Computers & Chemical Engineering最新文献_第4页

Big data-driven predictive control for nonlinear systems based on kernel density estimation of data trajectories 基于数据轨迹核密度估计的非线性系统大数据驱动预测控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.compchemeng.2026.109565

Shuangyu Han , Yitao Yan , Jie Bao , Biao Huang

引用次数: 0

Data compression and model reduction based approach for kinetic parameter estimation with multiple spectra 基于数据压缩和模型约简的多光谱动力学参数估计方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.compchemeng.2026.109550

Jie Zhu , Weifeng Chen , Lorenz T. Biegler

{"title":"Data compression and model reduction based approach for kinetic parameter estimation with multiple spectra","authors":"Jie Zhu , Weifeng Chen , Lorenz T. Biegler","doi":"10.1016/j.compchemeng.2026.109550","DOIUrl":"10.1016/j.compchemeng.2026.109550","url":null,"abstract":"<div><div>Estimating reaction kinetic parameters from spectral measurement data remains a critical yet unresolved challenge. Although singular value decomposition (SVD) is commonly used for spectra-based kinetic parameter estimation, the effectiveness of the estimation formulation using reduced data is not well understood. In this work, the rationale behind this formulation is supported by its derivation within a maximum likelihood framework. To address the large-scale kinetic parameter estimation problem under multiple initial conditions, a SVD-based simultaneous approach is introduced, which, in contrast to the traditional simultaneous method, avoids the direct manipulation of large-scale spectral matrices. While the specific systems of ordinary differential equations governing the reaction process vary with experimental conditions, an underlying mathematical structure is common to all. Hence, proper orthogonal decomposition is introduced to compress the model, yielding a reduced-order model for kinetic estimation. The intrinsic properties of POD make the SVD-POD simultaneous approach effective for handling weakly nonlinear reaction systems. Numerical results show that the proposed approach substantially lowers computational demands while preserving the accuracy of reaction kinetic parameter estimation from multiple spectral data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109550"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974225","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

What’s new in biomass supply chain optimization? current trends and insights 生物质供应链优化有什么新进展？当前趋势和见解

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-04-01 Epub Date: 2025-12-29 DOI: 10.1016/j.compchemeng.2025.109545

Jeremy Pantet , Ludovic Montastruc , Pierre Thiriet

{"title":"What’s new in biomass supply chain optimization? current trends and insights","authors":"Jeremy Pantet , Ludovic Montastruc , Pierre Thiriet","doi":"10.1016/j.compchemeng.2025.109545","DOIUrl":"10.1016/j.compchemeng.2025.109545","url":null,"abstract":"<div><div>Biomass has emerged as a pivotal new resource that could alleviate dependence on fossil resources and support the ecological transition by benefiting local communities. There has been an expanding literature on the subject for the past two decades. The focus of this literature is primarily on the organization and optimization of the biomass supply chain (BSC), which is the key component in providing profitable and sustainable valorized goods from biomass. The aim of this paper is to evaluate the present state of known research gaps, identify research gaps in BSC design by including economic considerations, and propose new research orientations on the subject that rely on more multidisciplinary approaches. We found three main understudied gaps. The majority of papers still only consider the strategic and tactical decision levels, excluding the operational decision level. Therefore, there are still opportunities to improve the currently accepted BSC design. The demand, as part of the supply chain, appears to be understudied. In the reviewed literature, the demand is treated as a parameter, and is perfectly met by the production, without consideration for pricing, surplus, or shortage. The other gap found is that most of the models considered in this review describe a BSC in autarky, and few take into account importations either of additional biomass or of bioproduct in their studied case, or the potential exportation of surplus. Consequently, closing these gaps in biomass supply design and optimization would facilitate the integration of BSC modeling into broader economic models.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109545"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923405","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

HOFLON: Hybrid Offline Learning and Online Optimization for process start-up and grade-transition control HOFLON：过程启动和等级转换控制的混合离线学习和在线优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.compchemeng.2026.109566

