Computers & Chemical Engineering最新文献_第9页

Comparing separation alternatives for recovering 2,3 butanediol in a biorefinery: Multi-objective optimization of economic and environmental performance 比较生物精炼厂回收2,3丁二醇的分离方法：经济和环境性能的多目标优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-25 DOI: 10.1016/j.compchemeng.2025.109302

Marco Avendano, Jianpei Lao, Qiang Fu, Sankar Nair, Matthew J. Realff

{"title":"Comparing separation alternatives for recovering 2,3 butanediol in a biorefinery: Multi-objective optimization of economic and environmental performance","authors":"Marco Avendano, Jianpei Lao, Qiang Fu, Sankar Nair, Matthew J. Realff","doi":"10.1016/j.compchemeng.2025.109302","DOIUrl":"10.1016/j.compchemeng.2025.109302","url":null,"abstract":"<div><div>In this study, two separation pathways for recovering 2,3-butanediol (BDO) from a dilute aqueous fermentation stream were compared: distillation and simulated moving bed (SMB) adsorption followed by distillation. A multi-objective optimization framework employing Pareto fronts was used to evaluate the economic and environmental impact of integrating each pathway into a biorefinery producing renewable fuel from corn stover-derived BDO. Minimum fuel selling price (MFSP) and lifecycle greenhouse gas (GHG) emissions of the renewable fuel were chosen as the objective functions. Using a single distillation column to recover BDO resulted in GHG emissions of 43 g<sub>CO2e</sub> (CO<sub>2</sub> equivalent)/MJ, ∼50 % lower than petroleum-based fuel (93 g<sub>CO2e</sub>/MJ), but an MFSP of $2.55/GGE (gallon of gasoline equivalent, 1GGE = 121.3 MJ), exceeding the $2.50/GGE threshold set by the U.S. Department of Energy. In comparison, the SMB system lowered energy demand in the distillation columns and reduced GHG emissions to 18 g<sub>CO2e</sub>/MJ and MFSP to $2.40/GGE. Further exploration of this pathway led to the use of heat pumps to replace fossil-based steam utilities in the distillation columns. The final separation process design recommendation was an SMB system followed by two distillation columns, with only one column electrified by a mechanical vapor recompression (MVR) heat pump loop. This configuration attained GHG emissions of 13 g<sub>CO2e</sub>/MJ and an MFSP of $2.50/GGE. These findings highlight the economic and environmental advantages of employing a material-based separation such as SMB adsorption and integrating heat pump-assisted distillation to efficiently reduce reliance on fossil-based utilities.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109302"},"PeriodicalIF":3.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758008","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

Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses 生物过程中高效鲁棒多设定值多轨迹跟踪的强化学习

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-25 DOI: 10.1016/j.compchemeng.2025.109297

Sebastián Espinel-Ríos , José L. Avalos , Ehecatl Antonio del Rio Chanona , Dongda Zhang

{"title":"Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses","authors":"Sebastián Espinel-Ríos , José L. Avalos , Ehecatl Antonio del Rio Chanona , Dongda Zhang","doi":"10.1016/j.compchemeng.2025.109297","DOIUrl":"10.1016/j.compchemeng.2025.109297","url":null,"abstract":"<div><div>Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-learning framework for multi-setpoint and multi-trajectory tracking. Tracking multiple setpoints and time-varying trajectories in reinforcement learning is challenging due to the complexity of balancing multiple objectives, a difficulty further exacerbated by system uncertainties such as uncertain initial conditions and stochastic dynamics. This challenge is relevant, e.g., in bioprocesses involving microbial consortia, where precise control over population compositions is required. We introduce a novel return function based on multiplicative reciprocal saturation functions, which explicitly couples reward gains to the simultaneous satisfaction of multiple references. Through a case study involving light-mediated cybergenetic growth control in microbial consortia, we demonstrate via computational experiments that our approach achieves faster convergence, improved stability, and superior control compliance compared to conventional quadratic-cost-based return functions. Moreover, our method enables tuning of the saturation function’s parameters, shaping the learning process and policy updates. By incorporating system uncertainties, our framework also demonstrates robustness, a key requirement in industrial bioprocessing. Overall, this work advances reinforcement-learning-based control strategies in bioprocess engineering, with implications in the broader field of process and systems engineering.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109297"},"PeriodicalIF":3.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722698","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-period Carbon Capture, Utilization, and Storage (CCUS) infrastructure design and planning under uncertainty 不确定性下的多期碳捕集、利用与封存（CCUS）基础设施设计与规划

