Computers & Chemical Engineering最新文献

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Optimization models and heuristics for effective pipeline decommissioning planning in the oil and gas industry

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-22 DOI: 10.1016/j.compchemeng.2025.109044

Lucia Balsemão Furtado Logsdon , Virgilio José Martins Ferreira Filho , Paulo Cesar Ribas

Efficient operation sequencing is crucial in industrial processes to minimize delays and optimize resource utilization. This study focuses on the sequencing of operations for the recovery of decommissioned submarine pipelines, aiming to minimize project completion times. Unlike traditional sequencing problems, our approach incorporates unique constraints such as precedence relationships and the composition of trips for pipeline removal. We propose an optimization framework integrating a mathematical model and a hybrid solution that combines metaheuristic algorithms with exact methods for solving large-scale instances. Computational experiments were conducted on 40 instances of 100 pipelines each, randomly drawn from real-world data. The heuristic generated feasible initial solutions in all cases and enabled the mathematical model to find optimal solutions in 42.5% of the instances. However, in 35% of the cases, no feasible solutions were obtained within the time limit. For cases where the solver reached a solution, the average project completion time was 214.07 days, with a median of 0.0 and a standard deviation of 547.35 days. A real-world case study highlighted the practical applicability of the proposed approach. Using the constructive heuristic as the solver’s initial solution achieved the best result within 5000 s, with an objective function value of 9774 days. This work is particularly relevant in Brazil’s Oil and Gas industry, where deep-water flexible pipelines and strict environmental deadlines demand effective optimization models for decommissioning planning.

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引用次数: 0

Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-20 DOI: 10.1016/j.compchemeng.2025.109053

Mohammadhossein Modirrousta, Alireza Memarian, Biao Huang

Industry 4.0 has increased the demand for advanced fault detection and diagnosis (FDD) in complex industrial processes. This research introduces a novel approach to causal discovery and FDD using Variational Graph Autoencoders (VGAEs) enhanced with physics-informed constraints and conformal learning. Our method addresses limitations in conventional techniques, such as Granger causality, which struggle with high-dimensional, nonlinear systems. By integrating Graph Convolutional Networks (GCNs) and an entropy-based dynamic edge sampling method, the framework focuses on high-uncertainty regions of the causal graph. Conformal learning establishes rigorous thresholds for causal inference. Validated through simulation and case studies, including an Australian refinery and the Tennessee Eastman Process, our approach improves causal discovery accuracy, reduces spurious connections, and enhances fault classification. Integrating domain-specific physics information also led to faster convergence and reduced computational demands. This research provides an efficient, statistically robust approach for causal discovery and FDD in complex industrial systems.

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引用次数: 0

Noninvasive inline imaging and computer vision-based quality variable estimation for continuous slug-flow crystallizers

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-19 DOI: 10.1016/j.compchemeng.2025.109067

Derrick Adams , Jay H. Lee , Shin Hyuk Kim , Seongmin Heo

This study presents a transformative approach for the real-time monitoring of continuous slug-flow crystallizers in the pharmaceutical and fine chemical industries, marking a shift from traditional batch processing to continuous manufacturing. By leveraging advanced computer vision techniques within inline imaging systems, including single, binocular, and trinocular stereo visions, we offer a novel solution for the multispatial monitoring and analysis of the crystallization process. This methodology facilitates the automatic detection of solution slugs and bulk crystal regions, enabling the estimation of dynamic bulk crystal density, slug volumes, and porosity in real time. The deployment of ResNet18 and Mask R-CNN models underpins the method's efficacy, demonstrating remarkable performance metrics: ResNet18 ensures precise image detection, while Mask R-CNN achieves an average precision (AP) of 96.4%, with 100% at both AP50 and AP75 thresholds for bulk crystals and solution slugs’ segmentation. These results validate the models’ accuracy and reliability in estimating quality variables essential for continuous slug flow crystallization. This advancement not only addresses the limitations of existing monitoring methods but also signifies a leap forward in applying computer vision for process monitoring, offering significant implications for enhancing decision-making, optimization, and control in continuous manufacturing operations.

