Digital Chemical Engineering最新文献

Soft-measuring method of iron ore sintering process using transient model 铁矿石烧结过程瞬态模型软测量方法

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-30 DOI: 10.1016/j.dche.2025.100268

Yoshinari Hashimoto , Satoki Yasuhara , Yuji Iwami

{"title":"Soft-measuring method of iron ore sintering process using transient model","authors":"Yoshinari Hashimoto , Satoki Yasuhara , 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":"2025-09-30","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}

引用次数: 0

CFD and OCT-based optimisation of impeller-induced shear stress on membrane surfaces in a circular test cell 基于CFD和oct的圆形试验池中叶轮引起的膜表面剪切应力优化

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-25 DOI: 10.1016/j.dche.2025.100267

Masoud Haghshenasfard , Arthur Leon , Robin Starke , Steffi Drescher , Uli Klümper , Thomas Berendonk , Kristin Kerst , André Lerch

{"title":"CFD and OCT-based optimisation of impeller-induced shear stress on membrane surfaces in a circular test cell","authors":"Masoud Haghshenasfard , Arthur Leon , Robin Starke , Steffi Drescher , Uli Klümper , Thomas Berendonk , Kristin Kerst , 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":"2025-09-25","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}

引用次数: 0

Integration of sustainability assessment into early-stage carbon capture process design with an explainable AI framework 通过可解释的人工智能框架，将可持续性评估整合到早期碳捕获过程设计中

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-02 DOI: 10.1016/j.dche.2025.100265

Xin Yee Tai , Oliver Fisher , Lei Xing , Jin Xuan

{"title":"Integration of sustainability assessment into early-stage carbon capture process design with an explainable AI framework","authors":"Xin Yee Tai , Oliver Fisher , Lei Xing , 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":"2025-09-02","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}

引用次数: 0

Editorial: Special issue on pioneering digital chemical engineering 社论：关于开创性数字化学工程的特刊

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-01 DOI: 10.1016/j.dche.2025.100255

Jin Xuan , Jinfeng Liu

引用次数: 0

Fault detection using multiscale recursive principal component analysis for chemical process systems 基于多尺度递归主成分分析的化工过程系统故障检测

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-01 DOI: 10.1016/j.dche.2025.100264

Oktorifo Gardiola , Abdulhalim Shah Maulud , Muhammad Nawaz , Nabila Farhana Jamaludin

{"title":"Fault detection using multiscale recursive principal component analysis for chemical process systems","authors":"Oktorifo Gardiola , Abdulhalim Shah Maulud , Muhammad Nawaz , Nabila Farhana Jamaludin","doi":"10.1016/j.dche.2025.100264","DOIUrl":"10.1016/j.dche.2025.100264","url":null,"abstract":"<div><div>Process monitoring is essential for maintaining operational safety and product quality in chemical industries. Although conventional fault detection techniques are widely used, their static nature often leads to high false alarm rates (FAR) and missed detection rates (MDR) under dynamic conditions. To address these limitations, this study proposes a Multiscale Recursive Principal Component Analysis (MSRPCA)-based fault detection framework that combines multiscale signal decomposition with the adaptive capabilities of Recursive PCA (RPCA). The MSRPCA approach isolates process variations across different frequency bands while continuously updating the Principal Component Analysis (PCA) model using a moving window mechanism. This enables real-time adaptability and enhanced noise resistance. The proposed method is validated using the Tennessee Eastman Process (TEP), a widely used benchmark for chemical process monitoring under a range of fault types, including step, drift, and random variation disturbances. Fault detection performance is quantitatively assessed using FAR and MDR metrics across 20 distinct fault scenarios. The results demonstrate that MSRPCA consistently outperforms traditional techniques, significantly reducing false alarms while improving fault detection accuracy. For instance, in Fault 16, the MDR in the Hotelling’s <em>T</em><sup><em>2</em></sup> (<em>T</em><sup><em>2</em></sup>) chart decreased from 70.5 % (PCA) to 10.5 % (MSRPCA), while the FAR in the Squared Prediction Error (SPE) chart dropped from 21.3 % to 0 %. These findings underscore the robustness and effectiveness of MSRPCA for real-time fault detection in complex, time-varying, and noisy industrial environments.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100264"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920153","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}

