Computers & Chemical Engineering最新文献

{"title":"A hybrid ML-MILP framework for the optimal integration of photovoltaic and battery systems in manufacturing industries","authors":"Georgios P. Georgiadis ,&nbsp;Christos N. Dimitriadis ,&nbsp;Nikolaos Passalis ,&nbsp;Michael C. Georgiadis","doi":"10.1016/j.compchemeng.2025.109356","DOIUrl":"10.1016/j.compchemeng.2025.109356","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources, coupled with volatile electricity prices, poses significant challenges on energy-intensive industries seeking to reduce costs and improve energy efficiency. This work presents a novel hybrid framework combining machine learning (ML) predictive algorithms with a mixed-integer linear programming (MILP) model to optimize energy management in manufacturing industries utilizing photovoltaic (PV) systems and battery energy storage systems (BESS). The proposed framework accurately forecasts electricity prices, PV generation, and industrial energy demand, enabling both operational optimization and strategic investment planning. The MILP model ensures efficient energy resource utilization, by minimizing electricity costs and maximizing financial gains through optimal market participation. The framework was validated through a real-life case study of a Greek manufacturing facility, comparing different energy options, including scenarios with and without BESS. Results revealed that a properly sized BESS can significantly facilitate cost savings of up to 352 RMU<span><span>¹</span></span>/day via price arbitrage, especially during peak pricing periods. Further analysis indicated that increasing BESS capacity could yield even higher financial benefits thus enhancing industry profitability and competitiveness. Sensitivity analysis under varying electricity price scenarios confirmed the robustness and adaptability of the proposed framework to dynamic market conditions. Financial analysis highlighted that, with appropriate subsidies, the payback period for BESS investments could be considerably shortened from 7 years to 4 years, improving feasibility and attractiveness.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109356"},"PeriodicalIF":3.9,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887477","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":"Process monitoring and dynamic fusion of complex industrial systems: A reconstruction-based Bayesian framework","authors":"Husnain Ali ,&nbsp;Rizwan Safdar ,&nbsp;Jinfeng Liu ,&nbsp;Muhammad Bilal Asif ,&nbsp;Xiangrui Zhang ,&nbsp;Muhammad Hammad Rasool ,&nbsp;Yuan Yao ,&nbsp;Le Yao ,&nbsp;Jian Ding ,&nbsp;Furong Gao","doi":"10.1016/j.compchemeng.2025.109352","DOIUrl":"10.1016/j.compchemeng.2025.109352","url":null,"abstract":"<div><div>In the last decade, the automation complexity and sophistication of multiple sensors in modern industrial systems have grown significantly with the fast transformation from Industry 4.0 to 5.0. This transformation of Industry 4.0 to 5.0 has still not been carefully investigated for dynamic monitoring and fusion information. Traditional monitoring techniques are not advanced to address these significant challenges when evaluating the intricate information collected from sophisticated sensors and computing systems. This paper presents an innovative, intelligent dynamic fusion framework that utilizes machine learning (<em>ML</em>) and deep learning (<em>DL</em>) to combine dynamic Bayesian global-local preserving projection (<em>DBGLPP</em>), mutual information entropy (<em>MIE</em>), stacked autoencoders (<em>SAE</em>), kernel density estimation (<em>KDE</em>), and reconstruction-based contributions (<em>RBC</em>). The novel dynamic fusion framework addresses the issues of real-time dynamic monitoring in physio-chemical systems. The approach seeks to investigate, categorize, isolate, identify, and diagnose anomalies and faults. The framework's feasibility and functionality are evaluated using recently established models such as <em>Wavelet-PCA, CWT-3D-CNN, DALSTM-AE</em>, and the newly proposed dynamic, intelligent fusion monitoring framework (<em>DBGLPP-SAE</em>) as model validation baselines. The proposed innovative methodology has been tested by assessment of the ethanol-water distillation column (<em>DC</em>) and the Tennessee Eastman Process (<em>TEP</em>) as baseline benchmarks. The results and findings showed that the novel dynamic intelligent fusion framework can address the issues and challenges of real-world complex industrial systems. It can robustly detect abnormalities, classify and isolate faults, identify root channels, and diagnose problematic variables.