Computers & Chemical Engineering最新文献

{"title":"A deep learning framework for cyberattack detection and classification in Industrial Control Systems","authors":"Malhar Barbhaya ,&nbsp;Purushottama Rao Dasari ,&nbsp;Seshu Kumar Damarla ,&nbsp;Rajagopalan Srinivasan ,&nbsp;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}

{"title":"A review of indicators-based computational methodologies to improve the sustainability of wastewater treatment plants","authors":"Rodrigo Salles ,&nbsp;Jérôme Mendes ,&nbsp;António J. Baptista ,&nbsp;Pedro Moura","doi":"10.1016/j.compchemeng.2025.109279","DOIUrl":"10.1016/j.compchemeng.2025.109279","url":null,"abstract":"<div><div>Water scarcity is currently a concerning problem and is likely to worsen in the future. To address this issue, it is essential that water used in human activities is treated before being reused or returned to nature. Wastewater is processed in wastewater treatment plants (WWTPs), which are complex structures that consume a considerable amount of resources and need to operate optimally. Many authors have proposed computational methodologies to optimize WWTPs, and each work has different approaches and characteristics, but most have in common the lack of concern with maximizing sustainability, in its broadest definition. Furthermore, even when sustainability is considered, it is typically addressed in an indirect or superficial manner, rather than being treated as a central objective. This paper provides a critical literature review of computational methodologies that, in some way, focus on improving the sustainability of WWTPs. Considering the target of the paper, this review aims to answer the following main questions: (1) What are the general objectives of the proposed works? (2) In which locations/phases of the treatment process are the proposed techniques applied? (3) What are the main methodologies and performance metrics used in the proposed techniques? The review identifies a strong focus on optimizing aeration in biological reactors, limited holistic and real-time optimization across WWTP stages, and sparse integration of sustainability metrics, especially for environmental and social impacts. Future research should prioritize the development of real-time, multi-objective optimization frameworks that encompass all WWTP stages and fully integrate economic, environmental, and social sustainability dimensions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109279"},"PeriodicalIF":3.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696929","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":"Design of a fluidized temperature swing adsorption process for biomass-derived flue gas carbon capture","authors":"Eduardo dos Santos Funcia ,&nbsp;Yuri Souza Beleli ,&nbsp;Enrique Vilarrasa-Garcia ,&nbsp;Marcelo Martins Seckler ,&nbsp;Galo Antonio Carrillo Le Roux","doi":"10.1016/j.compchemeng.2025.109280","DOIUrl":"10.1016/j.compchemeng.2025.109280","url":null,"abstract":"<div><div>The Paris Agreement has established ambitious global climate targets, necessitating the deployment of advanced carbon capture and storage (CCS) technologies to mitigate greenhouse gas emissions. Among these, Bioenergy with Carbon Capture and Storage (BECCS) stands out as a viable solution for achieving negative emissions. This study presents an optimization-based design framework for a simulated fluidized temperature swing adsorption (TSA) system tailored for large-scale CO₂ capture from biomass-derived flue gases. The process model incorporates thermodynamic, kinetic, and hydrodynamic parameters, optimizing key operating variables such as temperature, pressure, and flow rates to minimize operational costs. Simulations indicate that a four-stage adsorption system with a single desorption stage can achieve 95 % CO₂ purity and 97 % recovery at an estimated cost of 83.7 USD per ton of CO₂ captured. The study highlights the role of heat exchanger networks and process-wide optimization in improving energy efficiency and reducing costs. Furthermore, scalability analysis reveals that increasing the flue gas flow rate leads to significant economies of scale, reinforcing the viability of fluidized TSA for industrial applications. These findings contribute to the advancement of BECCS technologies, supporting global decarbonization efforts and the transition to a low-carbon economy.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109280"},"PeriodicalIF":3.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711292","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":"Improved Bayesian parameters estimation with model-based design of experiments for over-parameterized systems","authors":"Xinyu Cao ,&nbsp;Xi Chen ,&nbsp;Lorenz T. Biegler","doi":"10.1016/j.compchemeng.2025.109296","DOIUrl":"10.1016/j.compchemeng.2025.109296","url":null,"abstract":"<div><div>It is efficient to integrate Bayesian parameter estimation (BPE) with model-based design of experiments (MBDoE) to estimate process parameters. But for over-parameterized models, significant challenges are involved such as the singularity of the Fisher Information Matrix (FIM) and the non-convergence of Markov Chain Monte Carlo (MCMC) methods. To address these challenges, this study introduces a novel approach that integrates BPE and model transformation through singular value decomposition (SVD). Under the proposed structure, the noise distribution not only converges to a stable state but also consistently recovers the true noise variance. Good performance in achieving more accurate model and improved experimental efficiency is validated through two case studies on dynamic batch reactor systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109296"},"PeriodicalIF":3.