Computers & Chemical Engineering最新文献

{"title":"An integrated approach to the optimal design of sustainably efficient biorefinery supply chains","authors":"Lucas M. Machin Ferrero ,&nbsp;Richard Cabrera Jiménez ,&nbsp;Jonathan Wheeler ,&nbsp;Carlos Pozo ,&nbsp;Fernando D. Mele","doi":"10.1016/j.compchemeng.2025.109104","DOIUrl":"10.1016/j.compchemeng.2025.109104","url":null,"abstract":"<div><div>The agribusiness sector needs to strategically align its activities with the 2030 Sustainable Development Goals. Biorefineries offer a solution for regions rich in natural resources, allowing them to utilize available biomass and progressively reduce dependence on fossil fuels. To ensure the sustainable design and management of biorefinery supply chains, key decisions need to be carefully assessed, taking into account economic, environmental and social impacts. This study proposes a purpose-driven strategy for the multi-criteria design of sustainable biorefinery supply chains, combining a multi-scenario MILP optimization approach with an efficiency (and super-efficiency) ranking using Data Envelopment Analysis (DEA) to address economic, environmental, and social trade-offs. The capabilities of the approach are demonstrated through a real case study in Argentina, using sugarcane and lemon derivatives as feedstocks for bioproducts such as ethanol, citric acid, methanol and lactic acid. The results show the production of bioethanol from sugarcane juice, together with the production of other bioproducts from sugarcane bagasse, as the optimal and sustainably efficient scenario.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109104"},"PeriodicalIF":3.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685171","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":"Advanced data-driven fault detection in gas-to-liquid plants","authors":"Nour Basha ,&nbsp;Radhia Fezai ,&nbsp;Byanne Malluhi ,&nbsp;Khaled Dhibi ,&nbsp;Gasim Ibrahim ,&nbsp;Hanif A. Choudhury ,&nbsp;Mohamed S. Challiwala ,&nbsp;Hazem Nounou ,&nbsp;Nimir Elbashir ,&nbsp;Mohamed Nounou","doi":"10.1016/j.compchemeng.2025.109098","DOIUrl":"10.1016/j.compchemeng.2025.109098","url":null,"abstract":"<div><div>Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR charts. Linear and nonlinear data-driven models, namely principal component analysis and its kernel extension and neural networks, are combined with different statistical charts towards the detection of multiple fault types in three distinct case studies: synthetic, Tennessee Eastman process, and Gas-to-Liquid process. The results show that the MMGLR charts have a better detection accuracy than conventional charts, and that neural networks are more robust modeling techniques than PCA and KPCA for the sake of fault detection.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109098"},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643747","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":"Anomaly detection for drug product manufacturing considering data limitations and shifts: A case study on industrial freeze-dryers","authors":"Gianluca Lombardini ,&nbsp;Sara Badr ,&nbsp;Christian Schmid ,&nbsp;Stephanie Knüppel ,&nbsp;Hirokazu Sugiyama","doi":"10.1016/j.compchemeng.2025.109106","DOIUrl":"10.1016/j.compchemeng.2025.109106","url":null,"abstract":"<div><div>Ensuring the quality of biopharmaceutical products requires robust manufacturing processes and reliable monitoring systems. In industrial applications with real data, traditional data-driven anomaly detection methods often face challenges due to data scarcity and data shifts. To address these challenges, we propose the application of Statistic Alignment (SA) as a domain adaptation technique within the broader framework of transfer learning. A methodology is presented incorporating SA as an effective precursor for One-Class Support Vector Machine (OCSVM) based anomaly detection. Using industrial data from two parallel freeze-dryers, we investigate two cases: (1) transferring a model within the same machine to handle data shifts caused by maintenance, and (2) transferring a model between machines to assess cross-system transferability. We evaluate three SA methods—Mean Alignment, Standard Alignment, and Correlation Alignment—while also exploring data requirements for effective alignment. Moreover, we propose a heuristic-based hyperparameter tuning method for OCSVM to further enhance model performance. Our results demonstrate that SA allows model transfer between domains with F1 scores around 0.9, offering a promising solution for enhancing model robustness in dynamic biopharmaceutical production environments.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109106"},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of strategies to embed physics-informed neural networks in nonlinear model predictive control formulations solved via direct transcription","authors":"Carlos Andrés Elorza Casas,&nbsp;Luis A. Ricardez-Sandoval,&nbsp;Joshua L. Pulsipher","doi":"10.1016/j.compchemeng.2025.109105","DOIUrl":"10.1016/j.compchemeng.2025.109105","url":null,"abstract":"<div><div>This study aims to benchmark candidate strategies for embedding neural network (NN) surrogates in nonlinear model predictive control (NMPC) formulations that are subject to systems described with partial differential equations and that are solved via direct transcription (i.e., simultaneous methods). This study focuses on the use of physics-informed NNs and physics-informed convolutional NNs as the internal (surrogate) models within the NMPC formulation. One strategy embeds NN models as explicit algebraic constraints, leveraging the automatic differentiation (AD) of an algebraic modelling language (AML) to evaluate the derivatives. Alternatively, the solver can be provided with derivatives computed external to the AML via the AD routines of the machine learning environment the NN is trained in. The three numerical experiments considered in this work reveal that replacing mechanistic models with NN surrogates may not always offer computational advantages when smooth activation functions are used in conjunction with a local nonlinear solver (e.g., Ipopt), even with highly nonlinear systems. Moreover, in this context, the external function evaluation of the NN surrogates often outperforms the embedding strategies that rely on explicit algebraic constraints, likely due to the difficulty in initializing the auxiliary variables and constraints introduced by explicit algebraic reformulations.