Computers & Chemical Engineering最新文献

{"title":"A cloud-enabled digital twin platform for upstream processing in biotechnology: Integrating CFD, compartmentalization, and reaction kinetics","authors":"Xiyan Li ,&nbsp;Sebastian L. Jensen ,&nbsp;Noah B. Christiansen ,&nbsp;Elham Ramin ,&nbsp;Johan le Nepvou de Carfort ,&nbsp;Johannes Schmölder ,&nbsp;Eric von Lieres ,&nbsp;Krist V. Gernaey","doi":"10.1016/j.compchemeng.2025.109488","DOIUrl":"10.1016/j.compchemeng.2025.109488","url":null,"abstract":"<div><div>Biotechnology process modeling has become an essential tool in both research and industrial settings, offering a cost-effective and efficient alternative to extensive physical experimentation. It enables the simulation and analysis of complex biological systems, supporting faster development cycles and better decision-making. To foster collaboration and knowledge sharing in this domain, we present an open source cloud-enabled platform for upstream biotechnology process modeling. The platform provides an integrated environment where users can combine computational fluid dynamics (CFD), compartmental models, and kinetic simulations within a unified and modular interface. Each component of the model operates independently, but can be seamlessly coupled through a standardized API. The system is designed to support both research and educational use cases, with an emphasis on accessibility, extensibility, and reproducibility. This paper outlines the platform architecture and implementation, highlights the technical challenges addressed in model integration, and discusses opportunities for future development. By lowering technical barriers and encouraging community-driven innovation, the platform aims to advance digital twin applications in biotechnology upstream processing.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109488"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836367","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":"Harnessing machine learning for water energy food nexus sustainability: developing a surrogate to multi-objective optimization","authors":"Fatima Mansour ,&nbsp;Michael Yehya ,&nbsp;Ali Ayoub ,&nbsp;Mahmoud Al-Hindi","doi":"10.1016/j.compchemeng.2025.109526","DOIUrl":"10.1016/j.compchemeng.2025.109526","url":null,"abstract":"<div><div>Integrated management of water, energy, and food resources is critical for achieving sustainability under rising environmental and demographic pressures, yet existing approaches either lack computational efficiency for real-time decision support or fail to capture the full complexity of sectoral interdependencies. This study presents a Water Energy Food Nexus Machine Learning based surrogate Model (WEFN-MLM). The key innovation lies in training a Random Forest algorithm on comprehensive multi-objective optimization outputs from thousands of diverse scenarios, enabling the model to learn complex nonlinear interdependencies and resource trade-offs without requiring explicit mathematical formulation of system relationships. A high-resolution Multi-Objective Optimization WEFN model (MOO-WEFN) is used to generate the training dataset, incorporating constraints for resource availability, caloric requirements, and environmental thresholds. The trained model demonstrates high predictive accuracy, with most output variables achieving R² values above 0.90 and cosine similarity scores near 1.0. Normalized absolute error analysis reveals strong performance consistency across system-level metrics, with select deviations in sector-specific outputs, particularly those highly sensitive to scenario dynamics or underrepresented in the training space. Compared to traditional optimization, the surrogate model achieves up to a 300,000-fold reduction in computation time. The surrogate model is validated using a randomly generated test set of scenarios that enables direct comparison between surrogate predictions and optimization results. The results highlight the model’s effectiveness for high-resolution nexus analysis and scenario exploration, while also acknowledging trade-offs between speed and precision. Findings underscore the importance of diverse training scenarios, careful application boundaries, and integration with policy processes to support resilient resource planning.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109526"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797268","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":"Determination of minimal target indices for cyanobacteria-based biorefineries and optimal design of the metabolic network","authors":"Romina Lasry Testa ,&nbsp;Fernando D. Ramos ,&nbsp;Matías Ramos ,&nbsp;Vanina Estrada ,&nbsp;Maria Soledad Diaz","doi":"10.1016/j.compchemeng.2025.109539","DOIUrl":"10.1016/j.compchemeng.2025.109539","url":null,"abstract":"<div><div>In this work, we propose a mixed-integer nonlinear multiobjective optimization framework for the determination of minimal target indices for the sustainable design of an integrated cyanobacteria-based biorefinery and its heat exchanger network (HEN). The potential production of phycocyanin and zeaxanthin, poly (3-hydroxybutyrate) (PHB), fourth-generation bioethanol, biogas, hydrogen and diethyl ether is analyzed. The main objective is to determine the minimal target indices (productivity, yield, titer) for <em>Synechocystis</em> sp. that must be reached to achieve a sustainable biorefinery design, by imposing lower bounds on an economic parameter (Net Present Value) and a multi-criteria sustainability metric (Sustainability Net Present Value).