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109352"},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880389","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 two-stage stochastic MINLP model to design and operate a multi-energy microgrid by addressing carbon emission regulatory policies uncertainty","authors":"Handan Akülker ,&nbsp;Burak Alakent ,&nbsp;Erdal Aydin","doi":"10.1016/j.compchemeng.2025.109351","DOIUrl":"10.1016/j.compchemeng.2025.109351","url":null,"abstract":"<div><div>This study suggests a two-stage mixed-integer nonlinear programming model considering uncertainty related to implementation of carbon dioxide emission regulatory policies, which are carbon trading and emission taxing and can change over the years, for the purpose of optimal equipment selection from candidate equipment to design, size and operate a multi-energy microgrid. The uncertain sources are air temperature, wind speed, solar radiation, carbon dioxide trading price or tax, and natural gas price. Candidate equipment are wind turbines, PV arrays, a biomass-fired generator, biomass combined cycles, combined heat and power generators, conventional generators, an electricity storage unit, integrated gasification combined cycles, a heat pump, and a power-to-synthetic natural gas (P2G) system. Three case studies are investigated. In the first case, the model selects the optimal equipment for meeting the electricity and heat demands only. In the second case, the optimal equipment selections are determined to couple with the P2G system to meet the electricity, heat, and natural gas demands. In the third case, the model selects the optimal equipment to run with sustainable energy generators: wind turbines and solar panels. The optimal selections are compared between deterministic and stochastic forms of the optimization models.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109351"},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890404","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 Goal Programming Model to Design a Sustainable Microalgae Biofuel Supply Chain with Emerging on Technology Assessment","authors":"Simin Torki Beni ,&nbsp;Atefeh Amindoust ,&nbsp;Ali Saghafinia ,&nbsp;Mehrdad Nikbakht","doi":"10.1016/j.compchemeng.2025.109347","DOIUrl":"10.1016/j.compchemeng.2025.109347","url":null,"abstract":"<div><div>Biofuels, derived from biomass-based resources, have gained significant attention as a sustainable alternative to fossil fuels due to their potential to reduce environmental pollution and address the limited supply of conventional energy sources. Among various biofuel sources, microalgae have emerged as a promising candidate owing to their cost-effectiveness and versatile applications. This study aims to develop a sustainable supply chain model for microalgae biofuel production by integrating novel technologies and goal programming. To achieve this, a robust mathematical model was designed, incorporating four key objectives: (1) maximizing profit, (2) minimizing environmental pollution and emissions, (3) maximizing supplier service levels, and (4) minimizing the transportation distance from suppliers to markets. The Ordinary Priority Approach (OPA) method was employed to weigh technological criteria and determine the coefficients of the mathematical model. The proposed model was solved using two approaches: the METRIC-LP method and the Bat Algorithm. A comparative analysis of the results revealed minimal differences between the two methods, with Sample 9 (large-scale) achieving the highest optimal value. This research provides a comprehensive framework for optimizing microalgae biofuel production by balancing economic, environmental, and logistical considerations, and it offers valuable insights for stakeholders in the renewable energy sector.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109347"},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893668","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":"Causality in Process Systems Engineering: Fundamentals, Applications, and Emerging Trends","authors":"Rodrigo Paredes,&nbsp;Marco S. Reis","doi":"10.1016/j.compchemeng.2025.109345","DOIUrl":"10.1016/j.compchemeng.2025.109345","url":null,"abstract":"<div><div>The increasing availability of high-dimensional data from chemical and industrial processes has enabled the widespread adoption of machine learning and deep learning methods. However, their black-box nature raises critical concerns about reliability, ethics, and security in safety-critical industrial applications, highlighting the need for Explainable Artificial Intelligence (XAI) solutions. In this context, Causality analysis emerges as a foundational approach within XAI, moving beyond correlations to uncover genuine cause-and-effect relationships that are essential for reliable decision-making.