Computers & Chemical Engineering最新文献_第10页

Oil production forecasting using temporal Kolmogorov–Arnold networks 利用时间Kolmogorov-Arnold网络进行石油产量预测

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-11-14 DOI: 10.1016/j.compchemeng.2025.109483

Mengze Zheng, Tao Zhang, Jing Cao, Zhong Chen, Jian Zou

{"title":"Oil production forecasting using temporal Kolmogorov–Arnold networks","authors":"Mengze Zheng, Tao Zhang, Jing Cao, Zhong Chen, Jian Zou","doi":"10.1016/j.compchemeng.2025.109483","DOIUrl":"10.1016/j.compchemeng.2025.109483","url":null,"abstract":"<div><div>Given the challenges associated with resource depletion and market volatility, accurate oil production forecasting has become a critical component for optimizing oilfield development and enhancing decision-making processes. In recent years, various machine learning and deep learning methods have been widely adopted. However, these approaches still exhibit significant limitations in terms of accuracy and generalizability, often failing to fully capture the complexities of dynamic reservoir environments and multivariate datasets. To address these challenges, we propose the temporal Kolmogorov–Arnold networks (TKAN), a novel deep learning architecture specifically designed for multivariate time-series forecasting in the context of oil production. TKAN integrates Kolmogorov–Arnold decomposition with adaptive spline-enhanced activation functions, enabling the model to effectively capture nonlinear relationships and temporal dependencies. This leads to substantial improvements over conventional techniques when dealing with noisy and dynamic datasets. In the experimental section, the proposed TKAN model is utilized to predict oil production in the Volve Field. A comparative analysis with benchmark models, such as random forest, long short-term memory networks (LSTM), temporal fusion transformer (TFT), and Kolmogorov–Arnold Networks (KAN) demonstrates the superiority of TKAN. These results confirm that TKAN not only retains the lightweight advantages of KAN but also significantly improves predictive accuracy by incorporating temporal modeling, underscoring its potential for time series prediction in the oil and gas industry.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109483"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517214","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

Linear dynamic operability analysis with state-space projection for the online construction of achievable output funnels 基于状态空间投影的可实现输出通道在线构建的线性动态可操作性分析

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-09 DOI: 10.1016/j.compchemeng.2025.109428

San Dinh , Fernando V. Lima

{"title":"Linear dynamic operability analysis with state-space projection for the online construction of achievable output funnels","authors":"San Dinh , Fernando V. Lima","doi":"10.1016/j.compchemeng.2025.109428","DOIUrl":"10.1016/j.compchemeng.2025.109428","url":null,"abstract":"<div><div>This study presents the development of a dynamic operability analysis approach to determine an operable output funnel for linear time-invariant dynamic systems. Traditional operability mapping approaches are computationally expensive, limiting their application for online control. To address this challenge, a novel two-step calculation procedure is proposed in this article. The first step involves offline computation of the nominal funnel through convex hull construction of manipulated variable projections. The second step involves an online update that adapts the nominal funnel to an operable region based on current state information. The proposed method results in a dynamic funnel that can accommodate process disturbances and measurement noises in the form of transient output constraints. The obtained funnel can be effectively used for model predictive control applications. To demonstrate the effectiveness of the proposed framework, the cyber–physical fuel cell-gas turbine hybrid power system in the HYbrid PERformance (HYPER) process from NETL is used as an example in this study. The dynamic operability funnel constructed with the novel method requires a significantly smaller number of dynamic simulations when compared to the conventional operability mapping method, while maintaining similar accuracy. The results obtained using the proposed approach demonstrate its potential for improving the online control of dynamic systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109428"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265073","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

Learning to control inexact Benders decomposition via reinforcement learning 学习通过强化学习控制不精确的bender分解

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-28 DOI: 10.1016/j.compchemeng.2025.109461

