Computers & Chemical Engineering最新文献

{"title":"Data-driven approach to learning optimal forms of constitutive relations in models describing Lithium plating in battery cells","authors":"Avesta Ahmadi ,&nbsp;Kevin J. Sanders ,&nbsp;Gillian R. Goward ,&nbsp;Bartosz Protas","doi":"10.1016/j.compchemeng.2025.109252","DOIUrl":"10.1016/j.compchemeng.2025.109252","url":null,"abstract":"<div><div>In this study we construct a data-driven model describing Lithium plating in a battery cell, which is a key process contributing to degradation of such cells. Starting from the fundamental Doyle-Fuller-Newman (DFN) model, we use asymptotic reduction and spatial averaging techniques to derive a simplified representation to track the temporal evolution of two key concentrations in the system, namely, the total intercalated Lithium on the negative electrode particles and total plated Lithium. This model depends on an a priori unknown constitutive relation representing the plating dynamics of the cell as a function of the state variables. An optimal form of this constitutive relation is then deduced from experimental measurements of the time-dependent concentrations of different Lithium phases acquired through Nuclear Magnetic Resonance spectroscopy. This is done by solving an inverse problem in which this constitutive relation is found subject to minimum assumptions as a minimizer of a suitable constrained optimization problem where the discrepancy between the model predictions and experimental data is minimized. This optimization problem is solved using a state-of-the-art adjoint-based technique. In contrast to some of the earlier approaches to modeling Lithium plating, the proposed model is able to predict non-trivial evolution of the concentrations in the relaxation regime when no current is applied to the cell. When equipped with an optimal constitutive relation, the model provides accurate predictions of the time evolution of both intercalated and plated Lithium across a wide range of charging/discharging rates. It can therefore serve as a useful tool for prediction and control of degradation mechanism in battery cells.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109252"},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926114","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 demand bidding model for multi-product industrial plants","authors":"Xin Tang ,&nbsp;Michael Baldea ,&nbsp;Elaine T. Hale ,&nbsp;Ross Baldick ,&nbsp;Richard P. O’Neill","doi":"10.1016/j.compchemeng.2025.109349","DOIUrl":"10.1016/j.compchemeng.2025.109349","url":null,"abstract":"<div><div>The growing contribution of renewable energy sources has increased volatility and uncertainty in electricity markets, challenging traditional grid operation paradigms. Demand bidding (DB), a market participation model where (large) electricity users communicate their willingness to pay for electricity to the grid operator, was shown in previous work to enhance grid stability and lower generation cost. We present a DB model for multi-product industrial plants, based on an extended optimal power flow problem where the plant dynamics are represented using autoregressive with extra inputs (ARX) models. We compare DB to price-based demand-side management, showing that, under certain assumptions, the two approaches are equivalent, while DB provides more transparency and predictability to the grid operator. A case study based on an industrial air separation unit is discussed.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109349"},"PeriodicalIF":3.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019976","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":"Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations","authors":"Niels Krausch ,&nbsp;Martin Doff-Sotta ,&nbsp;Mark Cannon ,&nbsp;Peter Neubauer ,&nbsp;Mariano Nicolas Cruz Bournazou","doi":"10.1016/j.compchemeng.2025.109344","DOIUrl":"10.1016/j.compchemeng.2025.109344","url":null,"abstract":"<div><div>Bioprocesses are often characterized by nonlinear and uncertain dynamics, posing particular challenges for model predictive control (MPC) algorithms due to their computational demands when applied to nonlinear systems. Recent advances in optimal control theory have demonstrated that concepts from convex optimization, tube MPC, and differences of convex functions (DC) enable efficient, robust online process control. Our approach is based on DC decompositions of nonlinear dynamics and successive linearizations around predicted trajectories. By convexity, the linearization errors have tight bounds and can be treated as bounded disturbances within a robust tube MPC framework. We describe a systematic, data-driven method for computing DC model representations using deep neural networks with a special convex structure, and explain how the resulting MPC optimization can be solved using convex programming. For the problem of maximizing product formation in a cultivation with uncertain model parameters, we design a controller that ensures robust constraint satisfaction and allows online estimation of unknown model parameters. Our results indicate that this method is a promising solution for computationally tractable, robust MPC of bioprocesses.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109344"},"PeriodicalIF":3.