</div><div>Despite its potential, the adoption of causal reasoning in Process Systems Engineering (PSE) is still incipient. Therefore, in this work, we establish the crucial role of formal causal analysis as both a theoretical framework and a practical toolkit for addressing core challenges in PSE. We systematically present the fundamental concepts and methods of causal analysis, including <em>do</em>-calculus, causal discovery, and causal inference, providing the necessary fundamentals for PSE researchers and practitioners entering this field. Furthermore, we emphasize the integration of these causal data-driven techniques with domain knowledge, such as process diagrams, hazard studies, and first principles, to address inherent industrial complexities, including nonlinearities, multi-mode operations, feedback control loops, and dynamic behavior. The practical value of causality is illustrated in several application fields, and recent emerging trends are also covered.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109345"},"PeriodicalIF":3.9,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860568","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 novel circle-packing NLP model for offshore wind farm layout and cable optimization","authors":"Angel Francisco Negrete-Romero ,&nbsp;Efraín Quiroz Pérez ,&nbsp;Dulce Celeste López-Díaz ,&nbsp;Julio A. de Lira-Flores ,&nbsp;José María Ponce-Ortega","doi":"10.1016/j.compchemeng.2025.109346","DOIUrl":"10.1016/j.compchemeng.2025.109346","url":null,"abstract":"<div><div>The proper siting of wind turbines and cable routing in offshore wind energy systems can be used to prevent wake effects and electrical losses. This study adopts a continuous-domain optimization model based on nonlinear programming for offshore wind farms' simultaneous layout and electrical placement. In particular, it uses a circle-packing formulation to optimize the placement of turbines within a flexible, unconstrained spatial domain while incorporating a radial cabling strategy to evaluate and minimize power losses. In particular, the model positions turbines in a flexible, unconstrained spatial domain with circle packings and implements a radial cabling strategy to evaluate and minimize power losses. The method considers a Gaussian-based wake model and losses due to dips and resistivity of the electrical cables. The resulting model was solved using a global NLP solver (GAMS/BARON) for several scenarios. The result shows a 43 % reduction in the occupied area and a 0.42 % decrease in annual energy production. It also has more spatial compactness, shorter cable length, and more stable performance than the traditional grid-based and heuristic models. Its scalable and flexible formulation makes it suitable for planning offshore wind farms in earlier stages.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109346"},"PeriodicalIF":3.9,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887476","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 mathematical programming model for the optimal utilization of deep saline aquifers for CO2 storage","authors":"Diego J. Trucco ,&nbsp;Demian J. Presser ,&nbsp;Diego C. Cafaro ,&nbsp;Ignacio E. Grossmann ,&nbsp;Saurabh Shenvi Usgaonkar ,&nbsp;Qi Zhang ,&nbsp;Pratik Misra ,&nbsp;Heather Binagia ,&nbsp;Wayne Rowe ,&nbsp;Sanjay Mehta","doi":"10.1016/j.compchemeng.2025.109343","DOIUrl":"10.1016/j.compchemeng.2025.109343","url":null,"abstract":"<div><div>This work presents a novel nonlinear programming (NLP) formulation aimed at maximizing the overall amount of CO₂ stored into deep saline aquifers in the long term. The goal is to optimally determine CO₂ injection rates into vertical wells while properly managing bottom-hole pressures over time. The reservoir may comprise several layers with heterogeneous physical properties. The injection plan should meet the subsurface engineering policies for safe operations along with existing technical constraints. The major challenge is to track the CO₂ migration across the reservoir to ensure containment during the injection periods and also in the long term. The NLP formulation is based on a discrete space and time representation of the reservoir, comprising pressure propagation and mass balance equations between every pair of adjacent blocks in the grid. Results for several illustrative case studies in two dimensions show the potential of the model to find optimal solutions in few seconds. Injection plans suggested by the optimization model are efficient and have been validated by accurate simulation runs. Based on these findings, the model has the potential to be extended to three dimensions and adapted to real-world cases.