</div><div>For in silico strains, a bilevel optimization framework identifies gene knockouts in a genome-scale metabolic model of <em>Synechocystis</em> sp. PCC 6803 that couple product synthesis to growth. The resulting strain-specific performance indices are compared with their minimum feasible targets obtained from the process-level optimization. This integrated approach extends previous studies by combining genome-scale metabolic modelling with techno-economic and sustainability analysis within a unified optimization framework. Numerical results indicate that the minimal target indices are largely surpassed by two of the <em>in silico</em> strains, the wild-type and the coupled ethanol-producing, as well as by the <em>in vivo</em> strains. The proposed framework provides a quantitative basis to assess the feasibility of cyanobacteria-based biorefineries and to guide future metabolic engineering and process design strategies.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109539"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880238","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":"Cluster-based adaptive sampling methodology for systems modeling","authors":"Maaz Ahmad ,&nbsp;Yin Jun ,&nbsp;Marta Moreno-Benito ,&nbsp;Senthil Kumarasamy ,&nbsp;Harsha Nagesh Rao ,&nbsp;Jason Mustakis ,&nbsp;Iftekhar A Karimi","doi":"10.1016/j.compchemeng.2025.109527","DOIUrl":"10.1016/j.compchemeng.2025.109527","url":null,"abstract":"<div><div>Modeling real-world (experimental) or simulated (computational) systems using data-driven surrogate models involves selecting a sampling technique to generate the input-output data for training and selecting a surrogate form. In this work, we present a novel sampling technique, Cluster-based Adaptive Sampling, that generates training data smartly and adaptively for developing surrogate models over a given input domain. CAS iteratively clusters sampled points, defines Voronoi tessellation of cluster centroids, and approximates the tessellations using simple hypercubes. It then searches locally and globally over the domain at each iteration to identify nonlinear and under-explored regions respectively, where it samples two new points using a distance-based metric. CAS is agnostic to surrogate form and terminates automatically based on a surrogate quality metric. We assessed CAS against two existing sampling techniques on 40 diverse test functions using six surrogate forms. CAS outperformed both techniques in developing more accurate surrogates for a given computational effort and required lower computational effort for a specified accuracy across most test functions and forms. We highlight the practical applicability of CAS in modeling two pharmaceutical processes and showcase its superior performance over the two techniques.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109527"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797269","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 stability-oriented stochastic optimization strategy for refinery scheduling during unit shutdowns","authors":"Ziting Liang ,&nbsp;Zhi Li ,&nbsp;Xin Dai ,&nbsp;Yue Cao ,&nbsp;Feng Qian","doi":"10.1016/j.compchemeng.2025.109502","DOIUrl":"10.1016/j.compchemeng.2025.109502","url":null,"abstract":"<div><div>Planned unit shutdowns are critical to refinery operations. Careful scheduling is required to balance maintenance needs, safety inspections, and regulatory compliance. At the same time, it is essential to ensure production stability. Traditional refinery scheduling models primarily focus on economic objectives. The operational challenges introduced by shutdown conditions, such as variations in unit flow rates and inventory instability, are often overlooked in these models. Existing approaches predominantly utilize deterministic optimization frameworks. The frameworks fail to adequately address the uncertainties in process yields which arise from variations in feedstock properties and operational conditions. Also, Frequent unit transitions and inventory fluctuations is not considered in those frameworks. In order to overcome these limitations, a novel optimization strategy which explicitly incorporates stability considerations into shutdown scheduling is proposed in this paper. A new metric based on discrete switching event counts is introduced, which quantifies and limits the variability of unit flow rate. This metric helps reduce unnecessary adjustments and operational disruptions, as evidenced by few unit flow rate transitions observed in the optimized scheduling. Additionally, a two-stage stochastic optimization model is developed to handle unit yield uncertainties. The model improves the robustness of schedules by mitigating the impacts of uncertainty on key operational variables. The proposed method is validated using real industrial case studies. The scheduling results demonstrate that the proposed method has better performance on improving operational stability during refinery shutdowns.