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893535","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":"Model-based predictive control for pneumatic separation and classification of materials in lithium-ion battery recycling","authors":"Antonio I. García ,&nbsp;Oscar A. Marín ,&nbsp;Edelmira D. Gálvez","doi":"10.1016/j.compchemeng.2025.109358","DOIUrl":"10.1016/j.compchemeng.2025.109358","url":null,"abstract":"<div><div>Due to the considerable number of lithium-ion batteries (LIBs) required for telecommunication systems, electric transport, and renewable energy storage, among other applications, the recycling of spent LIBs is considered an increasingly critical operation. The improvement of this operation can reduce manufacturing costs, the consumption of raw materials, and the environmental footprint produced by their disposal. The present work is focused on implementing advanced control strategies for the separation and classification stages in spent LIBs recycling. The control strategies used correspond to model-based predictive control (MPC). The methodology consisted of implementing a phenomenological model that represents the operation of a device that separates and classifies materials based on their physical properties and uses an air jet as a suspension media. The study presents five control scenarios simulated considering performance approaches and one scenario regarding economic approach. The two manipulated variables example obtained the highest relative error for the output variable concerning the set point, with 1.7125%. Implementing MPC controllers for the material separation stage in LIBs recycling would allow the improvement of these processes in both performance and economic aspects.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109358"},"PeriodicalIF":3.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926066","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":"Bayesian optimization with search space movement for cooling crystallization process","authors":"Tae Hoon Oh ,&nbsp;Kazuki Kato ,&nbsp;Osamu Tonomura ,&nbsp;Ken-Ichiro Sotowa","doi":"10.1016/j.compchemeng.2025.109350","DOIUrl":"10.1016/j.compchemeng.2025.109350","url":null,"abstract":"<div><div>Experimental automation equipped with data-driven optimization is attracting significant attention as an effective platform for finding optimal operating conditions. The key is to automate the decision-making procedure using Bayesian optimization. However, the optimization performance depends heavily on the search space, which is typically selected manually by an expert with domain knowledge. This study proposes a new Bayesian optimization algorithm with a search space movement strategy to automate the search space selection procedure. Simulation studies of two benchmark problems show that the proposed method can determine the optimal conditions with fewer trials than existing methods. Furthermore, the proposed method was applied to maximize the productivity of batch cooling crystallization. The experimental results indicate that the proposed Bayesian optimization algorithm can automatically and robustly find the proper search space and thus improve productivity by up to 46%.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109350"},"PeriodicalIF":3.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880384","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 knowledge-graph-based pharmaceutical engineering chatbot for drug discovery","authors":"Naz Pinar Taskiran,&nbsp;Chia-En Jacklyn Tsai,&nbsp;Shuxin Huang,&nbsp;Arijit Chakraborty,&nbsp;Venkat Venkatasubramanian","doi":"10.1016/j.compchemeng.2025.109318","DOIUrl":"10.1016/j.compchemeng.2025.109318","url":null,"abstract":"<div><div>Despite their success in day-to-day applications, ChatGPT and other large language models (LLMs) have not covered as much ground in scientific and engineering domains. One key challenge is the abundance of domain-specific terminology, which an LLM is not trained to extract in accordance with the underlying physical laws. Such black-box models can also lead to unreliable results or hallucinations. Hybrid AI, which combines data-driven and symbolic methods, leverages domain knowledge to add explainability and reliability to answers. Our group has previously developed a domain-informed ontology-based information extraction tool called SUSIE, which extracts key terms and their context to present them to the user as knowledge graphs (KGs). Although KGs are used to visualize relationships between different entities, they are not easily accessible for user questions. However, they serve as a structured input for LLMs. Thus, KGs can efficiently query a corpus of pharmaceutical documents, streamlining drug discovery and manufacturing processes. In this work, we propose methods to improve the information extraction capabilities of SUSIE by expanding its knowledge base and improving its ability to understand scientific material through a sentence-restructuring module. Additionally, we present a customized question-and-answer module that enables the user to query from generated KGs and get an answer in natural language. Unlike black-box models such as those purely powered by OpenAI’s models and the LangChain GraphQA packages, combining our KGs with Neo4j limits hallucinations and provides reliable and traceable answers in a user-friendly chatbot interface.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109318"},"PeriodicalIF":3.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863217","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":"Model predictive control for continuous pharmaceutical manufacturing with mass retention constraints","authors":"Zheming Wang ,&nbsp;Chenyang Gu ,&nbsp;Bo Chen ,&nbsp;Shuwang Du","doi":"10.1016/j.compchemeng.2025.109332","DOIUrl":"10.1016/j.compchemeng.2025.109332","url":null,"abstract":"<div><div>The pharmaceutical industry is undergoing a significant shift from batch to continuous production processes in pursuit of enhanced productivity and profitability. This motivates the research of control techniques for continuous pharmaceutical manufacturing. Unlike batch processing, continuous pharmaceutical manufacturing involves a single input of raw materials, with all subsequent steps operating in an uninterrupted flow. This paper presents the application of constrained model predictive control (MPC) for the feeding and mixing units in continuous pharmaceutical manufacturing with mass retention constraints. Based on mechanistic modeling, we develop a dynamic model of the continuous pharmaceutical process by introducing two integral state variables, which allow to characterize mass retention constraints. With this model, we then design a MPC scheme to track the desired outlet mass flow subject to mass retention constraints. Finally, the effectiveness of the proposed MPC scheme is validated by a simulation example.