dche.2025.100283
Tossapon Katongtung , Nattawut Khuenkaeo , Yuttana Mona , Pana Suttakul , James C. Moran , Korrakot Y. Tippayawong , Nakorn Tippayawong
Dimensionality reduction plays a critical role in efficiently managing large and complex datasets in machine learning (ML) applications. This study presents an innovative integration of principal component analysis (PCA) and extreme gradient boosting (XGB) to model the hydrothermal carbonization (HTC) process. PCA effectively reduced the feature space from 18 to 9 principal components with minimal impact on model accuracy (R² decreased slightly from 0.8900 to 0.8480), significantly simplifying the model complexity. To enhance interpretability, one- and two-dimensional partial dependence plots (PDP) were employed, revealing key features and their interactions influencing HTC outcomes. This combined approach not only improves predictive performance but also provides meaningful insights into the underlying process variables, addressing common challenges of ML opacity. While the model demonstrates strong predictive capability, further experimental validation and extension to diverse biomass types are recommended to confirm practical applicability and enhance versatility. The proposed methodology offers a robust, interpretable, and computationally efficient framework for optimizing HTC and can guide future research involving high-dimensional datasets.
{"title":"Data driven prediction of hydrochar yields from biomass hydrothermal carbonization using extreme gradient boosting algorithm with principal component analysis","authors":"Tossapon Katongtung , Nattawut Khuenkaeo , Yuttana Mona , Pana Suttakul , James C. Moran , Korrakot Y. Tippayawong , Nakorn Tippayawong","doi":"10.1016/j.dche.2025.100283","DOIUrl":"10.1016/j.dche.2025.100283","url":null,"abstract":"<div><div>Dimensionality reduction plays a critical role in efficiently managing large and complex datasets in machine learning (ML) applications. This study presents an innovative integration of principal component analysis (PCA) and extreme gradient boosting (XGB) to model the hydrothermal carbonization (HTC) process. PCA effectively reduced the feature space from 18 to 9 principal components with minimal impact on model accuracy (R² decreased slightly from 0.8900 to 0.8480), significantly simplifying the model complexity. To enhance interpretability, one- and two-dimensional partial dependence plots (PDP) were employed, revealing key features and their interactions influencing HTC outcomes. This combined approach not only improves predictive performance but also provides meaningful insights into the underlying process variables, addressing common challenges of ML opacity. While the model demonstrates strong predictive capability, further experimental validation and extension to diverse biomass types are recommended to confirm practical applicability and enhance versatility. The proposed methodology offers a robust, interpretable, and computationally efficient framework for optimizing HTC and can guide future research involving high-dimensional datasets.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100283"},"PeriodicalIF":4.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Late-stage development of complex chemical processes presents significant challenges due to the high dimensionality and interactions of operating parameters. This complexity renders traditional factorial experimental designs impractical. Consequently, there is often a default reliance on suboptimal legacy technologies, which can lead to reduced overall performance and a larger environmental footprint. This work introduces a novel integrated methodology for combined process and product attribute screening specifically designed to overcome these limitations. The approach strategically integrates expert knowledge, high-fidelity first-principle modeling, and data mining techniques to accelerate the generation of critical process understanding. This supports the confident adoption of sustainable high-performance manufacturing routes. The sequential framework begins with expert knowledge to define promising technological pathways, which are then modeled using first-principle approaches, potentially enhanced by contemporary Artificial Intelligence (AI) techniques. Afterward, extensive parametric optimizations are performed, generating rich synthetic datasets. These data are then subjected to data mining algorithms for pattern recognition, identification of different clusters of the operational regime, and estimation of key product properties. The effectiveness of this methodology is demonstrated through a challenging case study that focuses on the crystallization of conglomerates, which combines deracemization and particle formation, steps traditionally performed sequentially with associated inefficiencies. Our analysis reveals that optimal operations form 12 distinct clusters within which the expected product properties can vary considerably. A key finding is that incorporating data from a strategically designed preliminary experiment enables the exclusion of difficult-to-measure material-specific parameters and enhances the cluster classification and product property estimation.