Alex Durkin , Jasper Stolte , Mehmet Mercangöz

{"title":"HOFLON: Hybrid Offline Learning and Online Optimization for process start-up and grade-transition control","authors":"Alex Durkin , Jasper Stolte , Mehmet Mercangöz","doi":"10.1016/j.compchemeng.2026.109566","DOIUrl":"10.1016/j.compchemeng.2026.109566","url":null,"abstract":"<div><div>Start-ups and product grade-changes are critical steps in continuous-process plant operation, because any misstep immediately affects product quality and drives operational losses. These transitions have long relied on supervision by a handful of expert operators, but the progressive retirement of that workforce is leaving plant owners without the tacit know-how needed to execute them consistently. In the absence of a process model, offline reinforcement learning (RL) promises to capture — and even surpass — human expertise by mining historical start-up and grade-change logs, yet standard offline RL struggles with distribution-shift and value-overestimation whenever a learned policy ventures outside the data envelope. We introduce HOFLON (Hybrid Offline Learning ＋ Online Optimization) to overcome those limitations. Offline, HOFLON learns (i) a latent data manifold that represents the feasible region spanned by past transitions and (ii) a long-horizon Q-critic that predicts the cumulative reward from state–action pairs. Online, it solves a one-step optimization problem that maximizes the Q-critic while penalizing deviations from the learned manifold and excessive rates of change in the manipulated variables. We test HOFLON on two industrial case studies—a polymerization reactor start-up and a paper-machine grade-change problem—and benchmark it against Implicit Q-Learning (IQL), a leading offline-RL algorithm. In both plants HOFLON not only surpasses IQL but also delivers on average better cumulative rewards compared to the best start-up or grade-change ever observed in the historical data, demonstrating its potential to automate transition operations beyond current expert capability.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109566"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974181","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 Hybrid Modelling of Chemical Processes: A Unified Framework Based on Deductive, Inductive, and Abductive Inference 化学过程混合模型的勘误表：基于演绎、归纳和溯因推理的统一框架

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-04-01 Epub Date: 2026-01-17 DOI: 10.1016/j.compchemeng.2026.109549

Raymoon Hwang , Jae Hyun Cho , Il Moon , Min Oh

引用次数: 0

Kinetic modelling of the CO2 capture and utilisation on NiRu-Ca/Al dual function material via parameter estimation 基于参数估计的nru - ca /Al双功能材料CO2捕集利用动力学模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.compchemeng.2025.109537

Meshkat Dolat , Andrew David Wright , Soudabeh Bahrami Gharamaleki , Loukia-Pantzechroula Merkouri , Melis S. Duyar , Michael Short

{"title":"Kinetic modelling of the CO2 capture and utilisation on NiRu-Ca/Al dual function material via parameter estimation","authors":"Meshkat Dolat , Andrew David Wright , Soudabeh Bahrami Gharamaleki , Loukia-Pantzechroula Merkouri , Melis S. Duyar , Michael Short","doi":"10.1016/j.compchemeng.2025.109537","DOIUrl":"10.1016/j.compchemeng.2025.109537","url":null,"abstract":"<div><div>This study presents a detailed, open-source kinetic modelling computational framework for CO₂ capture and utilisation using a newly formulated dual-function material (DFM) comprising 15 wt% Ni, 1 wt% Ru, and 10 wt% CaO supported on spherical alumina. A finite difference reactor model was developed to simulate the cyclic adsorption, purge, and hydrogenation stages. The model incorporates experimentally-derived rate expressions, accounts for system delay via a second-order response function, and was fitted to time-resolved concentration laboratory data using Bayesian optimisation. The robustness of the estimated parameters was rigorously assessed using Profile Likelihood Analysis (PLA), which confirmed the practical identifiability of the rate-limiting hydrogenation steps while statistically validating the masking effect of system delays on rapid adsorption kinetics. A combined parameter estimation strategy was employed to ensure mass continuity across stages and improve the robustness of purge kinetics. The kinetic parameters extracted reveal that carbonate decomposition, not methanation, is the rate-limiting step during hydrogenation. Temperature-dependent simulations confirm a trade-off between reaction kinetics and CO₂ storage capacity, with methane yield maximised at 300 °C when compared with the other temperature sets. By offering transparent methodology and reproducible code, this work provides a robust platform for researchers and practitioners to study, validate, and optimise DFM systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109537"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836405","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

