Digital Chemical Engineering最新文献_第5页

Decarbonizing the chemical industry through digital technologies 通过数字技术使化学工业脱碳

IF 3

Digital Chemical Engineering Pub Date : 2025-09-01 Epub Date: 2025-06-18 DOI: 10.1016/j.dche.2025.100250

Kathleen B. Aviso

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 : 2025-09-01 Epub 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":"2025-09-01","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

Temporal PFD-guided graph convolutional networks: a novel approach to process modeling 时间pfd引导图卷积网络：过程建模的新方法

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-01 Epub Date: 2025-08-04 DOI: 10.1016/j.dche.2025.100260

Hiroki Horiuchi, Yoshiyuki Yamashita

引用次数: 0

Hybrid neural networks for improved chemical process modeling: Bridging data-driven insights with physical consistency 用于改进化学过程建模的混合神经网络：桥接数据驱动的见解与物理一致性

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-01 Epub Date: 2025-07-25 DOI: 10.1016/j.dche.2025.100256

Jana Mousa, Stéphane Negny, Rachid Ouaret

{"title":"Hybrid neural networks for improved chemical process modeling: Bridging data-driven insights with physical consistency","authors":"Jana Mousa, Stéphane Negny, Rachid Ouaret","doi":"10.1016/j.dche.2025.100256","DOIUrl":"10.1016/j.dche.2025.100256","url":null,"abstract":"<div><div>The increasing reliance on neural networks (NN) in chemical process modeling highlights their capability for accurate predictions, yet their standalone application often struggles to adhere to fundamental physical laws such as equilibrium constraints and mass balance. Addressing this limitation, hybrid methods that integrate data-driven insights with physical consistency have gained prominence. This study systematically explores the integration of NNs with nonlinear data reconciliation (NDR) across multiple testing dimensions, including a Gibbs reactor, data robustness evaluations, and reactor-distillation system integration. Hybrid methodologies such as NN + NDR, NN + KKT (Karush-Kuhn-Tucker), and KKT + PINN (Physics-Informed Neural Networks with KKT conditions) are comparatively assessed. The proposed NN + NDR framework demonstrates superior performance in minimizing errors and enforcing physical laws, with minimal computational overhead. This work emphasizes the scalability, robustness, and transformative potential of modular hybrid strategies in advancing reliable, physically consistent chemical process modeling.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100256"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721106","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 : 2025-09-01 Epub 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":"2025-09-01","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

A circular exploration of cryoprotective agents for stem cells using computer-aided molecular design approaches 利用计算机辅助分子设计方法对干细胞冷冻保护剂进行循环探索

IF 3

Digital Chemical Engineering Pub Date : 2025-09-01 Epub Date: 2025-06-11 DOI: 10.1016/j.dche.2025.100248

Rei Tamaki , Yusuke Hayashi , Yuki Uno , Masahiro Kino-oka , Hirokazu Sugiyama

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 Epub Date: 2025-08-20 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

Solving bilevel problems under uncertainty with embedded neural networks: Incorporating scenario sets as inputs 用嵌入式神经网络解决不确定性下的双层问题：将场景集作为输入

IF 4.1

Digital Chemical Engineering Pub Date : 2025-09-01 Epub Date: 2025-08-13 DOI: 10.1016/j.dche.2025.100253

Isabela Fons Moreno-Palancas, Rubén Ruiz Femenia, Raquel Salcedo Díaz, José A. Caballero

{"title":"Solving bilevel problems under uncertainty with embedded neural networks: Incorporating scenario sets as inputs","authors":"Isabela Fons Moreno-Palancas, Rubén Ruiz Femenia, Raquel Salcedo Díaz, José A. Caballero","doi":"10.1016/j.dche.2025.100253","DOIUrl":"10.1016/j.dche.2025.100253","url":null,"abstract":"<div><div>Bilevel optimization is a sub-field of optimization widely valued both in academia and business due to its suitability to identify the best solutions for hierarchical decision-making processes. The predominant approach to solving bilevel problems involves reformulating them as single-level equivalents that can be solved with commercial solvers. However, traditional reformulation techniques are often constrained by the complexity of the lower-level problem, particularly when the number of variables or constraints is large, or uncertain parameters are present.</div><div>Given the intrinsic presence of uncertainty in most real-world applications of bilevel optimization, this work proposes a metamodeling approach that approximates the lower level using a neural network. Although this strategy has been satisfactorily applied to deterministic bilevel models, we extend its use to stochastic bilevel problems by training a neural network that learns over a set of realizations of the uncertain parameters. Our methodology is tested on the short-term scheduling of a batch chemical process, a context where classical reformulation approaches become unmanageable due to the presence of differential equations. The results indicate that our approach successfully achieves a single-level reformulation that is computationally tractable and can be solved efficiently even in complex bilevel settings, provided that the lower-level remains manageable and the main complexity arises from its integration into the upper level.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100253"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865689","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

A computational investigation of high-flux, plate-and-frame membrane modules for industrial carbon capture 用于工业碳捕集的高通量板框膜组件的计算研究

IF 3

Digital Chemical Engineering Pub Date : 2025-09-01 Epub Date: 2025-05-30 DOI: 10.1016/j.dche.2025.100246

Cheick Dosso , Hector A. Pedrozo , Thien Tran , Lingxiang Zhu , Victor Kusuma , David Hopkinson , Lorenz T. Biegler , Grigorios Panagakos

{"title":"A computational investigation of high-flux, plate-and-frame membrane modules for industrial carbon capture","authors":"Cheick Dosso , Hector A. Pedrozo , Thien Tran , Lingxiang Zhu , Victor Kusuma , David Hopkinson , Lorenz T. Biegler , Grigorios Panagakos","doi":"10.1016/j.dche.2025.100246","DOIUrl":"10.1016/j.dche.2025.100246","url":null,"abstract":"<div><div>In this work, we study the application of membrane-based separation systems for carbon capture, considering plate-and-frame membrane modules. The successful deployment of membrane CO<sub>2</sub> capture systems relies on high-performing membranes, as well as on effective membrane modules that can fully exploit the developed membranes. A plate-and-frame membrane module is especially attractive for CO<sub>2</sub> capture from industrial flue gas, due to its reduced pressure drop compared to its counterparts such as spiral wound modules and hollow fiber modules. To design better plate-and-frame modules, we investigate their basic unit - a single membrane stack - through a combination of computational modeling and experimental investigations. The modeling approach is based on computational fluid dynamics (CFD) to represent the multiphysics problem, including the fluid flow and diffusion processes within the membrane stack. We use experimental data collected under different operating conditions to validate the CFD model. Numerical results suggest good agreement between experiments and model outputs for CO<sub>2</sub> recovery, CO<sub>2</sub> mole fractions in the retentate and permeate, and stage-cut. The CFD model is able to predict accurately the flow behavior, providing valuable insights into the effects of fluid dynamics on the mass transfer of CO<sub>2</sub>. CFD models achieve high accuracy by capturing complex permeate-side flow patterns exhibiting a 4.5 % maximum relative error compared to experiments. Results suggest that deviations of 1D models, assuming ideal flow patterns, from the CFD increase as separation properties improve with material advancements, and can be as high as 21 % for some cases. We also carry out a sensitivity analysis to identify the effect of key parameters on the CO<sub>2</sub> recovery and the CO<sub>2</sub> purity of the outlet streams.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"16 ","pages":"Article 100246"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280749","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 Epub Date: 2025-05-30 DOI: 10.1016/j.dche.2025.100247

Jin Xuan , Jinfeng Liu

引用次数: 0