Computers & Chemical Engineering最新文献_第5页

Piecewise linear approximation using J1 compatible triangulations for efficient MILP representation

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-15 DOI: 10.1016/j.compchemeng.2025.109042

Felix Birkelbach

引用次数: 0

CPU and GPU based acceleration of high-dimensional population balance models via the vectorization and parallelization of multivariate aggregation and breakage integral terms

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-15 DOI: 10.1016/j.compchemeng.2025.109037

Ashley Dan , Urjit Patil , Abhinav De , Bhavani Nandhini Mummidi Manuraj , Rohit Ramachandran

{"title":"CPU and GPU based acceleration of high-dimensional population balance models via the vectorization and parallelization of multivariate aggregation and breakage integral terms","authors":"Ashley Dan , Urjit Patil , Abhinav De , Bhavani Nandhini Mummidi Manuraj , Rohit Ramachandran","doi":"10.1016/j.compchemeng.2025.109037","DOIUrl":"10.1016/j.compchemeng.2025.109037","url":null,"abstract":"<div><div>The development of mathematical models for physical systems often necessitates the use of high-dimensional spaces and fine discretizations to accurately capture complex dynamics. These models, which involve large matrices and extensive mathematical operations, tend to be computationally intensive, leading to slow execution times. In this study, we analyzed various acceleration strategies by comparing the simulation accuracy, computational time, and resource utilization of various vectorization and parallelization methods on both CPUs and GPUs, using a multi-dimensional Population Balance Model simulated in MATLAB and Python. Our findings revealed that GPU-based vectorization provided the highest performance, achieving a 40-fold speedup compared to the serial implementations. Unlike simulations on CPUs, where run time is often limited by processing power, GPUs simulations are limited by the available memory, especially at high resolution. This work highlights the importance of using appropriate resources and code optimization strategies to reduce computational time, for development of an efficient model.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109037"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422192","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}

引用次数: 0

Optimization of methanol synthesis under forced periodic operation in a non-isothermal fixed-bed reactor

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-15 DOI: 10.1016/j.compchemeng.2025.109040

Johannes Leipold , Daliborka Nikolic , Andreas Seidel-Morgenstern , Achim Kienle

{"title":"Optimization of methanol synthesis under forced periodic operation in a non-isothermal fixed-bed reactor","authors":"Johannes Leipold , Daliborka Nikolic , Andreas Seidel-Morgenstern , Achim Kienle","doi":"10.1016/j.compchemeng.2025.109040","DOIUrl":"10.1016/j.compchemeng.2025.109040","url":null,"abstract":"<div><div>Methanol synthesis with a conventional Cu/ZnO/Al<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>-catalyst is typically carried out under stationary conditions. However, due to the process non-linearities, dynamic operation may improve the reactor performance. This paper numerically investigates such a potential of improvement through forced periodic operation of methanol synthesis in a non-isothermal lab-scale fixed-bed reactor. A multi-objective optimization is performed in which both the molar flow rate of methanol and the yield of methanol based on the used carbon molecules are considered as objective functions. The best possible steady state operation is then compared with the best possible periodic operation to evaluate the full potential of improvement. Focus is on periodic forcing of two inputs with same forcing frequency but different phase. Several possible input combinations are considered systematically. In particular the possibility of inlet and/or cooling temperature modulation is explored and compared. The results demonstrate a significant improvement for several input combinations through forced periodic operation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109040"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452983","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}

引用次数: 0

Selectivity engineering with single-feed hybrid reactive distillation configurations: Complex reaction schemes having nonideal kinetics with/without inerts

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-15 DOI: 10.1016/j.compchemeng.2025.109046