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109105"},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting and Early Warning System for Wastewater Treatment Plant Sensors Using Multitask and LSTM Neural Networks: A Simulated and Real-World Case Study","authors":"Nicolò Ciuccoli ,&nbsp;Francesco Fatone ,&nbsp;Massimiliano Sgroi ,&nbsp;Anna Laura Eusebi ,&nbsp;Riccardo Rosati ,&nbsp;Laura Screpanti ,&nbsp;Adriano Mancini ,&nbsp;David Scaradozzi","doi":"10.1016/j.compchemeng.2025.109103","DOIUrl":"10.1016/j.compchemeng.2025.109103","url":null,"abstract":"<div><div>The increasing global water scarcity has made the safe reuse of treated wastewater essential, especially in agriculture, where untreated water poses risks to public health. Digitalizing Wastewater Treatment Plants (WWTPs) can enhance real-time water quality monitoring and optimize plant operations. This study implements an Early Warning System (EWS) at the Peschiera Borromeo WWTP in Milan, Italy, using predictive models based on simulated and real datasets to estimate key water quality parameters like Chemical Oxygen Demand (COD) and Total Suspended Solids (TSS). A Multi-Task Learning (MTL) neural network provided real-time predictions and sensor malfunction detection, while a Long Short-Term Memory (LSTM) network forecasted water quality up to six hours ahead. Simulated data showed high correlation coefficients above 0.98, but real-world data reduced performance to 0.31–0.67. Despite this, the EWS shows strong potential for improving treated water reuse reliability and operational efficiency in WWTPs.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109103"},"PeriodicalIF":3.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685169","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":"Planning strategies in the energy sector: Integrating bayesian neural networks and uncertainty quantification in scenario analysis & optimization","authors":"Funda Iseri ,&nbsp;Halil Iseri ,&nbsp;Harsh Shah ,&nbsp;Eleftherios Iakovou ,&nbsp;Efstratios N. Pistikopoulos","doi":"10.1016/j.compchemeng.2025.109097","DOIUrl":"10.1016/j.compchemeng.2025.109097","url":null,"abstract":"<div><div>The global energy market faces significant challenges due to increasing demand, growing competition, and the ongoing shift toward renewable sources. Addressing these complexities requires advanced methodologies that can effectively navigate uncertainty and optimize investment and operational decisions. This study presents a flexible scenario-based framework for capacity-related decision making and investment planning in energy systems comprising solar, wind, and natural gas facilities. The proposed framework integrates Bayesian Neural Networks (BNNs) into an optimization problem to address uncertainties in energy generation and demand forecasts. By leveraging posterior distributions from BNNs, the framework generates probabilistic, data-driven scenarios that capture future uncertainties. These scenarios are incorporated into a two-stage stochastic multi-period mixed-integer linear optimization model. The first stage optimizes investment decisions for new facilities prior to the realization of uncertainty, while the second stage incorporates operational costs, capacity expansions, and penalties for unmet demand across multiple future scenarios. We present a case study in Texas, demonstrating the applicability of the proposed framework. The results indicate the details on the capacity expansion and investment strategies for natural gas, wind and solar power plants to meet the increasing energy demand in the state. The model accounts for real-world considerations such as construction and expansion lag times, capacity constraints, and scenario-dependent demands. This methodology enhances the flexibility of energy systems, enabling planners to make cost-effective future investments and operational decisions through the complexities of the modern energy landscape. The proposed framework offers significant advantages over traditional methods by capturing nuanced uncertainty distributions and enabling flexible decision-making.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109097"},"PeriodicalIF":3.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808535","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":"Integrating solid direct air capture systems with green hydrogen production: Economic benefits and curtailment reduction","authors":"Sunwoo Kim ,&nbsp;Joungho Park ,&nbsp;Jay H. Lee","doi":"10.1016/j.compchemeng.2025.109102","DOIUrl":"10.1016/j.compchemeng.2025.109102","url":null,"abstract":"<div><div>The transition to a low-carbon energy system has positioned green hydrogen as a key clean energy carrier. However, the intermittent nature of renewable energy sources introduces significant challenges, such as substantial electricity curtailment, which affects both the economic feasibility and grid stability. Solid sorbent-based direct air capture systems, known for their high operational flexibility, offer a promising complementary solution to effectively utilize curtailed renewable power from green hydrogen production. This study examines the economic