Sequential KDE‑guided zero-shot regression under process changes across materials 在不同材料的工艺变化下，顺序KDE引导的零射击回归

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.compchemeng.2025.109522

Kanta Sato , Manabu Kano

{"title":"Sequential KDE‑guided zero-shot regression under process changes across materials","authors":"Kanta Sato , Manabu Kano","doi":"10.1016/j.compchemeng.2025.109522","DOIUrl":"10.1016/j.compchemeng.2025.109522","url":null,"abstract":"<div><div>This study presents a zero-shot regression framework that enables product quality prediction for a target process–material combination with no operating history. The framework facilitates scale-up and line transfer while minimizing experimental effort. A kernel density estimator sequentially selects source-process samples whose material features and product quality are similar to those on the target process, so that only relevant source data are reused. For each selected source-process sample, we predict operating conditions on the target process that would realize the same product quality for the same material, assuming that the material properties and product quality remain the same across processes. We then build a prediction model on a combined dataset consisting of the observed target-process samples and the selected source-process samples paired with their predicted target process operating conditions. A case study with two processes and seven materials demonstrates that the proposed method achieved consistent prediction accuracy; the median root mean squared error between prediction and measurement was 0.066 when using only target-process samples, 0.086 when combining target- and source-process samples without sample selection, and 0.055 with the proposed framework. The framework can successfully predict the behavior of new materials on the target process without additional experiments while suppressing negative transfer by automatically selecting and reusing existing data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109522"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836406","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

Stationarity fusion with SVM: A stationary combined features support vector machine approach for blast furnace iron-making process fault diagnosis 支持向量机与平稳融合：一种用于高炉炼铁过程故障诊断的平稳组合特征支持向量机方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.compchemeng.2025.109543

Yang Cao , Chunjie Yang , Siwei Lou , Yuelin Yang

{"title":"Stationarity fusion with SVM: A stationary combined features support vector machine approach for blast furnace iron-making process fault diagnosis","authors":"Yang Cao , Chunjie Yang , Siwei Lou , Yuelin Yang","doi":"10.1016/j.compchemeng.2025.109543","DOIUrl":"10.1016/j.compchemeng.2025.109543","url":null,"abstract":"<div><div>Blast furnace iron-making process (BFIP), constituting the core of modern steel production, presents formidable diagnostic challenges due to its inherent nonlinear dynamics and pronounced nonstationary characteristics. Addressing these challenges, we introduce the Stationary Combined Features Support Vector Machine (SCF-SVM) - a novel hybrid diagnostic framework that synergistically combines: a Differential Dynamic Feature (DDF) extraction module that precisely decouples the process’s complex temporal dynamics, and a Stationary Support Vector Machine (SSVM) classifier specifically engineered to handle nonstationary process behavior. This innovation establishes a new paradigm for BFIP condition monitoring, where the DDF component effectively captures transitional process states while the SSVM ensures robust classification under nonstationary conditions. Comprehensive validation using real-world BFIP operational data demonstrates the framework’s significant advancements over existing methods, achieving a 3.0% reduction in false alarms and a remarkable 9.5% enhancement in detection accuracy. Furthermore, we implement a dual-mode diagnostic system featuring seamless offline training-to-online deployment capability, confirming both the methodological superiority and practical viability for industrial applications.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109543"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836369","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

Data-driven source term estimation of hazardous gas leakages in complex chemical industrial parks 复杂化工园区有害气体泄漏的数据驱动源项估算

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.compchemeng.2025.109530

Chuantao Ni , Ziqiang Lang , Bing Wang , Ang Li , Chenxi Cao , Wenli Du , Feng Qian