Zhe Li , Bernard T. Agyeman , Ilias Mitrai , Prodromos Daoutidis

{"title":"Learning to control inexact Benders decomposition via reinforcement learning","authors":"Zhe Li , Bernard T. Agyeman , Ilias Mitrai , Prodromos Daoutidis","doi":"10.1016/j.compchemeng.2025.109461","DOIUrl":"10.1016/j.compchemeng.2025.109461","url":null,"abstract":"<div><div>Benders decomposition (BD), along with its generalized version (GBD), is a widely used algorithm for solving large-scale mixed-integer optimization problems that arise in the operation of process systems. However, the off-the-shelf application to online settings can be computationally inefficient due to the repeated solution of the master problem. An approach to reduce the solution time is to solve the master problem to local optimality. However, identifying the level of suboptimality at each iteration that minimizes the total solution time is nontrivial. In this paper, we propose the application of reinforcement learning to determine the best optimality gap at each GBD iteration. First, we show that the inexact GBD can converge to the optimal solution given a properly designed optimality gap schedule. Next, leveraging reinforcement learning, we learn a policy that minimizes the total solution time, balancing the solution time per iteration with optimality gap improvement. In the resulting RL-iGBD algorithm, the policy adapts the optimality gap at each iteration based on the features of the problem and the solution progress. In numerical experiments on a mixed-integer economic model predictive control problem, we show that the proposed RL-enhanced iGBD method achieves substantial reductions in solution time.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109461"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145413890","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

Assessing, diagnosing, and benchmarking control loops using the input-output cross autocorrelation diagram (IO-CAD) 使用输入-输出交叉自相关图（IO-CAD）评估、诊断和基准测试控制回路

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-11 DOI: 10.1016/j.compchemeng.2025.109438

Leonardo M. De Marco , Jorge Otávio Trierweiler , Fabio Cesar Diehl , Marcelo Farenzena

{"title":"Assessing, diagnosing, and benchmarking control loops using the input-output cross autocorrelation diagram (IO-CAD)","authors":"Leonardo M. De Marco , Jorge Otávio Trierweiler , Fabio Cesar Diehl , Marcelo Farenzena","doi":"10.1016/j.compchemeng.2025.109438","DOIUrl":"10.1016/j.compchemeng.2025.109438","url":null,"abstract":"<div><div>Monitoring the control loop performance is crucial for operation efficiency and safety in industrial processes. This study proposes a new methodology for control loop performance assessment based on the Input-Output Cross Autocorrelation Diagram (IO<img>CAD), a technique already established in the literature. In this work, two novel indicators based on a polar representation of IO<img>CAD are introduced, complementing four existing indicators previously developed using a Cartesian formulation. By analyzing the autocorrelation between the process variable (PV) and manipulated variable (MV), these indicators enable performance evaluation using only routine plant data. Compared to traditional approaches such as the Minimum Variance Control (MVC), the IO<img>CAD-based method shows greater robustness to noise and setpoint changes, while also providing diagnostic insights into the root causes of performance degradation, such as tuning issues or changes in process dynamics. A Control Performance Indicator (CPI) was also proposed. Simulations involving various control loops, including an offshore oil production control loop, confirmed the method’s effectiveness and applicability for real-time monitoring in diverse operational scenarios.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109438"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265072","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

Corrigendum to “Behavioral strategies evolution of stakeholders for wastewater recycling in eco-industrial parks under financial constraints” [Computers & Chemical Engineering, 2025, 204: 109402] “资金约束下生态工业园区废水循环利用的利益相关者行为策略演变”[j] .计算机与化学工程，2025,04:109402。

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-29 DOI: 10.1016/j.compchemeng.2025.109460

Kaixuan Zhang , Xu Han

引用次数: 0

Sustainable by design: A first attempt on bioprocessing 可持续设计：生物处理的第一次尝试

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-15 DOI: 10.1016/j.compchemeng.2025.109454

Miriam Sarkis , Mariana Monteiro , Andrea Bernardi , Ranjith Chiplunkar , Cleo Kontoravdi , Maria M. Papathanasiou

