Digital Chemical Engineering最新文献

{"title":"Early-stage chemical process screening through hybrid modeling: Introduction and case study of a reaction–crystallization process","authors":"Diana Wiederschitz ,&nbsp;Edith-Alice Kovacs ,&nbsp;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":"2026-03-01","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}

{"title":"Implementation of mixed reality for data visualization in liquid soap filling processes","authors":"Andrés Felipe Hurtado,&nbsp;Carlos Mario Paredes,&nbsp;Kelly Daniella Marín Montealegre,&nbsp;Juan Pablo González Molina,&nbsp;Juan Pablo Álzate Saiz","doi":"10.1016/j.dche.2025.100254","DOIUrl":"10.1016/j.dche.2025.100254","url":null,"abstract":"<div><div>Visualizing data in industrial processes represents a critical component of Industry 4.0, offering opportunities for better decision-making, real-time monitoring, and process optimization. This article presents an architecture design that enables data capture from control technologies and integrates into a Mixed Reality (MR) system for data visualization in the context of liquid soap filling processes. The system integrates real-time process data from a programmable logic controller (PLC) into MR technology, thereby creating an immersive platform that serves to enhance understanding and interaction with operational metrics. This was structured into five distinct phases. Initially, an analysis of the filling line was performed to determine the data sources and user requirements. Subsequently, immersive technologies that would facilitate hands-free interaction, spatial mapping, and integration of digital data with the physical environment were evaluated. The third phase entailed the implementation of the PLC-MR data integration via a custom API. The fourth phase involved iterative refinements, informed by hands-on feedback from prototype trials. Finally, a usability evaluation was conducted to ensure the effectiveness and user-friendliness of the developed solution. A validation with seventeen operators of the industrial filling system confirmed that the system provides a clear and intuitive view of the filling process. When the interaction time of the proposed platform was evaluated, it was found that it was improved compared to the traditional method of visualization through the HMI. This position the interface as a viable reference for future industrial MR applications. The results of this study underscore the potential of MR as a transformative tool for industrial data visualization.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100254"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010708","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}

{"title":"Soft-measuring method of iron ore sintering process using transient model","authors":"Yoshinari Hashimoto ,&nbsp;Satoki Yasuhara ,&nbsp;Yuji Iwami","doi":"10.1016/j.dche.2025.100268","DOIUrl":"10.1016/j.dche.2025.100268","url":null,"abstract":"<div><div>To achieve efficient sintering machine operation in the steel industry, we developed an online soft-measuring method that can visualize the temperature distribution in the sintering process using a two-dimensional (2D) transient model. Although various numerical simulation models of the sintering process have been proposed, the conventional models suffer from estimation errors caused by unmeasurable disturbances, such as the fluctuations in raw material characteristics, when these models are applied for online control in actual plants over a long period. In this study, to reduce the estimation errors, the model parameters were adjusted successively by moving horizon estimation (MHE), considering the effects of the disturbances. The validation results with actual plant data showed that the estimation errors of the burn rising point (BRP) and the exhaust gas compositions were reduced significantly by MHE. In particular, the root mean square error (RMSE) of the BRP estimation was only 1.48 m. In addition, a correlation was confirmed between the estimated high-temperature holding time of the material and the product yield. The developed soft-measuring method is beneficial for process automation to improve product yield.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100268"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267047","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}

{"title":"Next-generation thermal spray coatings for military use: Innovations, challenges, and applications (bibliometric review 2015–2025)","authors":"Agus Nugroho ,&nbsp;Sarbani Daud ,&nbsp;Prabowo Puranto ,&nbsp;Rizalman Mamat ,&nbsp;Zhang Bo ,&nbsp;Mohd Fairusham Ghazali","doi":"10.1016/j.dche.2025.100259","DOIUrl":"10.1016/j.dche.2025.100259","url":null,"abstract":"<div><div>This study presents a comprehensive bibliometric and thematic analysis of 743 research articles published between 2015 and 2025 on thermal spray coatings for military applications. Advanced bibliometric tools visualized co-authorship networks, keyword evolution, and citation clusters, mapping research trajectories and material-process innovations. The review highlights significant advancements aimed at improving wear resistance, corrosion protection, and thermal stability under extreme conditions. Research on nanostructured and multifunctional coatings has increased by over 45 % in the past five years, addressing needs for electromagnetic shielding, stealth, and biological functions. Publication trends closely correlate with global defense modernization and geopolitical tensions, emphasizing the strategic importance of these materials. While plasma spraying and high-velocity oxygen fuel (HVOF) dominate, emerging eco-friendly spray techniques and AI-assisted designs constitute fewer than 10 % of studies, indicating future research opportunities. Key gaps include real-time in-situ diagnostics and sustainability-focused coatings. This work provides strategic, actionable insights for defense-oriented surface engineering, facilitating the lab-to-field transition and guiding researchers, engineers, and strategists in advancing next-generation military-grade thermal spray coatings.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100259"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050157","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}