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696928","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":"Novel global optimization approach for the simultaneous design of heat exchangers in heat exchanger networks constrained by pressure drop","authors":"Hassan Hajabdollahi ,&nbsp;André L.H. Costa ,&nbsp;Miguel J. Bagajewicz","doi":"10.1016/j.compchemeng.2025.109299","DOIUrl":"10.1016/j.compchemeng.2025.109299","url":null,"abstract":"<div><div>We address the simultaneous globally optimal basic design of shell-and-tube heat exchangers interconnected in a HEN, where the total pressure drop of the streams that pass through several exchangers is limited to a maximum. We explore the optimal distribution of the available pressure drop in each stream among different heat exchangers, instead of the traditional procedure based on individual values of available pressure drop. The approach is useful in the context of HEN synthesis based on network structure enumeration when the design of the equipment is embedded in the algorithm. The objective is to obtain the optimal geometry of each exchanger, such that the total area or investment cost is minimized subject to the collective pressure drop constraints. The design variables for the shell-and-tube heat exchangers include all typical geometric dimensions. The novel global optimization approach builds an integer linear optimization model (ILOM), which is solved using an integer linear programming method (ILP). The ILOM involves only feasible heat exchanger candidates, obtained using Complete Set Trimming. The procedure was applied to two test cases involving 8 and 23 heat exchangers, respectively, each exchanger having 12,852,000 initial candidate geometries. The computational times for this task are below one hour for the two studied cases when implemented in MATLAB. Results for optimizing using the total annualized cost (CAPEX+OPEX) as an objective function and without pressure drop constraints are also presented for comparison.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109299"},"PeriodicalIF":3.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713151","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":"Stochastic optimization for strategic planning of efficient and sustainable hydrogen supply chain networks under demand uncertainty","authors":"Mohamed Amjath ,&nbsp;Fadwa Eljack ,&nbsp;Mohamed Haouari","doi":"10.1016/j.compchemeng.2025.109298","DOIUrl":"10.1016/j.compchemeng.2025.109298","url":null,"abstract":"<div><div>Hydrogen has emerged as a crucial energy carrier, playing a vital role in the global transition towards a low-carbon economy. Despite its growing strategic importance, accurately forecasting hydrogen demand remains challenging due to the influence of diverse and interdependent factors. An effective and sustainable infrastructure planning requires modeling frameworks that explicitly incorporate demand uncertainty inherent in hydrogen market projections. This study proposes a stochastic mixed-integer linear programming (SMILP) framework for optimizing the strategic development of hydrogen supply chain (HSC) infrastructure, accounting for the demand uncertainty. Demand uncertainty is modeled using discrete probabilistic realizations across multiple time periods. A novel approach is presented in incorporating and quantifying the expected penalty costs for unfulfilled demand through a linear formulation. The proposed model aims to minimize the total system cost, encompassing both economic and emission costs across all stages of the HSC, including production, conditioning and storage, transportation, and re-conditioning. This study uses Qatar as an illustrative case to evaluate three distinct investment strategies to test the model's applicability in real-world scenarios. Across the different strategies, the end-to-end levelized cost of hydrogen (LCOH) for the entire supply chain ranges from $3.55/kg to $3.68/kg, while the production LCOH ranges from $1.18/kg to $1.23/kg. The findings highlight the importance of adopting a balanced approach to infrastructure planning, particularly under volatile and fluctuating market conditions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109298"},"PeriodicalIF":3.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711293","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":"Simultaneous safety process design and control of a polymerization reactor using Safety Weighted Hazard Index","authors":"Fernando Henrique Marques,&nbsp;Luz Adriana Alvarez","doi":"10.1016/j.compchemeng.2025.109277","DOIUrl":"10.1016/j.compchemeng.2025.109277","url":null,"abstract":"<div><div>We present the development of simultaneous safety process design and control (SSPDC) for a continuous polystyrene reactor. The proposed approach is capable to quantify and incorporate inherent safety techniques during the process and control design stage, using Safety Weighted Hazard Index (SWeHI). Besides, the control design is developed from a model predictive control (MPC) containing safety constraint based on the process states. This method is divided in three optimization problems: an economic target for process plant design; a dynamic model for MPC design, that aims to obtain the optimal controller tuning vector, so that the advanced control structure is based on real time optimization (RTO), a target calculation (TC), and an infinite horizon model predictive control (IHMPC) with zone control. At the end, both optimization models are integrated to determine, simultaneously, the optimal process and control design setting. The results suggest that this approach has advantage when compared with only process and control design, since it can improve safety design features for a chemical process. Furthermore, the framework allows obtaining different scenarios for an economically and safely process plant design.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109277"},"PeriodicalIF":3.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653293","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":"Bridging net-zero, nature-positive, and people-positive goals: A case study in the polyester industry","authors":"Aniket Mali ,&nbsp;Xinyu Zhen ,&nbsp;Bhavik R. Bakshi","doi":"10.1016/j.compchemeng.2025.109276","DOIUrl":"10.1016/j.compchemeng.2025.109276","url":null,"abstract":"<div><div>Sustainability has become a central focus in global discourse, demanding solutions that integrate three critical goals: net-zero emissions, nature-positive outcomes, and people-positive development. Progress in emissions and resource reduction exists, but integrating net-zero, nature-, and people-positive goals remains limited. This study applies the open-source Chemicals and Materials Industry (CMI) model to assess environmental, economic, and social impacts of polyester fiber production, spanning current and emerging technologies. The model was validated against literature, and sensitivity analysis highlights circularity as a key lever, capable of reducing emissions by up to 40%, though often requiring significant investment and risking widened social inequalities.