Computers & Chemical Engineering最新文献

Online alarm flood classification via interpretable template extraction and structured convolutional matching 基于可解释模板提取和结构化卷积匹配的洪水报警在线分类

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-19 DOI: 10.1016/j.compchemeng.2026.109570

Yashar Rahimi , Harikrishna Rao Mohan Rao , Jing Zhou , Tongwen Chen

{"title":"Online alarm flood classification via interpretable template extraction and structured convolutional matching","authors":"Yashar Rahimi , Harikrishna Rao Mohan Rao , Jing Zhou , Tongwen Chen","doi":"10.1016/j.compchemeng.2026.109570","DOIUrl":"10.1016/j.compchemeng.2026.109570","url":null,"abstract":"<div><div>Alarm flood classification in industrial alarm systems is a challenging task due to variability in fault durations, process noise, and the volume of overlapping alarms. However, alarm floods triggered by similar faults often exhibit recurring structural patterns, which, if identified effectively, can support the root cause diagnosis and informed decision-making by operators. Existing classification methods often rely on opaque models, extensive retraining, or lack integration with operator-facing tools. Motivated by this practical problem, a unified visual analytics-based methodology for the real-time classification of alarm floods is proposed in this paper. The contributions are threefold: (1) The existing High-Density Alarm Plot (HDAP) is extended into a structured matrix representation to encode alarm activity over time; (2) a 2D convolution-based alignment technique is developed to extract representative templates from historical alarm floods, enabling category-specific pattern generation; and (3) a dynamic matrix representation is introduced to support real-time alarm monitoring, where similarity matching against pre-learned templates facilitates online classification with minimal delay. The proposed method is interpretable, operator-friendly, and seamlessly integrates with existing visual tools. The effectiveness of the proposed method is validated on the Tennessee Eastman Process benchmark, demonstrating robust and accurate early-stage classification.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109570"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transforming electric vehicle battery recycling network with integrated winner determination for enhanced resilience 基于综合赢家判定的电动汽车电池回收网络改造

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-29 DOI: 10.1016/j.compchemeng.2026.109561

Xuefeng Wang , Qiang Liu , Mingqiang Yin

{"title":"Transforming electric vehicle battery recycling network with integrated winner determination for enhanced resilience","authors":"Xuefeng Wang , Qiang Liu , Mingqiang Yin","doi":"10.1016/j.compchemeng.2026.109561","DOIUrl":"10.1016/j.compchemeng.2026.109561","url":null,"abstract":"<div><div>With the rapid development of the electric vehicle industry, the recycling of retired power batteries has become a critical component for enhancing resource efficiency and supporting low-carbon development. To address supply chain disruption risks caused by electric vehicle battery recycling network design (EVBRND), an optimization framework integrating EVBRND and winner determination (WD) is developed from a fourth-party logistics perspective. This study innovatively integrates the WD approach into EVBRND to achieve a cost-effective balance between proprietary recycling networks and external recyclers under disruption risks. A two-stage stochastic resilient model, incorporating a multi-level backup strategy, is formulated and transformed into a mixed-integer linear programming model. To address the computational complexity caused by a large number of disruption scenarios, a scenario-reduction-based decomposition algorithm is proposed. The scenario reduction algorithm is employed to reduce the number of disruption scenarios and to efficiently obtain an upper bound of the optimal solution of the proposed model. Variable relaxation and Lagrangian relaxation algorithms are used to obtain a lower bound of the optimal solution of the proposed model. To verify the effectiveness of the proposed model and algorithms, a real-world case study is conducted. Numerical analysis results indicate that integrating the WD approach improves recycling network responsiveness and economic efficiency.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109561"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Partial least-squares model adaptation by bootstrap resampling 自举重采样自适应偏最小二乘模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-02-02 DOI: 10.1016/j.compchemeng.2026.109585

Elia Arnese-Feffin , Jinwook Rhyu , Benjamin T. Smith , Chris D. Castro , Jacqueline M. Wolfrum , Stacy L. Springs , Roger A. Hart , Tom Mistretta , Richard D. Braatz

