Computers & Chemical Engineering最新文献

{"title":"Integrating process data and topology information through edge-group sparse principal component analysis for process analytics","authors":"Yi Liu ,&nbsp;Po-Wei Yeh ,&nbsp;Mingwei Jia ,&nbsp;Po-Chun Mao ,&nbsp;Yuan Yao","doi":"10.1016/j.compchemeng.2025.109419","DOIUrl":"10.1016/j.compchemeng.2025.109419","url":null,"abstract":"<div><div>Despite rapid advancements in deep learning, traditional methods like principal component analysis (PCA) remain indispensable in chemical process analysis due to their strong mathematical foundations and powerful visualization capabilities, which uncover variable correlations and reveal process variations. This study introduces edge-group sparse PCA (ESPCA) for process analytics, integrating process topology while enforcing sparsity on loading vectors to enhance interpretability. A systematic application procedure is demonstrated through illustrative examples. In these applications, ESPCA proves particularly effective in identifying key process units and variables associated with faults or disturbances, providing a solid foundation for root cause analysis. Visualization tools play a crucial role in integrating available process knowledge, facilitating the interpretation of results, and enabling engineers to derive conclusions in a clear and intuitive manner. Additionally, statistical causality analysis methods like transfer entropy can be used alongside ESPCA to trace propagation paths and pinpoint root causes of process anomalies.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109419"},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227129","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}

{"title":"Multimodal hazardous materials risk graph completion: a joint optimization approach with dual channel embedding and generative adversarial network","authors":"Shuangbao Zhang,&nbsp;Quan Cheng","doi":"10.1016/j.compchemeng.2025.109424","DOIUrl":"10.1016/j.compchemeng.2025.109424","url":null,"abstract":"<div><div>Multimodal hazardous chemical risk knowledge graphs are gradually becoming a critical technical foundation for industrial safety management, offering novel pathways for intelligent identification and risk prediction through their integration capabilities over multi-source heterogeneous data. However, existing multimodal hazardous chemical knowledge graph face significant challenges in practical construction, including uneven distribution across modalities and severe missingness of high-dimensional feature information. These issues lead to incomplete graph structures, negatively impacting the accuracy of knowledge reasoning and risk prediction. To address these challenges, this paper proposes a multimodal knowledge graph completion model, named HCMMKGC, integrating a dual-channel embedding mechanism and generative adversarial optimization. Specifically, the dual-channel architecture independently models high- and low-dimensional multimodal data, preserving complex structural details while improving multimodal semantic consistency. Additionally, a Generative Adversarial Network is introduced to synthesize scarce modality samples, alleviating representation bias caused by modality imbalance and thereby enhancing graph completion effectiveness and downstream reasoning performance. The experimental findings demonstrate that the HCMMKGC model exhibits strong performance on the HCKG-Text and HCKG-Visual datasets, with an MRR of 0.453 and 0.414, respectively. The model's Hit@10 values of 0.642 and 0.572 are indicative of significant improvement over the existing baseline model. These results underscore the model's superior generalization capabilities and robustness.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109424"},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227130","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}

{"title":"BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertainty","authors":"Akshay Kudva ,&nbsp;Joel A. Paulson","doi":"10.1016/j.compchemeng.2025.109393","DOIUrl":"10.1016/j.compchemeng.2025.109393","url":null,"abstract":"<div><div>Optimal design under uncertainty remains a fundamental challenge in advancing reliable, next-generation process systems. Robust optimization (RO) offers a principled approach by safeguarding against worst-case scenarios across a range of uncertain parameters. However, traditional RO methods typically require known problem structure, which limits their applicability to high-fidelity simulation environments. To overcome these limitations, recent work has explored robust Bayesian optimization (RBO) as a flexible alternative that can accommodate expensive, black-box objectives. Existing RBO methods, however, generally ignore available structural information and struggle to scale to high-dimensional settings. In this work, we introduce BONSAI (Bayesian Optimization of Network Systems under uncertAInty), a new RBO framework that leverages partial structural knowledge commonly available in simulation-based models. Instead of treating the objective as a monolithic black box, BONSAI represents it as a directed graph of interconnected white- and black-box components, allowing the algorithm to utilize intermediate information within the optimization process. We further propose a scalable Thompson sampling-based acquisition function tailored to the structured RO setting, which can be efficiently optimized using gradient-based methods. We evaluate BONSAI across a diverse set of synthetic and real-world case studies, including applications in process systems engineering. Compared to existing simulation-based RO algorithms, BONSAI consistently delivers more sample-efficient and higher-quality robust solutions, highlighting its practical advantages for uncertainty-aware design in complex engineering systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109393"},"PeriodicalIF":3.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227134","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}

