Computers & Chemical Engineering最新文献

{"title":"A practical scenario generation method for electricity prices on day-ahead and intraday spot markets","authors":"Chrysanthi Papadimitriou ,&nbsp;Jan C. Schulze ,&nbsp;Alexander Mitsos","doi":"10.1016/j.compchemeng.2025.109118","DOIUrl":"10.1016/j.compchemeng.2025.109118","url":null,"abstract":"<div><div>The increasing interest in demand-side management (DSM) as part of the energy cost optimization calls for effective methods to determine representative electricity prices for energy optimization and scheduling investigations. We propose a practical method to construct price profiles of day-ahead (DA) and intraday (ID) electricity spot markets. We construct single-day and single-week price profiles based on historical market time series to provide ready-to-use price data sets. Our method accounts for dominant mechanisms in price variation to preserve critical statistical features (e.g., mean and standard deviation) and transient patterns in the constructed profiles. Unlike common scenario generation approaches, the method is deterministic, with few degrees of freedom and minimal application effort. Our method ensures consistency between ID and DA price profiles when both are considered and introduces profile scaling to enable multiple scenario generation. Finally, we compare the constructed profiles to clustering techniques in a DSM case study, noting similar cost results.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109118"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864431","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":"Implementing NLP in industrial process modeling: Addressing categorical variables","authors":"Eleni D. Koronaki ,&nbsp;Geremy Loachamín-Suntaxi ,&nbsp;Paris Papavasileiou ,&nbsp;Dimitrios G. Giovanis ,&nbsp;Martin Kathrein ,&nbsp;Christoph Czettl ,&nbsp;Andreas G. Boudouvis ,&nbsp;Stéphane P.A. Bordas","doi":"10.1016/j.compchemeng.2025.109146","DOIUrl":"10.1016/j.compchemeng.2025.109146","url":null,"abstract":"<div><div>Important variables of processes are often categorical, i.e. names or labels representing, e.g. categories of inputs, or types of reactors or a sequence of steps. In this work, we use Natural Language Processing Models to derive embeddings of such inputs that represent their actual meaning, or reflect the “distances” between categories, i.e. how similar or dissimilar they are. This is a marked difference from the current standard practice of using binary, or one-hot encoding to replace categorical variables with sequences of ones and zeros. Combined with dimensionality reduction techniques, either linear such as Principal Component Analysis, or nonlinear such as Uniform Manifold Approximation and Projection, the proposed approach leads to a <em>meaningful</em>, low-dimensional feature space. The significance of obtaining meaningful embeddings is illustrated in the context of an industrial coating process for cutting tools that includes both numerical and categorical inputs. In this industrial process, subject matter expertise suggests that the categorical inputs are critical for determining the final outcome but this cannot be taken into account with the current state-of-the-art. The proposed approach enables feature importance which is a marked improvement compared to the current state-of-the-art in the encoding of categorical variables. The proposed approach is not limited to the case-study presented here and is suitable for applications with similar mix of categorical and numerical critical inputs.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109146"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868230","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":"Predicting handsheet properties and enhancing refiner control using fiber analyzer data and latent variable modeling","authors":"Stefan B. Lindström ,&nbsp;Rita Ferritsius ,&nbsp;Johan E. Carlson ,&nbsp;Johan Persson ,&nbsp;Fritjof Nilsson","doi":"10.1016/j.compchemeng.2025.109143","DOIUrl":"10.1016/j.compchemeng.2025.109143","url":null,"abstract":"<div><div>This study focuses on the development of a compact model with improved interpretability compared to similar approaches, relating thermomechanical pulp (TMP) properties, quantified using a fiber analyzer, to Canadian standard freeness and handsheet properties. The data used in this study are obtained from TMP produced by a conical disc refiner. Utilizing the LASSO-regularized Latent Variable Regression (LASSO-LVR) model, we identified three key latent variables – representing shives content, fibrillation, and slender fines content – that accurately predict eight distinct handsheet properties. In a subsequent analysis, we investigated the linkage between refiner settings and Specific Refining Energy (SRE) to these key analyzer readings and, consequently, to handsheet properties. The inclusion of SRE as an internal state variable in the model significantly enhanced predictive accuracy, providing a foundation for more precise and energy-efficient control strategies in refining processes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109143"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858928","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":"Screening green solvents for multilayer plastic film recycling processes","authors":"Ugochukwu M. Ikegwu ,&nbsp;Aurora del Carmen Munguía-López ,&nbsp;Victor M. Zavala ,&nbsp;Reid C. Van Lehn","doi":"10.1016/j.compchemeng.2025.109129","DOIUrl":"10.1016/j.compchemeng.2025.109129","url":null,"abstract":"<div><div>Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAP^TM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. This