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-25 DOI: 10.1016/j.compchemeng.2025.109300

Chinmay M. Aras , M.M. Faruque Hasan

{"title":"Multi-period Carbon Capture, Utilization, and Storage (CCUS) infrastructure design and planning under uncertainty","authors":"Chinmay M. Aras , M.M. Faruque Hasan","doi":"10.1016/j.compchemeng.2025.109300","DOIUrl":"10.1016/j.compchemeng.2025.109300","url":null,"abstract":"<div><div>The design of large-scale Carbon Capture, Utilization, and Storage (CCUS) networks is nontrivial due to large number of sources and sinks and their potential connectivity. The deployment of capture plants and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> transportation pipelines is a gradual process with facilities coming online at different points in time. Uncertainties in underground storage volumes for CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> sequestration and enhanced oil recovery pose additional challenges. In this work, we employ a multi-period model formulation and a stochastic optimization approach combined with a rolling horizon-based solution strategy to find optimal CCUS network designs and infrastructure deployment schedules under a wide range of future realizations of the geological storage volumes. We also consider constraint on the budget allocation for each period. Our results for a case study on CCUS for the Illinois Basin region in the U.S. show that selecting capture facilities with high CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions is both economically and environmentally favorable. Utilization provides a strong economic incentive and is favorable towards the early deployment of CCUS at sites that can generate revenue through enhanced oil recovery. Accounting for uncertainties in geological storage volumes enable recourse actions such as building additional pipeline connections to mitigate undesirable deviations from the planned capture levels. These indicate that incorporating uncertainties in CCUS network design and the planning of the deployment of such infrastructure is important to reduce deviations from the expected capture amount as compared to a deterministic approach.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109300"},"PeriodicalIF":3.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713478","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

Optimizing carbon capture and storage infrastructure including physics-based reservoir modelling 优化碳捕获和储存基础设施，包括基于物理的储层建模

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-24 DOI: 10.1016/j.compchemeng.2025.109293

Luca Bertoni , Olav Møyner , Jan Wiegner , Matteo Gazzani

{"title":"Optimizing carbon capture and storage infrastructure including physics-based reservoir modelling","authors":"Luca Bertoni , Olav Møyner , Jan Wiegner , Matteo Gazzani","doi":"10.1016/j.compchemeng.2025.109293","DOIUrl":"10.1016/j.compchemeng.2025.109293","url":null,"abstract":"<div><div>The deployment of carbon capture and storage (CCS) requires a potentially complex infrastructure to transport and store CO<sub>2</sub> underground. Its optimal roll-out is key to limiting costs and enabling a timely deployment in line with ambitious mitigation scenarios. However, identifying the optimal design of the infrastructure is computationally challenging. Within this framework, mixed-integer linear programming (MILP) offers a computationally effective solution, which, however, requires linear models. Not surprisingly, geological reservoirs are typically represented as static sinks with constant injection rates and storage capacities as parameters. This approach neglects the dynamic properties of CO<sub>2</sub> injection, such as the reservoir pressure evolution over time, limiting the ability to evaluate their impact on the CCS chain design.</div><div>In this work, we propose a novel MILP model that integrates physics-based reservoir modelling into CCS chain optimization. Extending existing work on reduced-order modelling of reservoirs, we combine proper orthogonal decomposition and trajectory piecewise linearization to obtain a precise, yet computational efficient MILP model for the dynamic behaviour of CO<sub>2</sub> injection. Compared to full-scale reservoir simulations, the model computes the pressure around the injection well with <span><math><mo>±</mo></math></span>5% accuracy and significant computational speed-ups (500-1800 times faster). We demonstrate its application in a full chain MILP model with an illustrative case study optimizing the decarbonization of a small industrial cluster through CCS, highlighting the model’s ability to couple optimal operation of capture technologies with varying injection rates and to ensure the reservoir safety constraints while designing the chain.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109293"},"PeriodicalIF":3.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711294","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

Balancing stakeholder objectives in the water-energy-food nexus through dimension reduction and multi-actor decision strategies 通过降维和多参与者决策策略平衡水-能源-食物关系中的利益相关者目标

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-23 DOI: 10.1016/j.compchemeng.2025.109310