{"title":"Partial least-squares model adaptation by bootstrap resampling","authors":"Elia Arnese-Feffin , Jinwook Rhyu , Benjamin T. Smith , Chris D. Castro , Jacqueline M. Wolfrum , Stacy L. Springs , Roger A. Hart , Tom Mistretta , Richard D. Braatz","doi":"10.1016/j.compchemeng.2026.109585","DOIUrl":"10.1016/j.compchemeng.2026.109585","url":null,"abstract":"<div><div>Soft sensors play a crucial role in biomanufacturing, the prime example being the estimation of product quality attributes using only easy-to-measure variables from the plant instrumentation. Data-driven models, such as partial least-squares regression, are widely used to this end. However, their predictive performance might degrade whenever process conditions shift, even after changes made on purpose by operators. Model adaptation strategies can keep the model up to date, but they tend to under-deliver when the process undergoes abrupt changes. In this study, we propose a novel model adaptation method that exploits knowledge of intentional abrupt changes in the process via bootstrap resampling. Exploiting such knowledge, the available data can be used in an optimal way. We demonstrate our approach on three case studies: a numerical example, a simulated penicillin production process, and an industrial biomanufacturing process. Compared to existing adaptation strategies, our method achieves faster adaptation and better predictive performance in the transition period between old and new process conditions. The proposed approach thus enables practitioners to quickly recover the predictive power of soft sensors, with clear benefits on process operation and product quality.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109585"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling and optimization of extractive distillation for IPA-water separation using artificial neural network models 基于人工神经网络模型的ipa -水萃取精馏建模与优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-22 DOI: 10.1016/j.compchemeng.2026.109580

Ziba Valizadeh, Hanieh Shokrkar

{"title":"Modelling and optimization of extractive distillation for IPA-water separation using artificial neural network models","authors":"Ziba Valizadeh, Hanieh Shokrkar","doi":"10.1016/j.compchemeng.2026.109580","DOIUrl":"10.1016/j.compchemeng.2026.109580","url":null,"abstract":"<div><div>The extraction of isopropyl alcohol (IPA) from water has long presented a considerable challenge in the chemical industry, mainly due to the formation of an azeotropic mixture and the close boiling points of the two substances. This research introduces a hybrid extractive distillation method utilizing dimethyl sulfoxide (DMSO) as an entrainer, which was simulated using Aspen Plus V11 (NRTL model). The comprehensive simulation attained an impressive IPA purity of 99.9%, which resulted in considerable energy costs. In order to tackle the balance between purity and energy consumption, a novel hybrid framework was created, and essential operational parameters such as feed stage, reflux ratio, an entrainer ratio, condenser duty, and reboiler duty were fine-tuned to enhance IPA purity while reducing energy consumption. The advancement is found in substituting conventional simulation methods with a trained artificial neural network (ANN) to enable rapid predictions, alongside the use of particle swarm optimization (PSO) for fine-tuning parameters. The ANN-PSO framework successfully pinpointed an optimal operating point that led to a 25% decrease in total energy consumption when compared to the baseline. While achieving a practically acceptable IPA purity of 95% in the first column and a water purity of 95% in the second column at reflux ratios of 0.77 and 0.4, respectively, this method demonstrates a notable decrease in computational effort (40% less time) and offers a reliable strategy for low-energy separation processes at an industrial level.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109580"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YANNs: Y-wise affine neural networks for exact and efficient representations of piecewise linear functions YANNs：用于分段线性函数的精确和有效表示的y向仿射神经网络

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-02-10 DOI: 10.1016/j.compchemeng.2026.109589

Austin Braniff, Yuhe Tian

{"title":"YANNs: Y-wise affine neural networks for exact and efficient representations of piecewise linear functions","authors":"Austin Braniff, Yuhe Tian","doi":"10.1016/j.compchemeng.2026.109589","DOIUrl":"10.1016/j.compchemeng.2026.109589","url":null,"abstract":"<div><div>This work formally introduces Y-wise Affine Neural Networks (YANNs), a fully-explainable network architecture that continuously and efficiently represent piecewise affine functions with polytopic subdomains. Following from the proofs, it is shown that the development of YANNs requires no training to achieve the functionally equivalent representation. YANNs thus maintain all mathematical properties of the original formulations. Multi-parametric model predictive control is utilized as an application showcase of YANNs, which theoretically computes optimal control laws as a piecewise affine function of states, outputs, setpoints, and disturbances. With the exact representation of multi-parametric control laws, YANNs retain essential control-theoretic guarantees such as recursive feasibility and stability. This sets YANNs apart from the existing works which apply neural networks for approximating optimal control laws instead of exactly representing them. By optimizing the inference speed of the networks, YANNs can evaluate substantially faster in real-time compared to traditional piecewise affine function calculations. Numerical case studies are presented to demonstrate the algorithmic scalability with respect to the input/output dimensions and the number of subdomains. Future applications can leverage them as an efficient and interpretable starting point for data-driven modeling/control.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109589"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian optimization and temporal attention-enhanced deep neural network for accurate and reliable state of health estimation of lithium-ion batteries 基于贝叶斯优化和时间注意力增强的深度神经网络的锂离子电池健康状态准确可靠估计