work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109129"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874953","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":"Multiscale high-throughput screening of ionic liquid solvents for mixed-refrigerant separation","authors":"Ashfaq Iftakher ,&nbsp;Mohammed Sadaf Monjur ,&nbsp;Ty Leonard ,&nbsp;Rafiqul Gani ,&nbsp;M.M. Faruque Hasan","doi":"10.1016/j.compchemeng.2025.109138","DOIUrl":"10.1016/j.compchemeng.2025.109138","url":null,"abstract":"<div><div>Commonly used mixed-refrigerants are azeotropic mixtures of hydrofluorocarbons (HFCs) with high global warming potential. There is a need for reclamation and recovery of these HFCs. Solvent-based extractive distillation is a promising separation technique for recycling of these refrigerant components. Ionic liquids are suitable solvents for this application due to their negligible vapor pressures, tunable properties, and near-zero waste in closed-loop operations. However, the numerous potential combinations of cation-anion pairs make the selection of the optimal ionic liquid challenging. Moreover, the choice of ionic liquid critically affects energy efficiency and separation performance. To address this challenge, we present a hierarchical, multiscale computational workflow for computer-aided molecular and process design (CAMPD) that combines aspects of molecular simulation, machine learning, process performance measures, and equation-oriented process optimization for the solvent-based separation of azeotropic refrigerant mixtures. We employ a decomposition-based solution approach for CAMPD, where we first perform computer-aided molecular design (CAMD) to identify promising ionic liquid candidates through high-throughput screening, considering 16,352 known and generated ionic liquids. Next, we perform a focused CAMPD to identify the solvents that give the best process performance. We highlight the application of our method for the separation of refrigerants R-32 from R-125, which belong to the binary azeotropic refrigerant mixture commonly known and used as R-410A. Our method identified 43 ionic liquids (24 known and 19 generated) that matched all solvent and separation process specifications. Among these, five ionic liquids are found to be more sustainable and superior to others.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109138"},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868229","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":"Multi-loop PID tuning strategy based on non-iterative linear matrix inequalities","authors":"Diego José Trica","doi":"10.1016/j.compchemeng.2025.109137","DOIUrl":"10.1016/j.compchemeng.2025.109137","url":null,"abstract":"<div><div>Chemical processing plants usually have a control architecture composed of several single-paired loops. This type of control system is also called a multi-loop or decentralized control system. In this context, tuning PID controllers in a multi-loop system has become more important in recent decades. This is due to the need to ensure that the closed-loop system is stable or to achieve the expected dynamic performance over a wide range of possible operational conditions. To do this, several authors in the control theory field have used methods based on the Lyapunov stability criteria using linear matrix inequalities (LMI) to tune PID controllers in multi-loop systems. These methods solve the static output feedback (SOF) problem for systems represented by state spaces. This tuning problem is originally bilinear, and some authors have suggested iterative approaches that split the optimization into two layers to turn the problem into a convex one. However, these approaches may lead to high computational costs, depending on the initial guess for the decision variables. This work presents a strategy where only the control gain matrices are used as decision variables, and the Lyapunov matrix is expressed as a function of the control gain matrices. This makes quadratic matrix terms arise, which are handled by the congruency property and an <span><math><mi>S</mi></math></span>-procedure along with a slack variable. This strategy results in a non-iterative LMI-based SOF tuning approach. To illustrate the approach, a SOF problem that maximizes the system’s Lyapunov function decay rate with an upper bound on <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> norm was used.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109137"},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864430","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":"Capturing uncertainty in black-box chromatography modelling using conformal prediction and Gaussian processes","authors":"Tien Dung Pham ,&nbsp;Robert Bassett ,&nbsp;Uwe Aickelin","doi":"10.1016/j.compchemeng.2025.109136","DOIUrl":"10.1016/j.compchemeng.2025.109136","url":null,"abstract":"<div><div>We demonstrate that conformal predictors – specifically conformalised quantile regression (CQR) and locally adaptive conformal predictors (LACP) – outperform the commonly used Gaussian Process Regression (GPR) in uncertainty quantification of machine learning surrogate models for chromatography modelling. CQR excelled in black-box scenarios, effectively estimating challenging target variable distributions, while LACP provided extremely informative intervals when kinetic parameters were included. Incorporating kinetic data significantly reduced epistemic uncertainty and increased model accuracy, supporting the hypothesis that adding mechanistic data to black-box models improves prediction uncertainty. This study represents the first application of conformal methods in chromatography modelling, indicating high applicability of this new uncertainty quantification methodology. Our findings offer a promising direction for advancing uncertainty quantification methods in data-driven bioprocess modelling and optimisation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109136"},"PeriodicalIF":3.