{"title":"Noninvasive inline imaging and computer vision-based quality variable estimation for continuous slug-flow crystallizers","authors":"Derrick Adams , Jay H. Lee , Shin Hyuk Kim , Seongmin Heo","doi":"10.1016/j.compchemeng.2025.109067","DOIUrl":"10.1016/j.compchemeng.2025.109067","url":null,"abstract":"<div><div>This study presents a transformative approach for the real-time monitoring of continuous slug-flow crystallizers in the pharmaceutical and fine chemical industries, marking a shift from traditional batch processing to continuous manufacturing. By leveraging advanced computer vision techniques within inline imaging systems, including single, binocular, and trinocular stereo visions, we offer a novel solution for the multispatial monitoring and analysis of the crystallization process. This methodology facilitates the automatic detection of solution slugs and bulk crystal regions, enabling the estimation of dynamic bulk crystal density, slug volumes, and porosity in real time. The deployment of ResNet18 and Mask R-CNN models underpins the method's efficacy, demonstrating remarkable performance metrics: ResNet18 ensures precise image detection, while Mask R-CNN achieves an average precision (AP) of 96.4%, with 100% at both AP50 and AP75 thresholds for bulk crystals and solution slugs’ segmentation. These results validate the models’ accuracy and reliability in estimating quality variables essential for continuous slug flow crystallization. This advancement not only addresses the limitations of existing monitoring methods but also signifies a leap forward in applying computer vision for process monitoring, offering significant implications for enhancing decision-making, optimization, and control in continuous manufacturing operations.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109067"},"PeriodicalIF":3.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471647","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

Multi-scale causality in active matter

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-19 DOI: 10.1016/j.compchemeng.2025.109052

Alexander Smith, Dipanjan Ghosh, Andrew Tan, Xiang Cheng, Prodromos Daoutidis

Deciphering how local interactions drive self-assembly and multi-scale organization is essential for understanding active matter systems, such as self-organizing bacterial colonies. This study combines topological data analysis with causal discovery to capture the complex, hierarchical causality within these dynamic systems. By leveraging the Euler characteristic as a topological descriptor, we reduce high-dimensional, multi-scale data into essential structural representations, enabling efficient, meaningful analysis. Through causal discovery methods applied to the topology of these dynamic, multi-scale structures, we reveal how localized bacterial interactions propagate, guiding global organization in systems with both homogeneous and heterogeneous ordering. The findings indicate that, while ordering patterns may differ, the mechanisms underlying multi-scale self-assembly remain consistent, with information flowing primarily from local, highly-ordered structures. This framework enhances understanding of self-organization principles and supports applications requiring scalable causal analysis in complex data environments across natural and synthetic active matter.

{"title":"Multi-scale causality in active matter","authors":"Alexander Smith, Dipanjan Ghosh, Andrew Tan, Xiang Cheng, Prodromos Daoutidis","doi":"10.1016/j.compchemeng.2025.109052","DOIUrl":"10.1016/j.compchemeng.2025.109052","url":null,"abstract":"<div><div>Deciphering how local interactions drive self-assembly and multi-scale organization is essential for understanding active matter systems, such as self-organizing bacterial colonies. This study combines topological data analysis with causal discovery to capture the complex, hierarchical causality within these dynamic systems. By leveraging the Euler characteristic as a topological descriptor, we reduce high-dimensional, multi-scale data into essential structural representations, enabling efficient, meaningful analysis. Through causal discovery methods applied to the topology of these dynamic, multi-scale structures, we reveal how localized bacterial interactions propagate, guiding global organization in systems with both homogeneous and heterogeneous ordering. The findings indicate that, while ordering patterns may differ, the mechanisms underlying multi-scale self-assembly remain consistent, with information flowing primarily from local, highly-ordered structures. This framework enhances understanding of self-organization principles and supports applications requiring scalable causal analysis in complex data environments across natural and synthetic active matter.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109052"},"PeriodicalIF":3.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509477","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

Cubature-based uncertainty estimation for nonlinear regression models

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-17 DOI: 10.1016/j.compchemeng.2025.109035

Martin Bubel , Jochen Schmid , Maximilian Carmesin , Volodymyr Kozachynskyi , Erik Esche , Michael Bortz