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-19 DOI: 10.1016/j.compchemeng.2026.109571

Zhiyu Chen , Hanfei Wang , Siquan Li , Kena Chen , Jinjie Wang , Ping Wang , Lijun Yang

{"title":"Bayesian optimization and temporal attention-enhanced deep neural network for accurate and reliable state of health estimation of lithium-ion batteries","authors":"Zhiyu Chen , Hanfei Wang , Siquan Li , Kena Chen , Jinjie Wang , Ping Wang , Lijun Yang","doi":"10.1016/j.compchemeng.2026.109571","DOIUrl":"10.1016/j.compchemeng.2026.109571","url":null,"abstract":"<div><div>The capacity degradation trend can indirectly reflect the health status of a battery, and accurate state of health (SOH) estimation can reduce the risk of failure and ensure stable operation. However, the limited feature learning capability of traditional models and the random combination of hyperparameters often lead to large estimation errors. To address the issues, this research proposes a deep neural network (DNN) enhanced by Bayesian optimization (BO) and the temporal attention (TA) mechanism to achieve accurate and reliable SOH estimation for lithium-ion batteries. First, direct aging features of the battery are extracted based on the constant-current charging phase and further fused using principal component analysis (PCA). Then, a mapping model between the aging features and capacity is constructed, in which the TA mechanism is employed to enhance the feature learning capability of the DNN, and BO is used to determine the optimal combination of key hyperparameters. Finally, twelve single-battery under different operating conditions and seven multi-battery capacity estimation experiments are conducted. The estimation performance of the proposed model is evaluated using metrics such as mean absolute error (MAE). The experimental results show that the BO-TADNN model achieves capacity estimation errors within ±3% for single-battery experiments, representing an improvement of approximately 70% in stability compared to the DNN. Furthermore, BO-TADNN achieves the best performance across all evaluation metrics in the multi-battery experiments, which provides a theoretical foundation for future applications in battery management systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109571"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PSFCL: A Probabilistic Slow Feature Contrastive Learning approach for incipient fault diagnosis in industrial processes 基于概率慢特征对比学习的工业过程早期故障诊断方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-30 DOI: 10.1016/j.compchemeng.2026.109584

Liangliang Shang , Rui Fang , Jie Liu , Yingzi Jing , Wan Chen , Aibing Qiu

{"title":"PSFCL: A Probabilistic Slow Feature Contrastive Learning approach for incipient fault diagnosis in industrial processes","authors":"Liangliang Shang , Rui Fang , Jie Liu , Yingzi Jing , Wan Chen , Aibing Qiu","doi":"10.1016/j.compchemeng.2026.109584","DOIUrl":"10.1016/j.compchemeng.2026.109584","url":null,"abstract":"<div><div>Recent advances in machine learning have significantly improved fault diagnosis through refined feature extraction and intelligent classification. However, traditional methods often fail to effectively capture the incipient fault features in complex industrial processes. To address this challenge, this article proposes a novel framework named Probabilistic Slow Feature Contrastive Learning (PSFCL) for enhanced incipient feature representation and fault diagnosis. Unlike conventional approaches, the proposed method integrates divergence to extract probabilistic slow features that encode both temporal slowness and distributional distinctiveness. These features are then used to generate positive and negative sample pairs for contrastive learning, enabling the model to learn more discriminative representations. Furthermore, a slowness-guided mechanism adaptively adjusts model parameters during training, reinforcing fault feature extraction. The learned features are fed into a Softmax classifier for incipient fault diagnosis without requiring parameter fine-tuning. Benchmark-based validation on the Tennessee Eastman Process (TEP) under diverse fault types demonstrates that the proposed PSFCL framework outperforms state-of-the-art methods in both diagnostic accuracy and robustness, thereby showcasing its potential to maintain industrial process safety.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109584"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust optimal experimental design for identifiability using the overall mean squared estimation error 使用总体均方估计误差的可辨识性稳健优化实验设计

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-24 DOI: 10.1016/j.compchemeng.2026.109581