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109502"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145691662","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":"Parameter estimation in dynamic multiphase liquid–liquid equilibrium systems","authors":"Volodymyr Kozachynskyi ,&nbsp;Dario Staubach ,&nbsp;Erik Esche ,&nbsp;Lorenz T. Biegler ,&nbsp;Jens-Uwe Repke","doi":"10.1016/j.compchemeng.2025.109485","DOIUrl":"10.1016/j.compchemeng.2025.109485","url":null,"abstract":"<div><div>Modeling dynamic systems with a variable number of liquid phases is a challenging task, especially in scenarios where the model is designed for optimization tasks such as parameter estimation. Although there exist methods to model the appearance and disappearance of liquid phases in dynamic systems, they usually require integer variables. In this work, the smoothed continuous approach (SCA) is developed for use with a large number of solvers, since it relies only on continuous variables. To demonstrate the applicability of the new method, the SCA is then applied to model the batch esterification of acetic acid with 1-propanol to water and propyl acetate, and to estimate the reaction parameters. Since the mixture may separate into two liquid phases during the course of the reaction, the parameters are estimated with information on the liquid compositions of both separated liquid phases, which improves the accuracy of the parameter estimates and opens new possibilities for optimal experimental design.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109485"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145691663","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":"MINLP-based integrated modeling and multi-period optimization of mass-energy coupled FCC-steam systems with carbon-cost-oriented economic objective","authors":"Jian Long ,&nbsp;Bishi Zhao ,&nbsp;Kai Deng ,&nbsp;Cheng Huang ,&nbsp;Chen Fan","doi":"10.1016/j.compchemeng.2025.109503","DOIUrl":"10.1016/j.compchemeng.2025.109503","url":null,"abstract":"<div><div>With increasing global pressure to decarbonize the energy and chemical industries, the oil refining sector is undergoing a critical transformation toward green and low-carbon development. As one of the core oil refining units, the fluid catalytic cracking (FCC) process is complex. Meanwhile, it has high energy consumption and large carbon emissions. Separate optimization leads to the loss of energy and quality synergy. To address the issue of simultaneous energy and quality losses resulting from the separate optimization of the FCC and steam systems, this study models and optimizes the multi-cycle energy and quality coupling of catalytic cracking process and steam system collaboration. Based on the deep coupling of the cracking reaction and the dynamic transmission characteristics of the steam pipeline network, a multi-time-scale coupling model is established to reveal the interaction mechanism between the device and the steam system. This work develops a mathematical framework based on mixed-integer linear optimization, which aims to enhance the overall economic performance of the integrated plant, integrating the topological constraints of the pipeline network, the variable operating conditions characteristics of the equipment, and the discrete start-stop logic. Through case verification and system decoupling comparative experiments, the revenue increase of the global optimization scheme with energy and quality coupling reached 41.2 %, proving that the proposed method can effectively improve energy efficiency in the optimization scheme under the actual refinery.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109503"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145691684","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":"Safe reinforcement learning via adaptive robust model predictive shielding","authors":"Hilde Gerold,&nbsp;Sergio Lucia","doi":"10.1016/j.compchemeng.2025.109521","DOIUrl":"10.1016/j.compchemeng.2025.109521","url":null,"abstract":"<div><div>Ensuring constraint satisfaction during the deployment of reinforcement learning (RL) controllers remains a key challenge for safety-critical systems. Model predictive shielding addresses this by verifying proposed actions through predictive models and replacing unsafe ones with a backup policy, but existing approaches can be overly conservative, computationally demanding, and difficult to design for nonlinear systems with uncertainty.