Pub Date : 2026-03-01Epub 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":"2026-03-01","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}
This work presents the design space determination of freezing processes for human induced pluripotent stem (hiPS) cell-derived spheroids using hybrid modeling. First, a mechanistic model was developed considering spheroid characteristics and freezing phenomena. The developed model was then extended to calculate the cell recovery ratio after thawing using statistical modeling. The hybrid model parameters were estimated based on freezing experiments using hiPS cell-derived spheroids. The extension enabled the calculation of the cell recovery ratio and the number of living cells in the spheroid after thawing, as a function of the cooling rate and the average radius of spheroids. The application of the hybrid model was demonstrated in a case study. The design space of the freezing process was obtained under given constraints related to both quality and productivity. In this way, we could determine design spaces considering both quality and productivity toward industrial manufacturing of hiPS cell-derived spheroids.
{"title":"Design space determination of freezing processes for human induced pluripotent stem cell-derived spheroids using hybrid modeling","authors":"Masaharu Fujioka , Yusuke Hayashi , Yuta Yamaguchi , Tetsuya Fujii , Hirokazu Sugiyama","doi":"10.1016/j.dche.2026.100294","DOIUrl":"10.1016/j.dche.2026.100294","url":null,"abstract":"<div><div>This work presents the design space determination of freezing processes for human induced pluripotent stem (hiPS) cell-derived spheroids using hybrid modeling. First, a mechanistic model was developed considering spheroid characteristics and freezing phenomena. The developed model was then extended to calculate the cell recovery ratio after thawing using statistical modeling. The hybrid model parameters were estimated based on freezing experiments using hiPS cell-derived spheroids. The extension enabled the calculation of the cell recovery ratio and the number of living cells in the spheroid after thawing, as a function of the cooling rate and the average radius of spheroids. The application of the hybrid model was demonstrated in a case study. The design space of the freezing process was obtained under given constraints related to both quality and productivity. In this way, we could determine design spaces considering both quality and productivity toward industrial manufacturing of hiPS cell-derived spheroids.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100294"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385202","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 : 2026-03-01Epub Date: 2026-02-26DOI: 10.1016/j.dche.2026.100298
Mohammad Zaid Kamil , Dongyang Qiu , Mohammad Alauddin , Paul Amyotte
This study introduces a two-phase data-driven multi-model framework to automate the holistic understanding of dust explosions prevention through critical safety drivers. The proposed framework integrates the Natural Language Processing (NLP), Self-Organizing Maps (SOM), and Bayesian Networks (BN) to identify and analyze critical safety drivers. The framework analyzes unstructured incident reports from the Chemical Safety and Hazard Investigation Board (CSB) and WorkSafeBC (WBC) databases using NLP, transforming textual data into actionable insights for proactive safety management utilizing SOM and BN models. Eight critical safety drivers—including process safety management, inherently safer design (ISD), ignition source control, and safeguard effectiveness—are identified and prioritized through sensitivity analysis. By embedding these insights into process operation, the methodology supports Safety 5.0, enabling predictive interventions and reducing the likelihood of catastrophic events.