{"title":"Dynamic hybrid modeling: Incremental identification and model predictive control","authors":"Adrian Caspari ,&nbsp;Thomas Bierweiler ,&nbsp;Sarah Fadda ,&nbsp;Daniel Labisch ,&nbsp;Maarten Nauta ,&nbsp;Franzisko Wagner ,&nbsp;Merle Warmbold ,&nbsp;Constantinos C. Pantelides","doi":"10.1016/j.compchemeng.2025.109413","DOIUrl":"10.1016/j.compchemeng.2025.109413","url":null,"abstract":"<div><div>Mathematical models are crucial for optimizing and controlling chemical processes, yet they often face significant limitations in terms of computational time, algorithm complexity, and development costs. Hybrid models, which combine mechanistic models with data-driven models (i.e. models derived via the application of machine learning to experimental data), have emerged as a promising solution to these challenges. However, the identification of dynamic hybrid models remains difficult due to the need to integrate data-driven models within mechanistic model structures.</div><div>We present an incremental identification approach for dynamic hybrid models that decouples the mechanistic and data-driven components to overcome computational and conceptual difficulties. Our methodology comprises four key steps: (1) regularized dynamic parameter estimation to determine optimal time profiles for flux variables, (2) correlation analysis to evaluate relationships between variables, (3) data-driven model identification using advanced machine learning techniques, and (4) hybrid model integration to combine the mechanistic and data-driven components. This approach facilitates early evaluation of model structure suitability, accelerates the development of hybrid models, and allows for independent identification of data-driven components.</div><div>Three case studies are presented to illustrate the robustness, reliability, and efficiency of our incremental approach in handling complex systems and scenarios with limited data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109413"},"PeriodicalIF":3.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155407","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}

{"title":"Reinforcement learning-driven plant-wide refinery planning using model decomposition","authors":"Zhouchang Li,&nbsp;Runze Lin,&nbsp;Hongye Su,&nbsp;Lei Xie","doi":"10.1016/j.compchemeng.2025.109348","DOIUrl":"10.1016/j.compchemeng.2025.109348","url":null,"abstract":"<div><div>In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new demands, this paper presents a novel optimization framework for plant-wide refinery planning, integrating model decomposition with deep reinforcement learning. The approach decomposes the complex large-scale refinery optimization problem into manageable submodels, improving computational efficiency while preserving accuracy. A reinforcement learning-based pricing mechanism is introduced to generate pricing strategies for intermediate products, facilitating better coordination between submodels and enabling rapid responses to market changes. Two industrial case studies, covering both single-period and multi-period refinery planning, demonstrate significant improvements in computational efficiency while ensuring refinery profitability.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109348"},"PeriodicalIF":3.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227128","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}