{"title":"Sustainable by design: A first attempt on bioprocessing","authors":"Miriam Sarkis , Mariana Monteiro , Andrea Bernardi , Ranjith Chiplunkar , Cleo Kontoravdi , Maria M. Papathanasiou","doi":"10.1016/j.compchemeng.2025.109454","DOIUrl":"10.1016/j.compchemeng.2025.109454","url":null,"abstract":"<div><div>The growing commitment of the biopharmaceutical sector to transition to Net Zero is driving the industry to embed sustainability principles across its entire pipeline of operations from early-stage process development to manufacturing. In this context, a key challenge for process design is the prediction of the impact of upstream variability on downstream process performance and, therefore, design, with effects on process economics and sustainability. In this work, we focus on the economic and sustainability analysis of antibody-producing bioprocess designs in the presence and absence of downstream process performance constraints. Specifically, we introduce a kinetic model of upstream processing that predicts the profile of critical cell-derived and product-associated impurities and their variability based on culture conditions. Upstream model simulation results are then used to inform a superstructure optimization that maximizes monoclonal antibody (mAb) throughput under purity constraints. Flowsheet simulation models of the candidate designs are developed and process performance is evaluated through techno-economic and life cycle assessment. As expected, results show that purity constraints can lead to more complex downstream configurations, with higher nominal costs and footprint, and improved capacity to withstand feedstock variability. Although intuitive, the results highlight the significance of uncertainty quantification and impurity modeling for informing end-to-end process design. The digitally-enabled holistic approach proposed herein comprehensively enables cost-effective, eco-efficient, and uncertainty-aware design decisions in bioprocessing.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109454"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360246","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

Techno-economic analysis and life cycle assessment of a novel algae-based CCUS technology 基于藻类的新型CCUS技术的技术经济分析与生命周期评估

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-10 DOI: 10.1016/j.compchemeng.2025.109409

Swaminathan Sundar, Rahul Kakodkar, Efstratios N. Pistikopoulos

{"title":"Techno-economic analysis and life cycle assessment of a novel algae-based CCUS technology","authors":"Swaminathan Sundar, Rahul Kakodkar, Efstratios N. Pistikopoulos","doi":"10.1016/j.compchemeng.2025.109409","DOIUrl":"10.1016/j.compchemeng.2025.109409","url":null,"abstract":"<div><div>The energy sector is a major contributors of greenhouse gases and thus decarbonizing this sector is pivotal towards achieving carbon neutrality. Carbon Capture, Utilization and Sequestration (CCUS) technologies offers a promising pathway in mitigation of these emissions. In particular, valorization of the captured carbon into value added products can enhance the economic viability and scalability of some of the novel CCUS processes. Among these, algae based CCUS process is one such promising solution which has the potential to feature in future energy systems. In this study, we conduct a detailed Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA) of an algae-based CCUS process at scale. Sensitivity analysis was also carried out to identify critical bottlenecks that hinder the scale up of this process. The levelized cost of biomass production was estimated to be $388 per ton of biomass and the levelized emission was found to be 1.3 kg CO<sub>2</sub> per kg biomass. Based on a detailed Discount Cash Flow analysis, the minimum biomass selling price was estimated to be $424 per ton of biomass.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109409"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360247","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

Multi-objective optimization of phosphoric acid production unit, to minimize P2O5 losses 磷酸生产装置的多目标优化，以减少P2O5的损失

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-31 DOI: 10.1016/j.compchemeng.2025.109477

Ahmed Bichri , Yousra Jbari , Souad Abderafi

{"title":"Multi-objective optimization of phosphoric acid production unit, to minimize P2O5 losses","authors":"Ahmed Bichri , Yousra Jbari , Souad Abderafi","doi":"10.1016/j.compchemeng.2025.109477","DOIUrl":"10.1016/j.compchemeng.2025.109477","url":null,"abstract":"<div><div>The attack and maturation unit represents the key step in the wet phosphoric acid production process. It must ensure optimal conversion of natural phosphate into phosphoric acid, while minimizing P₂O₅ losses, to improve the overall efficiency of the process. The aim of this study consists to reduce the three P₂O₅ losses (water-soluble, co-crystallized and non-attacked), in the phosphoric acid production attack and maturation unit. An industrial database, relating to the three losses of filters A and B as a function of the operating variables of the wet phosphoric acid production process, was analyzed. After processing, it was used for the development of an appropriate model, using the artificial neural network to predict the three P₂O₅ losses. The developed model with an optimal structure (5–3–6) presented good statistical performance. After validation of the model, it was used as an objective function for the NSGA-III algorithm to perform a multi-objective optimization, to minimize P₂O₅ losses. The results obtained lead to an improvement in the overall efficiency, which reaches 95.56 %, i.e. a gain of 0.34 % compared to the average observed efficiency.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109477"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145463215","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