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868231","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":"Incorporating Physical Constraints inside Neural Networks to Improve their Accuracy and Physical Reliability for Chemical Engineering Unit Operations Modeling","authors":"Jana Mousa,&nbsp;Stéphane Negny,&nbsp;Rachid Ouaret,&nbsp;Alessandro Di Pretoro,&nbsp;Ludovic Montastruc","doi":"10.1016/j.compchemeng.2025.109156","DOIUrl":"10.1016/j.compchemeng.2025.109156","url":null,"abstract":"<div><div>Neural networks are machine learning models structured in interconnected layers of nodes, or neurons, designed to process and learn complex data patterns by adjusting connections based on the input data and desired output. A common challenge with these networks lies in their limited ability to incorporate fundamental physical principles, as they typically prioritize data pattern recognition over adherence to system-specific laws and constraints. This research introduces an advanced modeling framework that integrates physics-informed neural networks with data reconciliation techniques, embedding physical constraints directly into the neural network's learning process. By enforcing consistency with foundational physical laws, this hybrid approach effectively combines data-driven insights with physics-based accuracy, enhancing the model’s reliability for complex engineering applications. The study further assesses the performance of traditional neural networks, physics-informed networks, and data reconciliation methods, focusing on their application in the design and optimization of unit operations, revealing the advantages of this physics-augmented approach in bridging theoretical principles and practical modeling.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109156"},"PeriodicalIF":3.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868232","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":"Optimal investment and bidding strategies for wind power in electricity and green certificates markets","authors":"Maria Kanta,&nbsp;Christos N. Dimitriadis,&nbsp;Evangelos G. Tsimopoulos,&nbsp;Michael C. Georgiadis","doi":"10.1016/j.compchemeng.2025.109139","DOIUrl":"10.1016/j.compchemeng.2025.109139","url":null,"abstract":"<div><div>The transition to Renewable Energy (RE) is essential for addressing the growing energy demand and meeting the global sustainability goals. Following market trends, this work simultaneously investigates two key aspects: the strategic investment and bidding decisions of an RE producer, and the hourly coordination of Green Certificates Market (GCM) and Electricity Market (EM). To address these aspects a bilevel optimization model is developed. The upper-level problem seeks to maximize the strategic investor's profits, while the lower-level problems sequentially clear the EM and GCM. The model links electricity demand with green certificates demand, and the share of RE in the energy mix with the availability of green certificates. Employing Karush-Kuhn-Tucker conditions, binary expansion, and duality theory, makes the model solvable by commercial solvers. Applied to a modified Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and the IEEE 24-bus test system; the model shows that GCM encourages new RE investments. Strategic bidding in EM enhances these investments by driving down EM prices, securing a growing market share for the RE producer. This price reduction is combined with capacity withholding when needed to prevent zero-price scenarios. Moreover, higher Renewable Portfolio Standard (RPS) targets or increased rival offering prices boost GCM and EM profitability, thereby positively impacting investment decisions. Contrarily, lower wind capacity factors negatively impact new investments as they lead to higher EM and GCM prices.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109139"},"PeriodicalIF":3.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828337","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":"Real-time optimal power sharing in multi-stack fuel cells","authors":"Beril Tümer ,&nbsp;Deniz Şanlı Yıldız ,&nbsp;Yaman Arkun","doi":"10.1016/j.compchemeng.2025.109142","DOIUrl":"10.1016/j.compchemeng.2025.109142","url":null,"abstract":"<div><div>This paper presents a real-time optimization strategy for power allocation between two fuel cell stacks, maximizing overall efficiency while minimizing hydrogen consumption. The proposed method accounts for stack degradation, characterized by a time-varying electron transfer coefficient (α), estimated in real-time using RLS-Kalman filtering from voltage measurements. The strategy also considers hydrogen crossover effects, which impact fuel efficiency and utilization. The optimization approach was evaluated against two conventional strategies—equal distribution and daisy chain—demonstrating superior performance across various operating scenarios. A new efficiency-based daisy chain algorithm was introduced and compared with the classical power-based method, further highlighting the benefits of the optimization framework. The real-time formulation enables on-the-fly parameter estimation and model updates, making it adaptable to multiple stacks and various objective functions. This approach provides a robust and scalable solution for fuel cell power management under degradation, aging, and other adverse conditions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109142"},"PeriodicalIF":3.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824469","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}