Models are commonly utilized in chemical engineering to simulate real-world processes and phenomena. Given their role in guiding decision-making, accurately quantifying the uncertainty of these models is essential. Typically, these models are calibrated using experimental data that contain measurement errors, leading to uncertainty in the fitted model parameters. Current methods for estimating the prediction uncertainty of nonlinear regression models are often either computationally intensive or biased. In this study, we use sparse cubature formulas to estimate the prediction uncertainty of nonlinear regression models. Our findings indicate that this method provides a favorable balance between accuracy and computational efficiency, making it suitable for application in chemical engineering. We validate the performance of our proposed method through various regression case studies, including both theoretical toy models and practical models from chemical engineering.

引用次数: 0

ChemBERTa embeddings and ensemble learning for prediction of density and melting point of deep eutectic solvents with hybrid features

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-16 DOI: 10.1016/j.compchemeng.2025.109065

Ting Wu , Peilin Zhan , Wei Chen , Miaoqing Lin , Quanyuan Qiu , Yinan Hu , Jiuhang Song , Xiaoqing Lin

Deep eutectic solvents (DESs) are sustainable alternatives to traditional solvents, but their structural complexity makes accurate prediction of melting points and densities challenging. This study utilizes ChemBERTa, a pre-trained Transformer model, to extract high-dimensional embeddings from Simplified Molecular Input Line Entry System (SMILES) strings, effectively capturing complex molecular interactions and subtle structural features. Through feature importance analysis, we identified missing molecular information in the ChemBERTa embeddings and supplemented it with select physicochemical descriptors from RDKit, creating a feature set that enhances both interpretability and predictive accuracy. Optimized ensemble models, including ExtraTreesRegressor (ETR) and XGBRegressor (XGBR), are then applied to this enriched feature set, achieving notable improvements in prediction accuracy for DES melting point and density. Rigorous grid search and ten-fold cross-validation ensure model robustness and generalizability. Experimental results confirm the effectiveness of this approach, underscoring the transformative role of pre-trained deep learning models in chemical informatics and supporting scalable, sustainable DESs design.

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引用次数: 0

From automated to autonomous process operations

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-16 DOI: 10.1016/j.compchemeng.2025.109064

Michael Baldea , Apostolos T. Georgiou , Bhushan Gopaluni , Mehmet Mercangöz , Constantinos C. Pantelides , Kiran Sheth , Victor M. Zavala , Christos Georgakis

This paper considers current trends towards a higher degree of automation of process operations. Often referred to as “autonomous” process operations, these developments involve cyber-physical systems that can automate tasks that have hitherto relied extensively on human plant operators and, in particular, on their accurate assessment of the current plant situation based on a multitude of information sources, and on their ability to devise and implement plans of actions for dealing with often novel situations. The paper analyses the main drivers behind the need for a higher level of automation in process operations, and reviews the industrial applications that have been described in the public domain to date. It also presents a review of advances and potential impact of some of the enabling technologies for autonomy; these include sensors, mathematical modelling abstractions, reinforcement learning, knowledge graphs, and large language models.

引用次数: 0

Piecewise linear approximation using J1 compatible triangulations for efficient MILP representation

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-15 DOI: 10.1016/j.compchemeng.2025.109042

Felix Birkelbach

For including piecewise linear (PWL) functions in MILP problems, the logarithmic convex combination (Log) formulation has been shown to yield very fast solving times. However, identifying approximations that can be used with Log is a big challenge since the approximation has to be compatible with a J1 triangulation. In this article, an algorithm is proposed that identifies approximations using J1 compatible triangulations. It seeks to satisfy the specified error tolerance with the minimum number of linear pieces, so that the MILP formulation is small. To evaluate the performance of the J1 approach it is applied to two sets of benchmark functions from literature and results are compared to state-of-the-art approaches.

Overall the J1 approach is shown to efficiently approximate functions in up to 3 dimensions. Especially for tight error tolerances, these J1 approximations require fewer auxiliary variables in MILP compared to alternative approaches.

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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

{"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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-02-15 DOI: 10.1016/j.compchemeng.2025.109040

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

Methanol synthesis with a conventional Cu/ZnO/Al

_{2}

_{3}

-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.

{"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

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