{"title":"Integration of sustainability assessment into early-stage carbon capture process design with an explainable AI framework","authors":"Xin Yee Tai ,&nbsp;Oliver Fisher ,&nbsp;Lei Xing ,&nbsp;Jin Xuan","doi":"10.1016/j.dche.2025.100265","DOIUrl":"10.1016/j.dche.2025.100265","url":null,"abstract":"<div><div>This study introduces a novel framework for reducing environmental impacts by optimising operating conditions using a surrogate modelling approach integrated with Explainable AI (XAI). Two surrogate models were developed: a sequential surrogate model (SSM) with a two-step structure, and a direct surrogate model (DSM) with a single-step architecture. Both were trained on data from a validated physics-based simulation of a monoethanolamine (MEA)-based carbon capture process to predict environmental impacts across human health, ecosystem quality, and resource depletion. SHapley Additive exPlanations (SHAP) were used to enhance transparency by identifying key input variables influencing outcomes. Multi-objective optimisation was conducted using Particle Swarm Optimisation (PSO) and NSGA-II to determine optimal operating conditions. DSM achieved high prediction accuracy (R² up to 0.995) and lower errors, while SSM offered better interpretability and broader exploration of Pareto-optimal solutions. This study also shows that our framework identified optimum parameters that reduced environmental impacts by 76–88 % compared with the experiment optimum. This framework supports sustainable process design by combining interpretability, predictive performance, and computational efficiency.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100265"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007597","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}

{"title":"CFD and OCT-based optimisation of impeller-induced shear stress on membrane surfaces in a circular test cell","authors":"Masoud Haghshenasfard ,&nbsp;Arthur Leon ,&nbsp;Robin Starke ,&nbsp;Steffi Drescher ,&nbsp;Uli Klümper ,&nbsp;Thomas Berendonk ,&nbsp;Kristin Kerst ,&nbsp;André Lerch","doi":"10.1016/j.dche.2025.100267","DOIUrl":"10.1016/j.dche.2025.100267","url":null,"abstract":"<div><div>This study investigates the distribution of shear stress in a lab-scale membrane bioreactor consisting of a 56 mm-diameter cylindrical test cell, a 0.25 mm-thick polyethersulfone membrane, and a centrally mounted 35 mm rotating impeller. Computational Fluid Dynamics (CFD) simulations were used to examine how impeller speed and geometry affect wall shear stress across the membrane surface. Higher rotational speeds significantly increased shear stress, with the highest levels observed near the impeller rim and a marked decline beyond a radial distance of 0.0175 m due to wall-induced flow dampening. To validate CFD predictions, Optical Coherence Tomography (OCT) was employed for in-situ, real-time biofilm monitoring. OCT results confirmed that low-shear regions—particularly at the membrane periphery—were more prone to rapid and extensive biofilm accumulation, whereas high-shear areas exhibited delayed or reduced fouling. To improve shear distribution and minimize localized fouling, a multi-objective optimization was performed using response surface methodology. This led to an enhanced impeller design that promoted more uniform and effective shear coverage across the membrane. The integration of CFD modeling, experimental validation, and optimization provides a robust framework for the design of membrane systems with improved anti-fouling performance and operational stability.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100267"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221307","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}