{"title":"Data-driven source term estimation of hazardous gas leakages in complex chemical industrial parks","authors":"Chuantao Ni , Ziqiang Lang , Bing Wang , Ang Li , Chenxi Cao , Wenli Du , Feng Qian","doi":"10.1016/j.compchemeng.2025.109530","DOIUrl":"10.1016/j.compchemeng.2025.109530","url":null,"abstract":"<div><div>Hazardous gas leakage in chemical industrial parks (CIPs) can cause irreversible damage to the environment and human health. When this happens, it is crucial to perform source term estimation (STE) timely and accurately and take effective measures to reduce or prevent possible losses. To achieve real-time STE, machine learning (ML)-based STE methods have recently been developed, aiming to build a ML model to represent the relationship between sensor measurements and STE outcome to facilitate real-time applications. However, the problem with these methods is that they often cannot handle cases when sensor measurements are beyond the scope of the training dataset. To address this limitation, in the present study, a novel approach is developed in which ML is used to generate a surrogate representation of complex atmospheric transport and dispersion processes by utilizing data from a high-fidelity computational fluid dynamics (CFD) model. This surrogate ML model captures the forward relationship between hazardous gas leakage locations and rates and the resulting sensor observations, enabling efficient nonlinear optimisation for off-line STE. In addition, the study, for the first time, introduces the concept of the incremental linear response matrix to address issues with potential system nonlinearities. These approaches are evaluated on a pseudo-real concentration dataset generated by CFD simulated ethane leakage scenarios in a CIP with complex obstacles. The findings validate the effectiveness of the proposed approaches and demonstrate their superiority over existing ML-based STE methods, particularly in scenarios that extend beyond the training data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109530"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797288","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

Domain-enforced and operator-in-the-loop neural simulation platform for techno-enviro-economic performance enhancement of gas turbine system 面向燃气轮机系统技术环境经济性能提升的领域强制和操作者在环神经仿真平台

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.compchemeng.2025.109529

Waqar Muhammad Ashraf , Abdulelah S. Alshehri , Abdulrahman bin Jumah , Ghulam Moeen Uddin , Muhammad Akhtar , Vivek Dua

{"title":"Domain-enforced and operator-in-the-loop neural simulation platform for techno-enviro-economic performance enhancement of gas turbine system","authors":"Waqar Muhammad Ashraf , Abdulelah S. Alshehri , Abdulrahman bin Jumah , Ghulam Moeen Uddin , Muhammad Akhtar , Vivek Dua","doi":"10.1016/j.compchemeng.2025.109529","DOIUrl":"10.1016/j.compchemeng.2025.109529","url":null,"abstract":"<div><div>The domain-consistent and operator-centric artificial intelligence (AI) adoption has remained slow in the industrial operation of power systems, including gas power plants. This paper presents a domain-enforced and operator-in-the-loop neural simulation platform that is built upon embedding the neural surrogate models in the nonlinear optimisation framework and is implemented to analyse the operation of 395 MW capacity gas turbine system. Feed forward architecture-based neural network models like Artificial Neural Network (ANN), Data Information integrated Neural Network (DINN) and Kolmogorov-Arnold Networks (KAN) are trained to predict performance variables of gas turbine system (Power-MW, Turbine Heat Rate-kJ/kWh, Thermal Efficiency-%). KAN achieved slightly higher predictive performance on test dataset (R<sup>2</sup> ≥ 0.96) better than those of ANN (R<sup>2</sup> ≥ 0.92) and DINN (R<sup>2</sup> ≥ 0.93). Mahalanobis distance-based constraint introduces operator-in-the-loop and enforces the data-driven domain for estimating domain-consistent and energy-efficient optimised operating levels to produce a set value of power from gas turbine system. The failure modes of operation of the open-source neural simulation platform are also discussed to guide operators in estimating domain-consistent optimal operating levels. The domain-enforced neural simulation platform can reduce 2.9 kton/y [0.3 kton/y, 5.4 kton/y] of CO<sub>2</sub> emissions and may cut the annual operating cost of $ 0.95 m [$ 0.3 m, $ 1.6 m] from the operation of gas turbine system. We anticipate that the developed AI-powered simulation platform may adapt to the dynamic industrial power generation environment and enhance the access of AI and optimisation tools for data-informed decision-making for industrial applications.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"206 ","pages":"Article 109529"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797270","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