{"title":"Early-stage chemical process screening through hybrid modeling: Introduction and case study of a reaction–crystallization process","authors":"Diana Wiederschitz , Edith-Alice Kovacs , Botond Szilagyi","doi":"10.1016/j.dche.2025.100280","DOIUrl":"10.1016/j.dche.2025.100280","url":null,"abstract":"<div><div>Late-stage development of complex chemical processes presents significant challenges due to the high dimensionality and interactions of operating parameters. This complexity renders traditional factorial experimental designs impractical. Consequently, there is often a default reliance on suboptimal legacy technologies, which can lead to reduced overall performance and a larger environmental footprint. This work introduces a novel integrated methodology for combined process and product attribute screening specifically designed to overcome these limitations. The approach strategically integrates expert knowledge, high-fidelity first-principle modeling, and data mining techniques to accelerate the generation of critical process understanding. This supports the confident adoption of sustainable high-performance manufacturing routes. The sequential framework begins with expert knowledge to define promising technological pathways, which are then modeled using first-principle approaches, potentially enhanced by contemporary Artificial Intelligence (AI) techniques. Afterward, extensive parametric optimizations are performed, generating rich synthetic datasets. These data are then subjected to data mining algorithms for pattern recognition, identification of different clusters of the operational regime, and estimation of key product properties. The effectiveness of this methodology is demonstrated through a challenging case study that focuses on the crystallization of conglomerates, which combines deracemization and particle formation, steps traditionally performed sequentially with associated inefficiencies. Our analysis reveals that optimal operations form 12 distinct clusters within which the expected product properties can vary considerably. A key finding is that incorporating data from a strategically designed preliminary experiment enables the exclusion of difficult-to-measure material-specific parameters and enhances the cluster classification and product property estimation.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100280"},"PeriodicalIF":4.1,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dche.2025.100279
Shilpa Narasimhan , Nael H. El-Farra , Matthew J. Ellis
Control-enabled cyberattack detection approaches are necessary for enhancing the cybersecurity of process control systems (PCSs), as evidenced by recent successful cyberattacks against these systems. One type of cyberattack is false data injection attacks (FDIAs), which manipulate data over sensor-controller and/or controller–actuator communication links. This work presents an active detection strategy based on control mode switching, where the control parameters and/or the set-point are adjusted to induce perturbations that reveal stealthy FDIAs which would otherwise go undetected. To guarantee attack detection, the perturbations introduced by the detection method must be “attack-revealing”, a concept formally defined using reachability analysis in this work. Building on this foundation and considering a specific class of FDIAs, a screening algorithm is developed for selecting control modes that guarantee attack-revealing perturbations in the presence of an attack. A theoretical result is established, identifying control modes incapable of guaranteeing attack detection for a subset of these attacks—specifically, non-bias adding attacks, which do not cause a steady-state offset. This result simplifies the screening process by reducing the candidate control mode set and ensuring that only effective control modes are considered. The applicability of the screening algorithm is demonstrated for several FDIAs, including: (1) multiplicative attacks, (2) non-bias adding multiplicative attacks, and (3) replay attacks, where historic process data is injected into communication channels. The simulation results on an illustrative process validate the effectiveness of the modified screening algorithm and the active detection method in detecting non-biased additive and multiplicative replay attacks.
{"title":"Control mode switching for guaranteed detection of false data injection attacks on process control systems","authors":"Shilpa Narasimhan , Nael H. El-Farra , Matthew J. Ellis","doi":"10.1016/j.dche.2025.100279","DOIUrl":"10.1016/j.dche.2025.100279","url":null,"abstract":"<div><div>Control-enabled cyberattack detection approaches are necessary for enhancing the cybersecurity of process control systems (PCSs), as evidenced by recent successful cyberattacks against these systems. One type of cyberattack is false data injection attacks (FDIAs), which manipulate data over sensor-controller and/or controller–actuator communication links. This work presents an active detection strategy based on control mode switching, where the control parameters and/or the set-point are adjusted to induce perturbations that reveal stealthy FDIAs which would otherwise go undetected. To guarantee attack detection, the perturbations introduced by the detection method must be “attack-revealing”, a concept formally defined using reachability analysis in this work. Building on this foundation and considering a specific class of FDIAs, a screening algorithm is developed for selecting control modes that guarantee attack-revealing perturbations in the presence of an attack. A theoretical result is established, identifying control modes incapable of guaranteeing attack detection for a subset of these attacks—specifically, non-bias adding attacks, which do not cause a steady-state offset. This result simplifies the screening process by reducing the candidate control mode set and ensuring that only effective control modes are considered. The applicability of the screening algorithm is demonstrated for several FDIAs, including: (1) multiplicative attacks, (2) non-bias adding multiplicative attacks, and (3) replay attacks, where historic process data is injected into communication channels. The simulation results on an illustrative process validate the effectiveness of the modified screening algorithm and the active detection method in detecting non-biased additive and multiplicative replay attacks.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100279"},"PeriodicalIF":4.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.dche.2025.100277
Arthur Khodaverdian , Xiaodong Cui , Panagiotis D. Christofides
This work explores the implementation of reinforcement learning (RL)-based approaches to replace model predictive control (MPC) in cases where practical implementations of MPC are infeasible due to excessive computation times. Specifically, with the use of externally enforced stability guarantees, an RL-based controller that is trained to optimize the same cost function as the MPC with a long horizon that achieves the desirable closed-loop performance can serve as a potentially more appealing real-time option as opposed to using the same MPC with a shorter horizon. A benchmark nonlinear chemical process model is used to demonstrate the feasibility of this RL-based framework that simultaneously guarantees stability and enables improvements in computational efficiency and potential control quality of the closed-loop system. To explore the influence of the RL training method, two RL algorithms are explored, with one imitation learning method used as a reference.