L-TKAN:A fast and accurate Laplacian radial basis function-Based Temporal Kolmogorov–Arnold Network for state of charge estimation of lithium-ion batteries L-TKAN：一种快速准确的基于拉普拉斯径向基函数的锂离子电池充电状态估计时态Kolmogorov-Arnold网络

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-11-10 DOI: 10.1016/j.compchemeng.2025.109478

Zhiqiang Liu , Chong Kuai , Gang Wu , Ashun Zang

{"title":"L-TKAN:A fast and accurate Laplacian radial basis function-Based Temporal Kolmogorov–Arnold Network for state of charge estimation of lithium-ion batteries","authors":"Zhiqiang Liu , Chong Kuai , Gang Wu , Ashun Zang","doi":"10.1016/j.compchemeng.2025.109478","DOIUrl":"10.1016/j.compchemeng.2025.109478","url":null,"abstract":"<div><div>Traditional SOC estimation methods are susceptible to temperature variations and external disturbances, while conventional data-driven methods can effectively deal with these issues but struggle with nonlinear issues. The Kolmogorov–Arnold Network (KAN), a novel network configuration, demonstrates excellent performance in handling nonlinear problems. Nevertheless, the use of B-spline functions in KAN results in slow training speed. This paper proposes a novel neural architecture network, L-TKAN, by replacing the B-spline function (RMSE1.41%,MAE1.12%,R<sup>2</sup>0.997444) with the Laplacian radial basis function (Laplacian RBF) (RMSE1.25%,MAE0.92%,R<sup>2</sup>0.997988) to address this issue. The corresponding 300-epoch training times of these two configurations were 288 min and 375 min, respectively. The results demonstrate that the use of the Laplacian RBF not only significantly accelerates the training speed but also improves the model performance. Moreover, compared to the traditional Gaussian RBF, the Laplacian RBF also exhibits these two advantages (RMSE 1.35% and 1.39%; MAE 1% and 1.04%; R<sup>2</sup> 0.997657 and 0.997524, the shortest training time of 288 min and 294 min, respectively). During the experiments, we discovered the relationship between two key parameters (the number of center points and the decay factor <span><math><mi>σ</mi></math></span>) of the Laplacian RBF. Based on this finding, we further improved the performance of the model-When the number of center points was 2, the RMSE decreased 0.06%. Furthermore, the best performance was achieved with 1 center point, yielding an RMSE of 1.25%, MAE of 0.92%, and R<sup>2</sup> of 0.997988. The proposed model also outperforms other models, such as KAN, MLP, LSTM, GRU, TCN, CNNLSTM, CNNGRU, transformer, CNNtransformer.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109478"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517223","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

Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models 基于不确定性感知代理模型的段塞流结晶器多级模型预测控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-02-01 Epub Date: 2025-10-15 DOI: 10.1016/j.compchemeng.2025.109456

Collin R. Johnson , Stijn de Vries , Kerstin Wohlgemuth , Sergio Lucia

{"title":"Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models","authors":"Collin R. Johnson , Stijn de Vries , Kerstin Wohlgemuth , Sergio Lucia","doi":"10.1016/j.compchemeng.2025.109456","DOIUrl":"10.1016/j.compchemeng.2025.109456","url":null,"abstract":"<div><div>This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The developed model can accurately describe the main characteristics of slug flow crystallizers, including slug-to-slug variability but leads to a high computational complexity due to the consideration of partial differential equations and population balance equations. For that reason, the model cannot be directly used for process optimization and control. To solve this challenge, we propose two different approaches, conformalized quantile regression and Bayesian last layer neural networks, to develop surrogate models with uncertainty quantification capabilities. These surrogates output a prediction of the system states together with an uncertainty of these predictions to account for process variability and model uncertainty. We use the uncertainty of the predictions to formulate a robust model predictive control approach, enabling robust real-time advanced control of a slug flow crystallizer.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109456"},"PeriodicalIF":3.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322886","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