引用次数: 0

Editorial: Special issue on Emerging Stars in Digital Chemical Engineering 社论：数字化工新星特刊

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-01 DOI: 10.1016/j.dche.2025.100247

Jin Xuan , Jinfeng Liu

引用次数: 0

Root cause identification of fault in hot-rolling process by causal plot 用因果图识别热轧过程故障的根本原因

IF 4.1

Digital Chemical Engineering Pub Date : 2025-08-25 DOI: 10.1016/j.dche.2025.100263

Koichi Fujiwara , Yoshiaki Uchida , Taketsugu Osaka

{"title":"Root cause identification of fault in hot-rolling process by causal plot","authors":"Koichi Fujiwara , Yoshiaki Uchida , Taketsugu Osaka","doi":"10.1016/j.dche.2025.100263","DOIUrl":"10.1016/j.dche.2025.100263","url":null,"abstract":"<div><div>In the steel manufacturing industry, a hot-rolling process produces a thick steel plate from a slab as a batch operation; however, off-spec steel plates are sometimes produced when abnormalities occur during rolling operations. To improve the product yield, it is necessary to appropriately ascertain the root cause of a fault. Because the physicochemical behaviors of the slab during hot-rolling are complicated and yet to be fully understood, we adopted a data-driven approach to identify the cause of the fault in the hot-rolling process. We previously proposed a data-driven fault diagnosis method, referred to as a causal plot, that considers the causal relationships between process variables and monitoring indexes for process monitoring. In the causal plot, monitoring indexes were calculated using existing process monitoring methods, and the causal relationships between the process variables and the calculated monitoring indices were estimated. A linear non-Gaussian acyclic model (LiNGAM) can be adopted for causal inferences between the process variables and calculated monitoring indexes. In this study, we propose a new fault diagnosis method for a batch process, referred to as a b-causal plot, utilizing the causal plot and dynamic time warping (DTW). We analyzed real operation data when defective coils were produced in the hot-rolling process with the proposed b-causal plot and confirmed that the identified root cause was consistent with process engineers’ knowledge, which is typically a low-importance variable that operators do not constantly monitor in daily operation. Because the root cause identification of faults is crucial for maintaining product quality and efficiency in batch processes, the proposed b-causal plot contributes to improving productivity across industries, as demonstrated in this work.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"17 ","pages":"Article 100263"},"PeriodicalIF":4.1,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107110","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}

引用次数: 0

Multi-criteria decision support for flexible dividing wall distillation columns 柔性分壁精馏塔多准则决策支持

IF 4.1

Digital Chemical Engineering Pub Date : 2025-08-19 DOI: 10.1016/j.dche.2025.100258

David Mogalle , Patrick Otto Ludl , Tobias Seidel , Lea Trescher , Thomas Grützner , Michael Bortz

{"title":"Multi-criteria decision support for flexible dividing wall distillation columns","authors":"David Mogalle , Patrick Otto Ludl , Tobias Seidel , Lea Trescher , Thomas Grützner , Michael Bortz","doi":"10.1016/j.dche.2025.100258","DOIUrl":"10.1016/j.dche.2025.100258","url":null,"abstract":"<div><div>When designing a dividing wall column, some decisions regarding the layout of the column cannot be altered once the unit is built, whereas decisions regarding the column’s operation can, to some extent, be adjusted later. During the design phase, both layout and operation can be optimized to achieve an optimal column performance. However, such solutions are tailored to pre-specified process demands. If these demands change later, the physical layout can become suboptimal. Hence, we are interested in design decisions that keep the losses in performance minimal, leading to a column design that is flexible across different demands.</div><div>In this paper, we present a new methodology to measure flexibility. The approach is based on multi-criteria optimization, where Pareto fronts with fixed design variables and optimized operating variables are compared to an ideal Pareto front that optimizes both the layout and the operation simultaneously. The difference between two such fronts, representing the losses in performance of the fixed layout for a wide range of demands, is measured by a novel flexibility indicator. We apply our methodology to designing a dividing wall column separating an example mixture. A fast computation of the corresponding Pareto fronts is achieved by solving the arising optimization problems using a reduction method based on stage-to-stage calculations.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100258"},"PeriodicalIF":4.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907779","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}