Daniel Peña-Torres , Marianne Boix , Ludovic Montastruc

{"title":"Balancing stakeholder objectives in the water-energy-food nexus through dimension reduction and multi-actor decision strategies","authors":"Daniel Peña-Torres , Marianne Boix , Ludovic Montastruc","doi":"10.1016/j.compchemeng.2025.109310","DOIUrl":"10.1016/j.compchemeng.2025.109310","url":null,"abstract":"<div><div>Water, energy, and food are critical resources required to meet basic human needs, ensure economic development, and achieve sustainable development goals. However, the effects of rapid population growth and climate change have resulted in increased demand for these resources. Furthermore, water, energy, and food are highly interrelated, presenting both synergisms and trade-offs along their supply chains. The connection of these resources and the study of their interdependencies have been addressed in the literature as the Water Energy Food Nexus (WEFN). A critical aspect of WEFN systems is that they are integrated by multiple actors with competing goals and concerns. When addressing WEFN systems with optimisation tools, the incorporation of stakeholders’ interests increases the dimensions of the problem. In this study, we propose a dimensionality reduction approach based on a post-optimal analysis by imposing non-negativity constraints on specific actors’ economic goals. From the new problem with reduced dimensions, the TOPSIS multi-criteria decision analysis (MCDA) method was employed to select compromise solutions for the configuration of the WEFN system under different scenarios with varying importance for the analysed criteria. By coupling MCDA tools with dimensionality reduction strategies, this approach allows the analysis of fewer system configurations for reaching compromise without losing information when reducing the decision space. The study highlights the necessity for future research to adopt participatory approaches and consider societal opinions and concerns, thereby ensuring public acceptance in resource allocation and overall WEFN system management.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109310"},"PeriodicalIF":3.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713153","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

MILP reformulation and extension of multi-echelon inventory optimization model based on the guaranteed service approach 基于保证服务方法的多级库存优化模型的MILP重构与扩展

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-21 DOI: 10.1016/j.compchemeng.2025.109305

L.G. Porporatto , V.G. Achkar , Y. Fumero , B.B. Brunaud , N. Torres , G. Corsano

引用次数: 0

A decomposition algorithm using multiple linear approximations to solve integrated design and scheduling optimization frameworks — case study of nuclear based co-production of electricity and hydrogen 一种利用多重线性逼近求解集成设计和调度优化框架的分解算法——以核能电氢联产为例

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-21 DOI: 10.1016/j.compchemeng.2025.109213

Taemin Heo , Dharik S. Mallapragada , Ruaridh Macdonald

{"title":"A decomposition algorithm using multiple linear approximations to solve integrated design and scheduling optimization frameworks — case study of nuclear based co-production of electricity and hydrogen","authors":"Taemin Heo , Dharik S. Mallapragada , Ruaridh Macdonald","doi":"10.1016/j.compchemeng.2025.109213","DOIUrl":"10.1016/j.compchemeng.2025.109213","url":null,"abstract":"<div><div>This study introduces a modeling framework for optimizing integrated design and scheduling (IDS) of grid-interactive facilities with heat and mass integration and multiple co-products. Using a novel decomposition algorithm utilizing multiple linear approximations, the framework enables efficient optimization over 8760 h of annual operation while including a large number of nonlinear constraints, improving scalability and performance compared to previous methods. We apply the framework to optimize a nuclear power plant (NPP) and high-temperature steam electrolysis (HTSE) co-production system. The case study shows that co-production can be cost-competitive with standalone HTSE under realistic scenarios. We perform a sensitivity analysis of the cost of H<sub>2</sub> as a function of HTSE capital costs and current density limits, which suggests that early R&D should focus on increasing current density to 3–4 A/cm<sup>2</sup> before targeting cost reductions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109213"},"PeriodicalIF":3.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685690","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

State-space-guided neural networks for fault detection 状态空间导向的故障检测神经网络

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-21 DOI: 10.1016/j.compchemeng.2025.109260

A. Carter , A. Rezaei , S. Imtiaz , G. Naterer

{"title":"State-space-guided neural networks for fault detection","authors":"A. Carter , A. Rezaei , S. Imtiaz , G. Naterer","doi":"10.1016/j.compchemeng.2025.109260","DOIUrl":"10.1016/j.compchemeng.2025.109260","url":null,"abstract":"<div><div>This article investigates the use of state-space models to enhance neural networks for fault detection in engineering systems. In modern control theory, it is well-established that a nonlinear system can be maintained at a setpoint using a linearized state-space model to approximate system dynamics. This concept is adapted to state-space-guided neural networks (SSGNNs), where a simplified state-space model provides an imperfect approximation of the system state, which is then utilized within a physics-guided neural network (PGNN) framework. By incorporating state-space model estimates into the feature space, the SSGNN can capture intricate patterns and relationships that purely data-driven models might miss. This augmented feature space allows the neural network to learn characteristic relationships between measurements and state-space model estimates, enhancing fault detection capabilities. The methodology emphasizes on guiding a machine learning model with simplified and easily discoverable governing equations while still achieving high fault detection accuracy. This study demonstrates that SSGNNs offer improved fault detection performance compared to benchmark neural networks, using both simulated and laboratory data. These findings encourage further research into hybrid physics-guided machine learning to enhance reliable fault detection in industrial systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109260"},"PeriodicalIF":3.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738229","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

Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part I: Conceptual framework and equation generation 使用强化学习的化学过程数字孪生的机械模型的自动生成。第一部分：概念框架和方程生成

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-20 DOI: 10.1016/j.compchemeng.2025.109281

Mathis Heyer , Jiyizhe Zhang , Naoto Sugisawa , Jan-Frederic Laub , Alexei A. Lapkin

{"title":"Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part I: Conceptual framework and equation generation","authors":"Mathis Heyer , Jiyizhe Zhang , Naoto Sugisawa , Jan-Frederic Laub , Alexei A. Lapkin","doi":"10.1016/j.compchemeng.2025.109281","DOIUrl":"10.1016/j.compchemeng.2025.109281","url":null,"abstract":"<div><div>Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent model updates and ask for automation of this modeling process. In this work, we propose an automated workflow that generates accurate mechanistic reactor models from experimental concentration data of a given reactor. At the core of this workflow, a reinforcement learning agent assembles an interpretable reactor model by iteratively simplifying general differential balance equations and fitting the resulting candidate model to experimental data. We demonstrate the performance of our workflow in two case studies. An <em>in silico</em> case study shows that the workflow correctly reconstructs the model underlying a synthetic data set, is robust against noise in the input data, and has favorable scaling properties. The agent accelerates the model derivation process significantly compared to an exhaustive enumerative search. Secondly, an experimental case study is conducted employing a Taylor-Couette prototype reactor. A liquid-phase esterification reaction of (2-bromophenyl)methanol and acetic anhydride was used as a test system. Based on the experimental data, the workflow derives meaningful mechanistic models, with the most accurate model showing a normalized root mean squared error of 2.4%. Future work encompasses the integration of automated experiments into the workflow and the transfer of our workflow to process units beyond chemical reactors.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109281"},"PeriodicalIF":3.9,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722697","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

A deep learning framework for cyberattack detection and classification in Industrial Control Systems 工业控制系统中网络攻击检测与分类的深度学习框架

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-07-19 DOI: 10.1016/j.compchemeng.2025.109278

Malhar Barbhaya , Purushottama Rao Dasari , Seshu Kumar Damarla , Rajagopalan Srinivasan , Biao Huang

{"title":"A deep learning framework for cyberattack detection and classification in Industrial Control Systems","authors":"Malhar Barbhaya , Purushottama Rao Dasari , Seshu Kumar Damarla , Rajagopalan Srinivasan , Biao Huang","doi":"10.1016/j.compchemeng.2025.109278","DOIUrl":"10.1016/j.compchemeng.2025.109278","url":null,"abstract":"<div><div>The rapid integration of network-based control systems vulnerabilities within Industrial Control Systems (ICS) has increased exposure to sophisticated cyberattacks, especially in the chemical process industry. Adversaries exploit these systems by manipulating sensor data, disrupting operations, and compromising safety while remaining undetected by conventional fault detection mechanisms. Cyberattacks on critical infrastructure have become the new normal, with the World Economic Forum (WEF) ranking cyber threats as the seventh highest global risk in terms of likelihood over the next decade. Additionally, cybercrime has surged by 600% since COVID-19, highlighting the urgency of robust cybersecurity frameworks. This research introduces a hybrid cybersecurity framework combining an enhanced Typicality and Eccentricity Data Analytics (TEDA) algorithm with a Convolutional Neural Network (CNN) for real-time cyberattack detection and classification in ICS. The enhanced TEDA algorithm leverages a sliding window mechanism for adaptive statistical analysis and employs a characteristic model for detecting sophisticated cyber threats, enabling rapid anomaly identification and mitigation without requiring extensive historical data. Simultaneously, the CNN classifier accurately identifies attack types, facilitating timely mitigation strategies. Experimental validation on a laboratory-scale ICS demonstrates the framework’s effectiveness against various cyberattacks, including Min-Max, Surge, Ramp, and Replay attacks. Results highlight its adaptability, lightweight design, and real-time performance, making the proposed framework a scalable and deployable solution for enhancing ICS cybersecurity and operational resilience.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109278"},"PeriodicalIF":3.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722699","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