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109343"},"PeriodicalIF":3.9,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887478","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 general optimization framework for designing chemical & energy systems subject to multi-scale temporal variability","authors":"Nicholas N. Kalamaris ,&nbsp;Christos T. Maravelias","doi":"10.1016/j.compchemeng.2025.109315","DOIUrl":"10.1016/j.compchemeng.2025.109315","url":null,"abstract":"<div><div>We present a general optimization framework for designing chemical and energy systems that experience variability at multiple timescales. Motivated by an environmental need to decarbonize manufacturing, we seek to understand the viability of chemical and energy systems subject to temporal variability in physical and economic conditions. Our framework is based on a system specific superstructure and set of unit models, and it includes a representative time structure and the corresponding mathematical program for operation-informed design. The framework can be applied to determine the basic configuration and design of unit operations, associated time profiles of material and energy flows for flexible operation, and relevant thermodynamic variables (like temperature and pressure). It also allows us to identify how optimal design evolves over time. Understanding these behaviors is key to designing systems that successfully operate under variability. We apply our framework to study green ammonia synthesis, and identify optimal designs with distinct operational behavior at hourly, seasonal, and (multi-)yearly timescales. This includes charge/discharge decisions for energy storage, the behavior of mass storage tanks, and the seasonal purchase/sale of energy. We also observe transition points in design when considering different power grids.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109315"},"PeriodicalIF":3.9,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841117","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":"Coupling CO2 electrolysis and downstream processing via heat pump-based waste heat recovery","authors":"Riccardo Dal Mas,&nbsp;Andrea Carta,&nbsp;Ana Somoza-Tornos,&nbsp;Anton A. Kiss","doi":"10.1016/j.compchemeng.2025.109330","DOIUrl":"10.1016/j.compchemeng.2025.109330","url":null,"abstract":"<div><div>The electrification of chemical processes and CO₂ utilization are key approaches to improving efficiency and reducing CO₂ emissions in the process industry. The development of electrolyzers has gathered momentum, enabling the potential introduction of renewable electrons into the manufacture of CO₂-based chemicals. While the performance of electrolyzers is subject to improvements driven by the experimental community, the generation of waste heat is unavoidable due to electrical resistances and process inefficiencies within the electrochemical cells. Nonetheless, reusing this waste heat has yet to be investigated for CO₂ electrolyzers. This novel work shows the potential for upgrading the electrolyzer waste heat by means of a heat pump, enabling its utilization in the separation processes downstream of the carbon dioxide electrolyzer. The product chosen is formic acid (60 kt/y), and for our system, the waste heat represents approximately 60 % of the power input to the electrochemical cells, and it can be upgraded from 50 °C to 120 °C to drive the azeotropic distillation of formic acid and water. This integration results in the electrification of 76 % of the separation energy duty, yielding a decrease in CO₂ emissions of 29–84 % compared to the conventional production, depending on the source of electricity. The results demonstrate that the use of traditional heating media in thermal separation processes can be offset and substituted with (renewable) electrical energy, allowing for an increased overall system efficiency. This approach can be readily extended to different productions based on carbon dioxide electroreduction, for example for methanol and ethanol manufacture. This eco-efficient process design leads to a deeper penetration of renewable energy into chemical manufacturing, as both reaction and separation are driven by electricity.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109330"},"PeriodicalIF":3.9,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893636","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":"Feasibility in Real-Time Optimization using Lipschitz bounds: Robust optimization Vs. Adaptive filtering","authors":"A.G. Marchetti","doi":"10.1016/j.compchemeng.2025.109316","DOIUrl":"10.1016/j.compchemeng.2025.109316","url":null,"abstract":"<div><div>This paper investigates two strategies for ensuring feasible-side convergence in Real-Time Optimization (RTO) using Lipschitz-based constraint upper bounds. Strategy 1 embeds the bounds directly into the RTO problem, while Strategy 2 uses them to adaptively tune a filter gain. We compare their performance across three types of bounds: on the plant constraints, constraint modeling error, and constraint gradient error. The results show that Strategy 1 consistently achieves superior convergence, especially under model mismatch or when initialized near active constraints. In contrast, Strategy 2 often leads to premature convergence and suboptimality. These findings support the direct enforcement of Lipschitz bounds as a more robust and effective approach for RTO design.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"203 ","pages":"Article 109316"},"PeriodicalIF":3.9,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860569","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}