引用次数: 0

Neural network implementation of model predictive control with stability guarantees 神经网络实现具有稳定性保证的模型预测控制

IF 4.1

Digital Chemical Engineering Pub Date : 2025-08-18 DOI: 10.1016/j.dche.2025.100262

Arthur Khodaverdian , Dhruv Gohil , Panagiotis D. Christofides

{"title":"Neural network implementation of model predictive control with stability guarantees","authors":"Arthur Khodaverdian , Dhruv Gohil , Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100262","DOIUrl":"10.1016/j.dche.2025.100262","url":null,"abstract":"<div><div>This work explores the use of supervised learning on data generated by a model predictive controller (MPC) to train a neural network (NN). The goal is to create an approximate control policy that can replace the MPC, offering reduced computational complexity while maintaining stability guarantees. Through the use of Lyapunov-based stability constraints, an MPC can be designed to guarantee stability. Once designed, this MPC can be used to generate a dataset of various state-space points and their resulting immediate optimal control actions. With the MPC dataset representing an optimal control policy, an NN is trained to function as a direct substitute for the MPC. The resulting approximate control policy can then be applied in real-time to the process, with stability guarantees being enforced through post-inference validation. If, for a given set of sensor readings, the NN yields control actions that violate the Lyapunov stability constraints used in the MPC, the control action is discarded and replaced with stabilizing control from a fallback stabilizing controller. This control architecture is applied to a benchmark chemical reactor model. Using this model, a comprehensive study of the stability, performance, robustness, and computational burden of the approach is carried out.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100262"},"PeriodicalIF":4.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865569","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}

引用次数: 0

AutoRL framework for bioprocess control: Optimizing reward function, architecture, and hyperparameters 生物过程控制的AutoRL框架：优化奖励函数、结构和超参数

IF 4.1

Digital Chemical Engineering Pub Date : 2025-08-14 DOI: 10.1016/j.dche.2025.100261

D.A. Goulart , R.D. Pereira , F.V. Silva

{"title":"AutoRL framework for bioprocess control: Optimizing reward function, architecture, and hyperparameters","authors":"D.A. Goulart , R.D. Pereira , F.V. Silva","doi":"10.1016/j.dche.2025.100261","DOIUrl":"10.1016/j.dche.2025.100261","url":null,"abstract":"<div><div>This study proposes a structured AutoRL framework for the development of deep reinforcement learning (DRL) controllers in chemical process systems. Focusing on the control of a 3<span><math><mo>×</mo></math></span> 3 MIMO yeast fermentation bioreactor, the methodology jointly optimizes three key internal components of the DRL agent: the reward function, the neural network architecture, and the hyperparameters of the algorithm. A parameterizable logistic reward formulation is introduced to encode control objectives, such as steady-state accuracy, minimalization of actuation effort, and control smoothness, into a flexible and tunable structure. A dual loop optimization strategy combines grid search and Bayesian optimization to systematically explore and refine the agent’s design space. The resulting controller achieved average steady-state errors of 0.009 °C for reactor temperature and 0.19 g/L for ethanol concentration, while maintaining smooth and stable behavior under diverse operational scenarios. By formalizing reward design and integrating it with hyperparameter and architecture optimization, this work delivers a AutoRL methodology for DRL-based control, reducing reliance on expert heuristics and enhancing reproducibility in complex bioprocess applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100261"},"PeriodicalIF":4.1,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895879","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}

引用次数: 0