Deepshikha Singh, Antarim Dutta, Ankur Gaur, Shabih Ul Hasan

{"title":"Selectivity engineering with single-feed hybrid reactive distillation configurations: Complex reaction schemes having nonideal kinetics with/without inerts","authors":"Deepshikha Singh, Antarim Dutta, Ankur Gaur, Shabih Ul Hasan","doi":"10.1016/j.compchemeng.2025.109046","DOIUrl":"10.1016/j.compchemeng.2025.109046","url":null,"abstract":"<div><div>The present contribution is first of its kind in the field of conceptual designs of reactive distillation (RD) configurations, focusing on the impact of nonideal kinetics in obtaining the feasible designs of desired selectivity for complex reaction schemes, both with and without inert components. Our earlier work on selectivity engineering with reactive distillation through a series of publications was restricted to complex reaction schemes with ideal kinetics only. In this work, we extend it for nonideal kinetics and explore the impact of nonideal kinetics on the choice of hybrid RD configuration (HRDC) needed to achieve the desired selectivity for intermediate products. It has been found that the choice of HRDC strongly depends on the number of components involved, including inert components in a given complex reaction scheme with nonideal kinetics. The developed methodology was successfully applied to four industrially important multireaction schemes that featured nonideal kinetics with/without inerts.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109046"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stochastic algorithm-based optimization using artificial intelligence/machine learning models for sorption enhanced steam methane reformer reactor

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-14 DOI: 10.1016/j.compchemeng.2025.109060

Sumit K. Bishnu , Sabla Y. Alnouri , Dhabia M. Al Mohannadi

引用次数: 0

Operational flexibility-oriented selection of working fluid for organic Rankine cycles via Bayesian optimization

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-13 DOI: 10.1016/j.compchemeng.2025.109043

Jiayuan Wang, Yuxin Zhang, Chentao Mei, Lingyu Zhu

{"title":"Operational flexibility-oriented selection of working fluid for organic Rankine cycles via Bayesian optimization","authors":"Jiayuan Wang, Yuxin Zhang, Chentao Mei, Lingyu Zhu","doi":"10.1016/j.compchemeng.2025.109043","DOIUrl":"10.1016/j.compchemeng.2025.109043","url":null,"abstract":"<div><div>Working fluid selection is a crucial part of organic Rankine cycle (ORC) designs. Traditional selection methods primarily focus on optimizing performance under specific nominal operating conditions, often neglecting potential efficiency losses and feasibility issues that may arise under off-design conditions due to fluctuations in the heat source and sink. This research introduces a novel method for optimizing working fluid selection to achieve robust and efficient operation in the face of environmental variations. Specifically, operational flexibility is analyzed based on the ORC operational model to capture performance deviations from nominal conditions, and is quantified by evaluating the size of the feasible operational region within the uncertain parameter space. Working fluid selection is optimized simultaneously with the cycle configurations, resulting in a computationally challenging mixed-integer nonlinear programming (MINLP) problem, which is addressed through Bayesian optimization. A case study on geothermal brine heat recovery with a recuperative ORC compares flexibility-oriented and conventional working fluid selections, demonstrating a 102% increase in operational flexibility at the cost of an 11.5% efficiency loss. This research underscores the significant impact of working fluid selection on operational flexibility and demonstrates the effectiveness of Bayesian optimization in solving complex MINLP problems for integrated molecule-level and process-level designs.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109043"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A supply chain design for creating microalgae-based biodiesel considering resources nexus and uncertainty

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-11 DOI: 10.1016/j.compchemeng.2025.109047

Naeme Zarrinpoor , Kannan Govindan

引用次数: 0

A unified model integrating Granger causality-based causal discovery and fault diagnosis in chemical processes

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-11 DOI: 10.1016/j.compchemeng.2025.109028

Feiya Lv , Borui Yang , Shujian Yu , Shengwu Zou , Xiaolin Wang , Jinsong Zhao , Chenglin Wen

{"title":"A unified model integrating Granger causality-based causal discovery and fault diagnosis in chemical processes","authors":"Feiya Lv , Borui Yang , Shujian Yu , Shengwu Zou , Xiaolin Wang , Jinsong Zhao , Chenglin Wen","doi":"10.1016/j.compchemeng.2025.109028","DOIUrl":"10.1016/j.compchemeng.2025.109028","url":null,"abstract":"<div><div>Reasoning about cause and effect in industrial processes is fundamental to fault diagnosis. However, traditional methods for causal discovery and fault diagnosis are typically developed separately, resulting in complex and fragmented approaches that lack transparency and interpretability. Since the explicit identification of root causes from causal graphs remains an open issue, we propose a unified diagnosis model for chemical processes that integrates causal discovery, fault detection, and root cause diagnosis within a single framework. Granger causality is learned from monitoring time-series data for online predictions. This causal embedding ensures that prediction deviations occur only in variables causally linked to the root cause, effectively mitigating the ’smearing effect’ caused by unrelated variables. The explicit causal graph provides interpretive insights into fault propagation and enhances the traceability of the diagnostic process by enabling the identification of fault evolution paths and root causes. Experimental results on synthetic data, a continuously stirred-tank reactor (CSTR) process, and a real-world continuous catalytic reforming (CCR) process demonstrate that our approach achieves high diagnostic accuracy and low false alarm rates, offering a practical, interpretable, and scalable solution for fault diagnosis in industrial chemical processes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109028"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating feature attribution and symbolic regression for automatic model structure identification and strategic sampling