{"title":"A novel data augmentation strategy for gas leak detection and segmentation using Mask R-CNN and bit plane slicing in chemical process environments","authors":"Hritu Raj,&nbsp;Gargi Srivastava","doi":"10.1016/j.compchemeng.2025.109407","DOIUrl":"10.1016/j.compchemeng.2025.109407","url":null,"abstract":"<div><div>Gas leak detection is a critical task for environmental and industrial safety, often facilitated through imaging techniques such as Mask R-CNN. However, accurately segmenting gas plumes remains challenging due to their dynamic nature and complex background. In this study, we propose a novel approach to improve gas leak plume segmentation accuracy by combining Mask R-CNN with augmented bit plane images. Initially trained on a dataset of 1000 gas leak images, our model, utilizing a ResNet101 backbone, achieved a commendable F1-Score of 95.6%, outperforming MobileNetV2 and DenseNet169. Through the incorporation of a novel bit plane image augmentation strategy, specifically focusing on the XOR combination of bit planes 4 and 5, the ResNet101 model’s F1-Score significantly improved to 98.7%, showcasing the effectiveness of our approach in enriching the training data and enhancing the model’s ability to generalize to unseen instances. This bit plane augmentation method also demonstrated superior performance compared to other mainstream image enhancement techniques like CLAHE and Gamma correction. These findings suggest promising implications for improving gas leak detection systems, thereby contributing to enhanced safety measures in various industrial and environmental settings, with considerations for real-time industrial deployment.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109407"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154735","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}

{"title":"Data-driven initialization of evolutionary methods for process synthesis considering centrality and diversity criteria","authors":"Jean-Marc Commenge,&nbsp;Andres Piña-Martinez","doi":"10.1016/j.compchemeng.2025.109416","DOIUrl":"10.1016/j.compchemeng.2025.109416","url":null,"abstract":"<div><div>Process synthesis using evolutionary methods, based on the iterative application of mutation operators, requires to initialize the method by one or a set of process flowsheets. Appropriate initialization might reduce computation times by providing first proposals that decrease the number of mutations to reach optimal structures, in terms of units and connectivity. This work illustrates how to identify, from a given database of flowsheets, the flowsheets that might play a pivotal role in the further evolutionary synthesis. A home-made database with over 2000 flowsheets, digitalized from 800 recent scientific publications, is used, exhibiting the variety of possible structures from single distillation columns to biorefinery layouts. Selection of initialization flowsheets should ensure diversity in structures and units while minimizing the number of mutations needed to evolve to any other process flowsheet. A distance function is defined as the minimum number of mutations required to transform one flowsheet into another, and computed for all pairs of flowsheets in the database enabling to compare their topologies and quantitatively analyze the population. Four sampling strategies are compared, considering centrality criteria, sampling flowsheets in groups of similar structures, random sampling, and k-medoids clustering. For each strategy, the distribution of distances from the selected structures to the database population and their diversity are compared. Centrality-based selection minimizes the required number of mutations but shows poor units’ diversity. Selection from distinct groups of similar structures improves performance only for distant flowsheets. Random sampling ensures diversity but performs poorly in reducing required mutations. Conversely, k-medoids sampling shows good performance in both the number of required mutations and the diversity of selected flowsheets, making it a balanced method for flowsheet sampling. The initialization strategies are applied to the case study of benzene chlorination and their fitness and diversity are monitored along the generations of the evolutionary synthesis.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109416"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227127","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}

{"title":"Simultaneous design of microbe and bioreactor","authors":"Anita L. Ziegler ,&nbsp;Marc-Daniel Stumm ,&nbsp;Tim Prömper ,&nbsp;Thomas Steimann ,&nbsp;Jørgen Magnus ,&nbsp;Alexander Mitsos","doi":"10.1016/j.compchemeng.2025.109388","DOIUrl":"10.1016/j.compchemeng.2025.109388","url":null,"abstract":"<div><div>When developing a biotechnological process, the microorganism is first designed, e.g., using metabolic engineering. Then, the optimum fermentation parameters are determined on a laboratory scale, and lastly, they are transferred to the bioreactor scale. However, this step-by-step approach is costly and time-consuming and may result in suboptimal configurations. Herein, we present the bilevel optimization formulation <em>SimulKnockReactor</em>, which connects bioreactor design with microbial strain design, an extension of our previous formulation, SimulKnock (Ziegler et al., 2024). At the upper (bioreactor) level, we minimize investment and operation costs for agitation, aeration, and pH control by determining the size and operating conditions of a continuous stirred-tank reactor—without selecting specific devices like the stirrer type. The lower (cellular) level is based on flux balance analysis and implements optimal reaction knockouts predicted by the upper level. Our results with a core and a genome-scale metabolic model of <em>Escherichia coli</em> show that the substrate is the largest cost factor. Our simultaneous approach outperforms a sequential approach using OptKnock. Namely, the knockouts proposed by OptKnock cannot guarantee the required production capacity in all cases considered. SimulKnockReactor, on the other hand, provides solutions in all cases considered, highlighting the advantage of combining cellular and bioreactor levels. This work is a further step towards a fully integrated bioprocess design.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109388"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227132","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}