Pierre Denis , Constantinos Theodoropoulos , Alain Vande Wouwer

{"title":"Robust optimal experimental design for identifiability using the overall mean squared estimation error","authors":"Pierre Denis , Constantinos Theodoropoulos , Alain Vande Wouwer","doi":"10.1016/j.compchemeng.2026.109581","DOIUrl":"10.1016/j.compchemeng.2026.109581","url":null,"abstract":"<div><div>Mathematical modeling is essential for understanding and controlling physical systems, particularly in scientific and engineering contexts. Accurate parameter estimation is critical for model reliability but often constrained by the cost and complexity of experiments. Optimal Experimental Design (OED) addresses this challenge by identifying experimental conditions that maximize information gain. Traditional OED approaches rely on the Fisher Information Matrix (FIM) and scalar optimality criteria, yet they are sensitive to unknown parameter values. To mitigate this, robust OED methods incorporate prior uncertainty, using strategies such as maximin and expectation-based criteria. In this work, we introduce a novel robust OED framework that unifies prior parameter uncertainty and measurement noise into an Overall Mean Squared estimation Error (OMSE) matrix. This formulation enables the use of standard optimality criteria while inherently accounting for both sources of uncertainty/noise. We demonstrate the effectiveness of our method through two case studies involving dynamical systems of varying complexity and discuss practical considerations for its implementation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109581"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital twin framework with physics-informed neural networks for real-time monitoring of PEM electrolyzers in renewable microgrids 用于可再生微电网PEM电解槽实时监测的物理信息神经网络数字孪生框架

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-25 DOI: 10.1016/j.compchemeng.2026.109582

Hassan NAANANI, Meriem KAYSOUNY, Anas ABERHOUCH, Said SAIR, Abdessamad FAIK

{"title":"Digital twin framework with physics-informed neural networks for real-time monitoring of PEM electrolyzers in renewable microgrids","authors":"Hassan NAANANI, Meriem KAYSOUNY, Anas ABERHOUCH, Said SAIR, Abdessamad FAIK","doi":"10.1016/j.compchemeng.2026.109582","DOIUrl":"10.1016/j.compchemeng.2026.109582","url":null,"abstract":"<div><div>The operation of electrolyzers for producing green hydrogen faces two key challenges: the rapidly increasing demand for hydrogen across diverse applications and the limited durability of electrochemical components. Digital twin (DT) technology offers a promising pathway to address these limitations by enabling real-time monitoring, fault detection, and predictive analysis. This study presents the development of a DT for a laboratory-scale proton exchange membrane (PEM) electrolyzer composed of two series-connected cells. The virtual counterpart synchronizes with experimental voltage and current data acquired under varying operating conditions, enabling continuous verification of the system’s behavior. To enhance predictive capability, a physicsinformed neural networks (PINN) is integrated into the DT, combining polarization data with electrochemical constraints, including Butler-Volmer activation kinetics and ohmic resistance. A complementary 3D representation of the developed system provides an interactive visualization of the electrolyzer and its operating state. The resulting framework supports real-time supervision, performance assessment, and degradation monitoring, offering a practical foundation for intelligent control and diagnostic strategies in PEM electrolysis systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109582"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling pH gradients in industrial-scale lactic acid bacteria fermentation using flow-informed compartment models 利用流动信息室模型模拟工业规模乳酸菌发酵中的pH梯度

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-05-01 Epub Date: 2026-01-23 DOI: 10.1016/j.compchemeng.2026.109568

Johan Le Nepvou De Carfort , Michail Raptotasios , Víctor Puig-I-Laborda , Krist V. Gernaey , Ulrich Krühne

{"title":"Modeling pH gradients in industrial-scale lactic acid bacteria fermentation using flow-informed compartment models","authors":"Johan Le Nepvou De Carfort , Michail Raptotasios , Víctor Puig-I-Laborda , Krist V. Gernaey , Ulrich Krühne","doi":"10.1016/j.compchemeng.2026.109568","DOIUrl":"10.1016/j.compchemeng.2026.109568","url":null,"abstract":"<div><div>This study presents a numerical model of an industrial-scale lactic acid bacteria fermentation process that incorporates reactor hydrodynamics and chemical/biochemical reactions to resolve the impact of transport limitations on the industrial process. The model employs a three-dimensional CFD-based compartmentalization method that uses unsupervised clustering to build a simplified representation of the reactor volume while preserving essential mixing characteristics. This approach enabled fast simulations of the mixing dynamics over extended periods and facilitated the integration with reaction kinetics. The numerical model successfully simulated both batch and continuous processes, capturing the magnitude of gradients and quantifying their impact on process dynamics. During batch operation, pH gradients peaked during exponential growth but did not significantly affect biological growth, although more sensitive strains could be impacted. Continuous operation resulted in gradients in pH and substrate concentration, affecting the process dynamics and productivity. The compartment model’s adaptability to different reactor designs and scales makes it a valuable tool for industrial applications. Here, the model can be used to develop effective scale-up strategies and ensure consistent product quality across the various production scales.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109568"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0