</div><div>We propose Adaptive Robust Model Predictive Shielding to overcome these limitations. First, we employ an approximate robust nonlinear model predictive controller as the backup policy, trained offline from multi-stage robust model predictive control data. This robust model predictive shielding approach retains safety under uncertainty while enabling real-time applicability. Second, we introduce an adaptive safety parameter in the RL observation space, allowing the agent to dynamically adjust its conservativeness. Our adaptive model predictive shielding method thus enhances safety and adapts to current uncertainty levels while avoiding excessive conservatism. When deployed with a safe backup policy, adaptive robust model predictive shielding retains safety under uncertainty and reduces unnecessary backup interventions. Simulation results for a nonlinear continuous stirred tank reactor with parametric uncertainty show that the proposed adaptive robust model predictive shielding approach reduces interventions of backup policies while still guaranteeing safety. This framework can be especially beneficial for safe RL of chemical processes where a combination of safety guarantees, high performance, and real-time feasibility is critical.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109521"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749131","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":"Graph neural networks for soft sensors: Learning from process topology and operational data","authors":"Maximilian F. Theisen,&nbsp;Gabrie M.H. Meesters,&nbsp;Artur M. Schweidtmann","doi":"10.1016/j.compchemeng.2025.109532","DOIUrl":"10.1016/j.compchemeng.2025.109532","url":null,"abstract":"<div><div>Soft sensors estimate process variables that are difficult or impossible to measure directly by using mathematical models and available sensor data, e.g., product concentrations. Machine learning-based approaches have become popular for soft sensing tasks. These approaches offer automatic modeling using historical process data but lack basic process information, such as the process topology. This can lead to (1) modeling of correlations instead of causation between process measurements, (2) model deterioration in deployment due to unseen process scenarios, and (3) large data requirements. To overcome these shortcomings, we propose a novel ML modeling approach incorporating the process topology into soft sensor models for improved spatio-temporal modeling. For this, we propose process topology-aware graph neural networks. We combine process topology and sensor data by representing process data in a directed graph and leverage these process graphs to train graph neural networks. Our method demonstrates enhanced model robustness, reduced data requirements, and more intuitive data representations compared to standard black-box machine learning modeling approaches. Overall, this work introduces a new paradigm for soft sensing by directly embedding process information into the data, paving the way for more efficient and reliable digital twin applications.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109532"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880236","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":"Improving the precision of crude oil prices using hybrid modeling methods","authors":"Chunyu Du ,&nbsp;Wenbo Ma","doi":"10.1016/j.compchemeng.2025.109541","DOIUrl":"10.1016/j.compchemeng.2025.109541","url":null,"abstract":"<div><div>Energy security, investment choices, and risk management all depend on accurate crude oil price forecasting, which is still challenging because oil markets are nonlinear, volatile, and non-stationary. The current work posits a hybrid forecasting framework using the Multivariate Empirical Mode Decomposition-Barnacles Mating Optimizer- Interpretable Feature Temporal Self-Attention Transformer (MEMD-BMO-IFTT) model. The Interpretable Feature Temporal Self-Attention Transformer is used for implicitly capturing long-term temporal dependencies, the Barnacles Mating Optimizer is used for effective parameter optimization, and Multivariate Empirical Mode Decomposition is used for extracting multi-scale, informative features. The model was tested against prevalent algorithms such as Extreme Gradient Boosting with Random Forest, Extended Long Short-Term Memory, an IFTT, and MEMD-FA-IFTT based on June 2014 to October 2023 daily technical indicators, fundamental indicators, and trading volume data. It is observed that MEMD-BMO-IFTT is more efficient in the prediction process with a test coefficient of determination of 0.9937. While real-world backtesting demonstrated its usefulness by producing higher cumulative returns, lower drawdowns, and stronger risk-adjusted performance when compared to a buy-and-hold strategy, out-of-sample testing validated its generalization to unseen data. The MEMD-BMO-IFTT framework provides a reliable, understandable, and practically useful solution for forecasting crude oil prices. This novel hybrid model can provide a valuable tool for researchers and investors in forecasting other financial markets.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109541"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880237","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}