{"title":"Data-driven safety analytics for dust explosion prevention","authors":"Mohammad Zaid Kamil , Dongyang Qiu , Mohammad Alauddin , Paul Amyotte","doi":"10.1016/j.dche.2026.100298","DOIUrl":"10.1016/j.dche.2026.100298","url":null,"abstract":"<div><div>This study introduces a two-phase data-driven multi-model framework to automate the holistic understanding of dust explosions prevention through critical safety drivers. The proposed framework integrates the Natural Language Processing (NLP), Self-Organizing Maps (SOM), and Bayesian Networks (BN) to identify and analyze critical safety drivers. The framework analyzes unstructured incident reports from the Chemical Safety and Hazard Investigation Board (CSB) and WorkSafeBC (WBC) databases using NLP, transforming textual data into actionable insights for proactive safety management utilizing SOM and BN models. Eight critical safety drivers—including process safety management, inherently safer design (ISD), ignition source control, and safeguard effectiveness—are identified and prioritized through sensitivity analysis. By embedding these insights into process operation, the methodology supports Safety 5.0, enabling predictive interventions and reducing the likelihood of catastrophic events.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100298"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385205","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 : 2026-03-01Epub 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":"2026-03-01","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 : 2026-03-01Epub 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":"2026-03-01","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 : 2026-03-01Epub 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":"2026-03-01","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 : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.dche.2026.100293
Philipp Müller , Oleg Špakov , Jarmo Verho , Timo Salpavaara , Mariaana Savia , Atte Sinkkonen , Venla Kamppari , Jussi Rantala , Pasi Kallio , Veikko Surakka
Recreating odors is considerably more challenging than, for example, recreating colors. Odor recreation necessitates knowledge of chemistry, sensory perception, sensors, automation, artificial intelligence, and machine learning. In general, recreating an odor requires analyzing its chemical composition by means of gas chromatography and/or mass spectrometry. However, odors can consist of hundreds of chemicals, which makes recreating them a complex, time-consuming and expensive task. Alternatively, one could try to create synthetic odors from a limited number of chemicals and compare them to the original odor. For the comparison, original and synthetic odors could be measured by an electronic nose, such as differential mobility spectrometry (DMS). In this article, a self-learning multichannel olfactory display that enables recreation of a target odor by mixing up to five chemicals was developed and tested. The display incorporates a machine learning module based on differential evolution algorithms for automatically finding a composition of a synthetic odor yielding a DMS measurement similar to the DMS measurement of the target odor. The article demonstrates via simulations and tests with real odors that the self-learning multichannel odor display can find synthetic odors resembling the target odors. Simulations proved that the selected algorithm is capable of yielding synthetic odors almost identical to the target odor. For two-component mixtures, the average deviations in flow rates between the target and trial odors were 0.8 sccm after ten and 0.3 sccm after 20 iterations, while for five-component mixtures the average deviations were 2.8 sccm after ten and 1.4 sccm after 20 iterations. Tests with isopropanol–ethanol mixtures also resulted in accurately reproduced synthetic mixtures but demonstrated challenges due to the unequal chemical imprints in DMS measurements. For ethanol, the average offset in the estimated flow rate was 2.2 sccm, while it was 7.0 sccm for isopropanol due to two runs with large offsets of 15 and 16 sccm respectively.
{"title":"Self-learning multichannel olfactory display","authors":"Philipp Müller , Oleg Špakov , Jarmo Verho , Timo Salpavaara , Mariaana Savia , Atte Sinkkonen , Venla Kamppari , Jussi Rantala , Pasi Kallio , Veikko Surakka","doi":"10.1016/j.dche.2026.100293","DOIUrl":"10.1016/j.dche.2026.100293","url":null,"abstract":"<div><div>Recreating odors is considerably more challenging than, for example, recreating colors. Odor recreation necessitates knowledge of chemistry, sensory perception, sensors, automation, artificial intelligence, and machine learning. In general, recreating an odor requires analyzing its chemical composition by means of gas chromatography and/or mass spectrometry. However, odors can consist of hundreds of chemicals, which makes recreating them a complex, time-consuming and expensive task. Alternatively, one could try to create synthetic odors from a limited number of chemicals and compare them to the original odor. For the comparison, original and synthetic odors could be measured by an electronic nose, such as differential mobility spectrometry (DMS). In this article, a self-learning multichannel olfactory display that enables recreation of a target odor by mixing up to five chemicals was developed and tested. The display incorporates a machine learning module based on differential evolution algorithms for automatically finding a composition of a synthetic odor yielding a DMS measurement similar to the DMS measurement of the target odor. The article demonstrates via simulations and tests with real odors that the self-learning multichannel odor display can find synthetic odors resembling the target odors. Simulations proved that the selected algorithm is capable of yielding synthetic odors almost identical to the target odor. For two-component mixtures, the average deviations in flow rates between the target and trial odors were 0.8<!--> <!-->sccm after ten and 0.3<!--> <!-->sccm after 20 iterations, while for five-component mixtures the average deviations were 2.8<!--> <!-->sccm after ten and 1.4<!--> <!-->sccm after 20 iterations. Tests with isopropanol–ethanol mixtures also resulted in accurately reproduced synthetic mixtures but demonstrated challenges due to the unequal chemical imprints in DMS measurements. For ethanol, the average offset in the estimated flow rate was 2.2<!--> <!-->sccm, while it was 7.0<!--> <!-->sccm for isopropanol due to two runs with large offsets of 15 and 16<!--> <!-->sccm respectively.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100293"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385095","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 : 2026-03-01Epub Date: 2026-01-19DOI: 10.1016/j.dche.2026.100289
Balázs Fricz , Gergely Horváth , Alex Kummer
In the chemical process industry, the frequent measurement of quality variables is not always a viable path to take. It can be due to the measurement being time- or cost-consuming, leading to a sampling time of hours or even a day. As such, soft sensors were developed as a solution to this problem, providing a model trained on the available output measurements and predicting its value between samplings. This paper presents the process of soft sensor development for an industrial case study and the application of recently developed Kolmogorov–Arnold Networks (KAN) as a possible soft sensor model, where also the inherent interpretability of KAN through symbolic regression is analyzed.