{"title":"Reinforcement learning-based autonomous control of bench-scale primary separation vessel","authors":"Oguzhan Dogru,&nbsp;Mahmut Berat Tatlici,&nbsp;Biao Huang","doi":"10.1016/j.compchemeng.2025.109405","DOIUrl":"10.1016/j.compchemeng.2025.109405","url":null,"abstract":"<div><div>In the process industry, smart automation of complex operations has great potential for efficient and safe operation, making it a key component for unlocking economic and sustainable large-scale production. However, real-world process units such as primary separation vessels (PSVs) pose numerous challenges, such as sensory uncertainty, nonlinear dynamics, and operational variability. This study introduces a novel autonomous control framework integrating model predictive control (MPC), reinforcement learning (RL), and state estimation techniques for building an adaptive, optimal, and safe control strategy. The proposed framework is demonstrated in a real-world scenario using a bench-scale experimental setup of the PSV that mimics the actual process. The implemented closed-loop control system accurately predicted a crucial process variable, optimized the operating point in real time, and achieved robust set-point tracking performance by tuning the controller for real process conditions. The results indicate that incorporating adaptive and data-driven techniques such as reinforcement learning into feedback control approaches is promising for building robust autonomous control strategies that maximize efficiency while respecting physical constraints, paving the way for autonomous control systems that are deployable in complex real-world scenarios.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109405"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155405","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}

{"title":"Data-driven globalized distributionally robust multi-period location-routing-scheduling model for waste-to-energy supply chain under emissions ambiguity","authors":"Xuekun Wang ,&nbsp;Zhaozhuang Guo ,&nbsp;Ying Liu","doi":"10.1016/j.compchemeng.2025.109397","DOIUrl":"10.1016/j.compchemeng.2025.109397","url":null,"abstract":"<div><div>The intensification of global energy shortages and continuous expansion of municipal solid waste require effectively optimizing the waste-to-energy supply chain (WtESC). When the distribution information of uncertain parameters is partially known, WtESC often faces complex and ambiguous challenges. To address this, we construct data-driven inner and outer ambiguity sets based on real data and utilize globalized distributionally robust (GDR) optimization framework to handle uncertainty. Compared with classical distributionally robust optimization, it allows for controllable violations of constraints in the outer ambiguity set. A data-driven globalized distributionally robust WtESC (GDR-WtESC) model is developed, and transformed into an equivalent mixed integer linear programming model according to duality theory. The computational results of real case indicate that <span><math><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow></math></span> There is a conflict between economic and environmental objectives, and decision-makers can prioritize them based on their own preferences. <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span> The tolerance level for constraint violation has a positive impact on the total cost. Specifically, the increase of tolerance level from 0.1 to 0.9 can reduce the optimal cost by 1.07%. <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span> The optimal decision of GDR-WtESC model has strong stability and high quality. Compared with the sample average approximation (SAA) model, the variance of the objective value in out of sample experiments decreases by 88.28% on average, and the average cost decreases by 0.55%. The SAA method can address the uncertainty, but cannot handle constraint violations in realistic. Thus, for decision makers who are sensitive to distributional ambiguity, the GDR method is recommended for WtESC problem, because it enhances the robustness and reduces conservatism.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109397"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154737","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}