{"title":"Towards robust fault detection for industrial processes with a hybrid feature fusion and ensemble learning framework","authors":"Abid Aman,&nbsp;Yiqi Liu,&nbsp;Yan Chen","doi":"10.1016/j.dche.2025.100270","DOIUrl":"10.1016/j.dche.2025.100270","url":null,"abstract":"<div><div>Early fault identification and evaluation are crucial to ensure the efficiency, safety, and reliability of the industrial process. With the rapid growth of process data in modern industries, machine learning and data-driven methods have become indispensable for effective process monitoring and fault diagnosis. This study proposes a fault detection framework that effectively leverages feature fusion and ensemble learning to boost monitoring performance under intricate industrial conditions. The proposed method combines Slow Feature Analysis (SFA), Kernel SFA (KSFA), and Dynamic SFA (DSFA) to extract distinctive features that accurately reflect linear, nonlinear, and dynamic changes in process data. Furthermore, independent applications of ensemble learning techniques, such as majority and weighted voting, can further increase the reliability of identifying faults with the help of statistical monitoring metrics. The effectiveness of this approach is confirmed using the Tennessee Eastman (TE) benchmark dataset alongside real-world data from a wastewater treatment facility in Beijing. The study spans simulated and real industrial settings to develop a robust framework for fault detection in dynamic and nonlinear processes. The results show that feature fusion and ensemble learning outperform single-model approaches, offering higher sensitivity and reliability. The framework demonstrates strong potential to reduce false alarms, improve anomaly detection, and enhance both efficiency and safety in industrial operations.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100270"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416789","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}

{"title":"Utilizing reinforcement learning in feedback control of nonlinear processes with stability guarantees","authors":"Arthur Khodaverdian ,&nbsp;Xiaodong Cui ,&nbsp;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}

{"title":"Automated flow pattern classification in multiphase systems using artificial intelligence and capacitance sensing techniques","authors":"Nian Ran ,&nbsp;Fayez M. Al-Alweet ,&nbsp;Richard Allmendinger ,&nbsp;Ahmad Almakhlafi","doi":"10.1016/j.dche.2025.100274","DOIUrl":"10.1016/j.dche.2025.100274","url":null,"abstract":"<div><div>Accurate classification of flow patterns in multiphase systems is pivotal for optimizing fluid transport and enhancing overall system performance. Conventional methods—such as visual inspection, standard video analysis, and high-speed imaging—remain widely used in industrial and laboratory settings. However, these approaches are often constrained by subjective interpretation, limited applicability to non-transparent pipelines, and inconsistent performance under varying operating conditions. To overcome these limitations, this study introduces a novel framework that integrates capacitance sensing with Artificial Intelligence (AI)-driven classification. The proposed methodology employs a one-dimensional Squeeze-and-Excitation Network (1D SENet) to extract and interpret time-series features from raw capacitance signals. Experimental validation demonstrates robust classification accuracies, achieving over 85 % on in-distribution datasets and 71 % on out-of-distribution scenarios—substantially outperforming traditional techniques. These findings underscore the enhanced generalization and reliability of the proposed system. This work establishes a scalable foundation for real-time flow regime monitoring and predictive analytics, offering transformative potential for intelligent fluid management in complex industrial environments.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100274"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578596","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}

{"title":"Predicting the viscosity of hydrogen – methane blends at high pressure for hydrogen transportation and geo-storage: Integration of robust white-box machine learning frameworks","authors":"Saad Alatefi ,&nbsp;Mohamed Riad Youcefi ,&nbsp;Menad Nait Amar ,&nbsp;Hakim Djema","doi":"10.1016/j.dche.2025.100273","DOIUrl":"10.1016/j.dche.2025.100273","url":null,"abstract":"<div><div>The integration of hydrogen into underground storage systems is pivotal for large-scale energy management, often involving blends with methane to leverage existing infrastructure. Accurate viscosity prediction of hydrogen – methane blends under subsurface conditions is essential for optimizing flow assurance and operational safety. Accordingly, this study employs three data-driven models, namely Genetic Expression Programming (GEP), Group Method of Data Handling (GMDH), and Multi-Gene Genetic Programming (MGGP), to predict the viscosity of hydrogen – methane mixtures for transportation and underground storage applications. A comprehensive dataset of 313 experimentally measured values from the literature were utilized to develop and validate the established correlations. The MGGP paradigm emerged as the top performer, achieving a root mean square error (RMSE) of 0.4054 and an R² value of 0.9940, outperforming both GEP and GMDH, as well as prior predictive models. The consistency of the dataset was confirmed using the Leverage approach, ensuring robust predictions. In addition, the Shapley Additive Explanations technique revealed key factors influencing the viscosity predictions, enhancing the interpretability of the best-performing correlation. Furthermore, comparative trend analysis demonstrated the MGGP correlation's superior accuracy and robustness across varying blend compositions and operational conditions. These findings offer a reliable and simple-to-use predictive correlation for engineers and researchers designing hydrogen transport and storage systems, supporting efficient energy storage and the transition to a low-carbon economy.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100273"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473898","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}