The soft-sensor development and interpretability analysis is performed on a benchmark dataset (steam turbine dataset), and on a real industrial case study (aniline synthesis plant). The prediction performance of KAN is compared to different complexity ML models, and in addition, the models were compared on the basis of their interpretability and explainability using Shapley values. The results show that the inherent interpretability of KAN models using symbolic regression is too complicated on simple tasks, thus losing its ability to explain the model estimations, though its model performance is similar to the classic feedforward neural networks.
{"title":"Kolmogorov–Arnold and deep learning networks for industrial explainable product quality prediction","authors":"Balázs Fricz , Gergely Horváth , Alex Kummer","doi":"10.1016/j.dche.2026.100289","DOIUrl":"10.1016/j.dche.2026.100289","url":null,"abstract":"<div><div>In the chemical process industry, the frequent measurement of quality variables is not always a viable path to take. It can be due to the measurement being time- or cost-consuming, leading to a sampling time of hours or even a day. As such, soft sensors were developed as a solution to this problem, providing a model trained on the available output measurements and predicting its value between samplings. This paper presents the process of soft sensor development for an industrial case study and the application of recently developed Kolmogorov–Arnold Networks (KAN) as a possible soft sensor model, where also the inherent interpretability of KAN through symbolic regression is analyzed.</div><div>The soft-sensor development and interpretability analysis is performed on a benchmark dataset (steam turbine dataset), and on a real industrial case study (aniline synthesis plant). The prediction performance of KAN is compared to different complexity ML models, and in addition, the models were compared on the basis of their interpretability and explainability using Shapley values. The results show that the inherent interpretability of KAN models using symbolic regression is too complicated on simple tasks, thus losing its ability to explain the model estimations, though its model performance is similar to the classic feedforward neural networks.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"18 ","pages":"Article 100289"},"PeriodicalIF":4.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022558","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 : 2026-03-01Epub Date: 2025-11-13DOI: 10.1016/j.dche.2025.100275
Mohd Fauzi Zanil , Zainal Ahmad , Syamsul Rizal Abd Shukor , Mohmmad Jakir Hossain Khan , Mohd Hardyianto Vai Bahrun
This study proposes the optimal development of rule bases using Type-2 fuzzy logic specifically designed for stochastic chemical control systems. The research addresses complexities and uncertainties inherent in stochastic pH neutralisation processes with Type-2 fuzzy logic as inversed hybrid model which able to provide good control action in the fuzzy rule. Comprehensive simulation and experimental-based performance evaluations, including setpoint tracking accuracy and disturbance rejection capabilities, were conducted to rigorously compare the proposed Type-2 fuzzy logic controller with traditional PID and conventional fuzzy logic controllers. Results demonstrate that the optimized Type-2 fuzzy logic controller significantly outperforms existing methods, offering faster system responses, minimized overshoot, and improved system stability. Further, robustness tests involving stochastic perturbations, such as variable flow rates of NaOH and HCl solutions and random acid injections during operational conditions, confirm the controller’s enhanced adaptability and effectiveness. The study concludes that the developed Type-2 fuzzy logic controller provides a robust, efficient, and reliable control solution constructed through simulation and validated using real experimental data, suitable for real-time (stochastic) management of complex stochastic chemical systems.
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Pub Date : 2026-03-01Epub 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":"2026-03-01","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}
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