</div><div>Our findings reveal critical trade-offs. While biobased materials and renewable energy can enable 100% emission reduction, they may simultaneously drive resource extraction, biodiversity loss, and land use change. Global-scale assessments further show that even if country-level trends appear sustainable, the cumulative impact may exceed safe and just environmental limits. These insights underscore the importance of evaluating decarbonization strategies not only by emissions, but also by their broader ecological and social implications.</div><div>To align climate goals with ecological restoration, we quantify nature-based solutions. For instance, forests sequester <span><math><mo>∼</mo></math></span>0.5 kg CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>/year, allowing decision-makers to calculate reforestation needs for emissions offsets. This approach makes nature-positive pathways measurable and actionable, transforming ecosystem services into integral components of climate mitigation.</div><div>Simultaneously, improving labor conditions and access to sustainable inputs supports people-positive goals but can raise production costs. Despite trade-offs, combining renewables, biobased feedstocks, and circular practices creates win–win–win pathways—cutting emissions, restoring ecosystems, and promoting equity.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109276"},"PeriodicalIF":3.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670350","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":"Integrated decision-making approach for the simultaneous design of food packaging and waste management technologies to achieve a Circular Economy","authors":"Paola Munoz-Briones ,&nbsp;Aurora del Carmen Munguía-López ,&nbsp;Kevin Sánchez Rivera ,&nbsp;Styliani Avraamidou","doi":"10.1016/j.compchemeng.2025.109269","DOIUrl":"10.1016/j.compchemeng.2025.109269","url":null,"abstract":"<div><div>Food packaging plays a fundamental role in food supply chains; however, the surge in plastic waste, coupled with limited recycling efforts and improper disposal, emphasizes the need for sustainable food packaging solutions. Circular Economy can help mitigate environmental impacts associated with packaging by promoting recycling and the utilization of renewable resources. However, literature lacks integrated approaches to analyze both food packaging and waste management strategies alongside their economic, environmental, and circularity impacts. This study proposes a systems engineering framework, that employs a multi-objective mixed-integer linear optimization model to identify optimal combinations of packaging and waste management technologies based on economic and environmental criteria. Using a superstructure approach, and combining techno-economic analysis (TEA), life cycle assessment (LCA), and circularity assessment, the proposed framework provides insights for guiding food packaging selection while considering waste management options. A case study focusing on coffee packaging illustrates the framework’s applicability. Results show that the most economically profitable pathway involves the use of multilayer bags paired with an advanced recycling technology that produces high-quality plastic resins. Reusable glass containers emerge as the least greenhouse gas (GHG) emitting option when considering short-distances between glass collection and washing facilities. Moreover, the study demonstrates that the pathways with the lowest GHG emissions do not necessarily exhibit the highest circularity score, with the model identifying trade-off solutions between GHG emissions and circularity when transportation distances are longer. Trade-offs between costs, GHG emissions, and circularity are explored and visualized in Pareto fronts, aiding informed decision-making.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109269"},"PeriodicalIF":3.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679679","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":"Dynamic surrogate-assisted particle swarm optimization for chemical kinetic mechanisms","authors":"Hao Hu ,&nbsp;Mengjie Li ,&nbsp;Huangwei Zhang","doi":"10.1016/j.compchemeng.2025.109294","DOIUrl":"10.1016/j.compchemeng.2025.109294","url":null,"abstract":"<div><div>Optimizing chemical kinetic mechanisms is challenging due to the large number of uncertain rate parameters and the high computational cost of kinetic simulations. While existing methods often rely on static surrogate models to approximate the kinetic response across the entire uncertainty space, they typically suffer from limited robustness and poor generalization in complex regions. This study proposes a dynamic surrogate-assisted particle swarm optimization (PSO) framework that adaptively refines the surrogate model during optimization to improve both accuracy and efficiency. The framework operates iteratively through three stages: active sampling, surrogate training, and swarm update. A radial basis function neural network (RBFNN) is employed as a surrogate model to predict kinetic responses. Based on the predictions and associated uncertainty, an active sampling strategy selects informative samples for kinetic simulations to obtain real responses. These samples are then used to incrementally retrain the surrogate, enhancing prediction accuracy in regions of interest. Finally, guided by the retrained surrogate, a truncated learning PSO algorithm updates the samples to efficiently find the optimal rate parameters. The proposed method is evaluated on an ammonia combustion mechanism and demonstrates superior optimization accuracy with orders-of-magnitude acceleration compared to state-of-the-art baselines. It also supports inverse uncertainty quantification for posterior analysis and uncertainty reduction. Furthermore, its scalability is validated by including more rate parameters for optimization, leading to a 550-dimensional space. These results highlight the effectiveness of the dynamic optimization strategy and its potential for advancing data-driven mechanism development in complex reacting systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"202 ","pages":"Article 109294"},"PeriodicalIF":3.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653295","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}