{"title":"Utilizing reinforcement learning in feedback control of nonlinear processes with stability guarantees","authors":"Arthur Khodaverdian , Xiaodong Cui , Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100277","DOIUrl":"10.1016/j.dche.2025.100277","url":null,"abstract":"<div><div>This work explores the implementation of reinforcement learning (RL)-based approaches to replace model predictive control (MPC) in cases where practical implementations of MPC are infeasible due to excessive computation times. Specifically, with the use of externally enforced stability guarantees, an RL-based controller that is trained to optimize the same cost function as the MPC with a long horizon that achieves the desirable closed-loop performance can serve as a potentially more appealing real-time option as opposed to using the same MPC with a shorter horizon. A benchmark nonlinear chemical process model is used to demonstrate the feasibility of this RL-based framework that simultaneously guarantees stability and enables improvements in computational efficiency and potential control quality of the closed-loop system. To explore the influence of the RL training method, two RL algorithms are explored, with one imitation learning method used as a reference.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100277"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.dche.2025.100278
José Pedreira , José Pinto , Daniel Gonçalves , Pedro Barahona , Rui Oliveira , Rafael S. Costa
Hybrid modeling is gaining prominence in various industrial sectors because it offers a flexible balance between mechanistic and data-driven modeling. However, the adoption of such hybrid modeling techniques has been rather limited. Only few expert researchers using in-house tools have technical background and skills to develop such hybrid models worldwide. Additionally, freely available and user-friendly software tools for developing hybrid models in bioprocesses and biological systems are lacking.
To address these gaps, we developed HYBpy. HYBpy is a user-friendly web-based framework based on a generalized step-by-step pipeline for quick and easy generation/training of hybrid models compliant with current file formats. We demonstrated the HYBpy functionalities using two literature case studies in the biological engineering domain. HYBpy is expected to greatly facilitate the usage of hybrid modeling, making these approaches accessible for the nonexpert community.
Availability: HYBpy and two case examples can be accessed online at www.hybpy.com. Although HYBpy is offered as a web-based tool, it can also be installed locally as described in the GitHub repository instructions. The source code is hosted and publicly available on GitHub at https://github.com/joko1712/HYBpy under the GNU General Public License v3.0.
{"title":"HYBpy: A web-based framework for hybrid modeling of biological systems","authors":"José Pedreira , José Pinto , Daniel Gonçalves , Pedro Barahona , Rui Oliveira , Rafael S. Costa","doi":"10.1016/j.dche.2025.100278","DOIUrl":"10.1016/j.dche.2025.100278","url":null,"abstract":"<div><div>Hybrid modeling is gaining prominence in various industrial sectors because it offers a flexible balance between mechanistic and data-driven modeling. However, the adoption of such hybrid modeling techniques has been rather limited. Only few expert researchers using in-house tools have technical background and skills to develop such hybrid models worldwide. Additionally, freely available and user-friendly software tools for developing hybrid models in bioprocesses and biological systems are lacking.</div><div>To address these gaps, we developed HYBpy. HYBpy is a user-friendly web-based framework based on a generalized step-by-step pipeline for quick and easy generation/training of hybrid models compliant with current file formats. We demonstrated the HYBpy functionalities using two literature case studies in the biological engineering domain. HYBpy is expected to greatly facilitate the usage of hybrid modeling, making these approaches accessible for the nonexpert community.</div><div>Availability: HYBpy and two case examples can be accessed online at <span><span>www.hybpy.com</span><svg><path></path></svg></span>. Although HYBpy is offered as a web-based tool, it can also be installed locally as described in the GitHub repository instructions. The source code is hosted and publicly available on GitHub at <span><span>https://github.com/joko1712/HYBpy</span><svg><path></path></svg></span> under the GNU General Public License v3.0.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100278"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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