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109332"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880388","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":"Development of approximate scheduling-adaptive controllers for multi-products continuous chemical processes using deep learning techniques and model predictive control","authors":"M. Abou El Qassime ,&nbsp;A. Shokry ,&nbsp;A. Espuña ,&nbsp;E. Moulines","doi":"10.1016/j.compchemeng.2025.109359","DOIUrl":"10.1016/j.compchemeng.2025.109359","url":null,"abstract":"<div><div>Recently, Machine learning (ML) techniques are increasingly used to enhance process control. However, most ML-based control solutions treat control problems in isolation from higher-level decision-making layers (scheduling in this study), which they must interact with and adapt to during operation. Consequently, they often become ineffective or inapplicable when scheduling scenarios vary (e.g., product types, sequences, quantities, or qualities).</div><div>Therefore, this work proposes a new Deep Learning-based Scheduling-Adaptive Controller (DL-SAC) that approximates Model Predictive Control (MPC) solutions while explicitly incorporating scheduling-layer decisions. DL-SAC learns how variations in product sequence, production rates, and quality specifications influence optimal closed-loop control actions. It is trained using a dataset generated by solving the nonlinear MPC problem under diverse scheduling scenarios. Each training instance includes state and control trajectories along with scheduling features such as production rates and product quality specifications, thereby embedding scheduling-contextual information into the control approximation.</div><div>The proposed approach is validated on a benchmark multi-product continuous chemical process subject to various scheduling configurations and process disturbance. Across these scenarios, DL-SAC achieves a Normalized Root Mean Square Error (NRMSE) of 1.19 % in predicting control actions, while reducing the online computational time required to solve the MPC problem by approximately 98.8 %. These results demonstrate the method’s capability to deliver accurate, real time control approximations while maintaining adaptability to variations in scheduling decisions and process dynamics. The approach (i) enhances real-time operational flexibility and adaptability of chemical plants and (ii) provides basis for improved integration between control and scheduling, enabling more unified and responsive process optimization.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109359"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926068","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":"Using large language models for solving textbook-style thermodynamic problems","authors":"Rébecca Loubet ,&nbsp;Pascal Zittlau ,&nbsp;Luisa Vollmer ,&nbsp;Marco Hoffmann ,&nbsp;Sophie Fellenz ,&nbsp;Fabian Jirasek ,&nbsp;Heike Leitte ,&nbsp;Hans Hasse","doi":"10.1016/j.compchemeng.2025.109333","DOIUrl":"10.1016/j.compchemeng.2025.109333","url":null,"abstract":"<div><div>Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving capabilities of LLMs in the domain of thermodynamics. A benchmark of 22 textbook-style thermodynamic problems to evaluate LLMs is presented that contains both simple and advanced problems. Five different LLMs are assessed: GPT-3.5, GPT-4, and GPT-4o from OpenAI, Llama 3.1 from Meta, and le Chat from MistralAI. The answers of these LLMs were evaluated by trained human experts, following a methodology akin to the grading of academic exam responses. The scores and the consistency of the answers are discussed, together with the analytical skills of the LLMs. Both strengths and weaknesses of the LLMs become evident. They generally yield good results for the simple problems, but also limitations become clear: The LLMs do not provide consistent results, they often fail to fully comprehend the context and make wrong assumptions. Given the complexity and domain-specific nature of the problems, the statistical language modeling approach of the LLMs struggles with the accurate interpretation and the required reasoning. The present results highlight the need for more systematic integration of thermodynamic knowledge with LLMs, for example, by using knowledge-based methods.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109333"},"PeriodicalIF":3.9,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893669","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":"Physics-guided transfer learning for Bayesian optimization of chemical port-Hamiltonian systems","authors":"Negareh Mahboubi,&nbsp;Junyao Xie,&nbsp;Biao Huang","doi":"10.1016/j.compchemeng.2025.109331","DOIUrl":"10.1016/j.compchemeng.2025.109331","url":null,"abstract":"<div><div>Bayesian optimization (BO) has emerged as a powerful black-box optimization approach for complex systems, making sequential decisions through Gaussian process (GP) models to explore complex search spaces. However, conventional BO faces certain challenges when applies to optimizations of chemical systems, particularly with limited measurement data and physical constraints. This paper proposes an adaptive framework combining transfer learning with physics-informed GP to enhance BO performance for chemical process optimization. By incorporating physics-based priors through Gaussian Process Port-Hamiltonian Systems (GP-PHS) in the point-by-point transfer learning methodology, the proposed approach dynamically leverages knowledge from related source domains while satisfying physical constrains. The framework’s effectiveness is demonstrated across three chemical systems including a water tank, an electrochemical cell, and an isothermal continuous stirred tank reactor (CSTR). Results show improvements in both optimization accuracy and convergence speed compared to traditional BO methods. This proposed approach bridges the gap between data-driven optimization and physical principles, offering a robust solution for complex chemical system optimization under data scarcity.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109331"},"PeriodicalIF":3.9,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863216","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}