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-11 DOI: 10.1016/j.compchemeng.2025.109036

Alexander W. Rogers , Amanda Lane , Cesar Mendoza , Simon Watson , Adam Kowalski , Philip Martin , Dongda Zhang

{"title":"Integrating feature attribution and symbolic regression for automatic model structure identification and strategic sampling","authors":"Alexander W. Rogers , Amanda Lane , Cesar Mendoza , Simon Watson , Adam Kowalski , Philip Martin , Dongda Zhang","doi":"10.1016/j.compchemeng.2025.109036","DOIUrl":"10.1016/j.compchemeng.2025.109036","url":null,"abstract":"<div><div>In today's competitive and dynamic global markets, rapidly designing processes for formulated products – complex blends such as cosmetics, detergents, or personal care goods – is both essential and challenging. Understanding how processing conditions and chemical composition interact to determine product key performance indicators (KPIs) often remains unclear. In this work, we introduce a novel model-based design of experiments (MbDoE) framework that combines artificial neural network feature attribution with symbolic regression (SR) to uncover interpretable physical relationships. By leveraging feature attribution to guide the search within SR's large combinatorial space, our method efficiently targets structural improvements in candidate models. Additionally, a strategic sampling approach determining the most informative time points to measure KPI determining attributes ensures that each experiment yields maximum information. Applied to a comprehensive in-silico case study, the framework successfully recovered the differential equations for the underlying mechanisms driving the rate of change in the KPIs during the formulated product manufacturing process and reduced the required number of experiments threefold, even with limited data availability. These results highlight the significant potential of artificial neural network guided SR-MbDoE to accelerate process flow diagram development, enhance understanding of complex formulated processes, and improve decision-making in the chemical industry.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109036"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592107","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}

引用次数: 0

Advancing algal biofuel production through data-driven insights: A comprehensive review of machine learning applications

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-10 DOI: 10.1016/j.compchemeng.2025.109049

Olakunle Ayodeji Omole , Chukwuma C. Ogbaga , Jude A. Okolie , Olugbenga Akande , Richard Kimera , Joseph Lepnaan Dayil

{"title":"Advancing algal biofuel production through data-driven insights: A comprehensive review of machine learning applications","authors":"Olakunle Ayodeji Omole , Chukwuma C. Ogbaga , Jude A. Okolie , Olugbenga Akande , Richard Kimera , Joseph Lepnaan Dayil","doi":"10.1016/j.compchemeng.2025.109049","DOIUrl":"10.1016/j.compchemeng.2025.109049","url":null,"abstract":"<div><div>This paper examines machine learning (ML)'s contemporary applications in biofuel production, emphasizing microalgae-based bioenergy systems. The study aims to explore various aspects of ML integration in the biofuel production process, including microalgae detection, classification, growth phase optimization, and dataset quality and quantity considerations. The research methodology is in a detailed literature review of current ML models and their applications in biofuel production. It covers bioenergy systems, microalgae detection, growth phase optimization, dataset quality, ML applications in microalgal biorefineries, and the advantages and disadvantages of ML models over first-principle models. The analysis highlights the challenges and implications of utilizing smaller datasets in biofuel production models and investigates the impact of dataset quality and quantity on ML model performance. Despite sparse datasets, the findings offer insights into leveraging ML techniques for improved efficiency and sustainability in microalgae-based biofuel production systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109049"},"PeriodicalIF":3.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0