viability of integrating green hydrogen systems with solid direct air capture technology. The findings indicate that the integration can reduce curtailed renewable energy by up to 40 %, subsequently decreasing total annualized costs by approximately 6 % compared to operating these systems independently. Further economic improvements could be realized by optimizing the CO₂ capture-to-H₂ production ratio, capitalizing on anticipated cost reductions in direct air capture technology, and enhancing heat pump flexibility. With these improvements—including a 50 % reduction in direct air capture costs, an optimized CO₂-to-H₂ ratio, and enhanced heat pump flexibility—the economic benefits could increase from 6 % to 12 %. These results underscore the transformative potential of sector coupling in addressing the scalability challenges of green hydrogen, reducing renewable energy curtailment, and accelerating progress towards achieving net-zero and net-negative emissions goals.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109102"},"PeriodicalIF":3.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685168","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 modular electrolysis and modular high-efficiency fuel cell systems for green hydrogen production and power generation with low emission of carbon dioxide","authors":"Waraporn Kongjui ,&nbsp;Weerawat Patthaveekongka ,&nbsp;Chuttchaval Jeraputra ,&nbsp;Pornchai Bumroongsri","doi":"10.1016/j.compchemeng.2025.109101","DOIUrl":"10.1016/j.compchemeng.2025.109101","url":null,"abstract":"<div><div>This study presents a system model of the process for converting water and sunlight into green hydrogen which is then used to generate electrical energy with low emission of carbon dioxide. The proposed system model incorporates modular electrolysis cells for green hydrogen production and modular high-efficiency fuel cells for power generation. The results show that modular electrolysis cells can produce hydrogen at 149 tons/day. The produced hydrogen can be used to generate 100 MW of electricity. The carbon dioxide emission index is 0.206 tons/MWh which is lower than conventional technologies. The proposed systems have excellent performance in terms of efficiency and environmental pollution reduction. The results in this paper can be used in the process design for green hydrogen production and power generation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109101"},"PeriodicalIF":3.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685167","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 synchronous data-driven hybrid framework for optimizing hydrotreating units and hydrogen networks under uncertainty","authors":"Shizhao Chen ,&nbsp;Xin Peng ,&nbsp;Chenglin Chang ,&nbsp;Zhi Li ,&nbsp;Weimin Zhong","doi":"10.1016/j.compchemeng.2025.109050","DOIUrl":"10.1016/j.compchemeng.2025.109050","url":null,"abstract":"<div><div>Minimizing hydrogen consumption while maintaining the production quality in the refinery is increasingly important with more usage of heavy crude oil. However, the uncertainty of the impurity content in the input flow has led to the optimal solution losing efficacy. Therefore, a synchronous optimization framework for the hydrogen network and the production system is proposed. In this work, the relationship between the production state and the hydrogen demand is characterized by a hybrid model. Besides, a Wasserstein distributionally robust optimization module is inserted into the optimization of the hydrogen network, considering the uncertain condition of the impurity content in the input flow. The results show that the balance of hydrogen consumption and production quality could be improved. a lower hydrogen demand, reduced energy consumption, and higher product profit could be achieved with a stabler production state.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109050"},"PeriodicalIF":3.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628986","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":"Prediction of internal corrosion rate for gas pipeline: A new method based on transformer architecture","authors":"Li Tan ,&nbsp;Yang Yang ,&nbsp;Kemeng Zhang ,&nbsp;Kexi Liao ,&nbsp;Guoxi He ,&nbsp;Jing Tian ,&nbsp;Xin Lu","doi":"10.1016/j.compchemeng.2025.109084","DOIUrl":"10.1016/j.compchemeng.2025.109084","url":null,"abstract":"<div><div>Accurate assessment of internal corrosion rates in steel natural gas pipelines is a critical process in oil and gas pipeline integrity management. However, the existing models used for predicting internal corrosion rates often suffer from various issues, such as low accuracy, poor generalization, and a lack of interpretability. In order to appropriately address these challenges, we propose CNN-BO-Transformer, and employ DeepSHAP for enhancing the interpretability of the model. The proposed CNN-BO-Transformer is used to predict the corrosion rate in natural gas pipelines, while DeepSHAP is utilized to analyze the causal relationships between input variables and model's predictions. The proposed model is validated by using a real pipeline excavation dataset obtained from a gas field located in Northwest China, achieving an average error of 0.21mm/y. This represents reductions of 69.74 % and 66.67 % as compared to the errors of support vector regression (SVR) and the Transformer model, respectively. The proposed method significantly improves the accuracy and reliability of corrosion rate predictions in natural gas gathering and transportation pipelines, thus providing an effective approach for predictive maintenance and repair of steel gathering in transmission pipelines in gas fields.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109084"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685170","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}