Computers & Chemical Engineering最新文献_第10页

A time series forecasting method for oil production based on Informer optimized by Bayesian optimization and the hyperband algorithm (BOHB) 基于Bayesian优化和超带算法（BOHB）优化的Informer石油产量时间序列预测方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-22 DOI: 10.1016/j.compchemeng.2025.109068

Wu Deng , Xiankang Xin , Ruixuan Song , Xinzhou Yang , Weifeng Wang , Gaoming Yu

{"title":"A time series forecasting method for oil production based on Informer optimized by Bayesian optimization and the hyperband algorithm (BOHB)","authors":"Wu Deng , Xiankang Xin , Ruixuan Song , Xinzhou Yang , Weifeng Wang , Gaoming Yu","doi":"10.1016/j.compchemeng.2025.109068","DOIUrl":"10.1016/j.compchemeng.2025.109068","url":null,"abstract":"<div><div>Oil production forecasting is essential in the petroleum and natural gas sector, providing a fundamental basis for the adjustment of development plans and improving resource utilization efficiency for engineers and decision-makers. However, current deep learning models often struggle with long-term dependencies in long time series and high computational costs, limiting their effectiveness in complex time series forecasting tasks. This paper introduced the Informer model, an enhancement over the Transformer framework, to address these limitations. For evaluation and verification, the Informer model and reference models such as CNN, LSTM, GRU, CNN-GRU, and GRU-LSTM were applied to publicly available time-series datasets, and the optimal hyperparameters of the model were identified using Bayesian optimization and the hyperband algorithm (BOHB). The experimental results demonstrated that the Informer model outperformed others in computational speed, resource efficiency, and handling large-scale data, showing potential for practical applications in the future.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109068"},"PeriodicalIF":3.9,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549411","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 models and heuristics for effective pipeline decommissioning planning in the oil and gas industry 油气行业有效管道退役规划的优化模型与启发式方法

IF 3.9 2区工程技术

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

{"title":"Optimization models and heuristics for effective pipeline decommissioning planning in the oil and gas industry","authors":"Lucia Balsemão Furtado Logsdon , Virgilio José Martins Ferreira Filho , Paulo Cesar Ribas","doi":"10.1016/j.compchemeng.2025.109044","DOIUrl":"10.1016/j.compchemeng.2025.109044","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109044"},"PeriodicalIF":3.9,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511261","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

Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant 稀土元素回收厂浸出过程的代理建模与优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-22 DOI: 10.1016/j.compchemeng.2025.109061

Dimitrios M. Fardis , Donghyun Oh , Nikolaos V. Sahinidis , Alejandro Garciadiego , Andrew Lee

{"title":"Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant","authors":"Dimitrios M. Fardis , Donghyun Oh , Nikolaos V. Sahinidis , Alejandro Garciadiego , Andrew Lee","doi":"10.1016/j.compchemeng.2025.109061","DOIUrl":"10.1016/j.compchemeng.2025.109061","url":null,"abstract":"<div><div>Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109061"},"PeriodicalIF":3.9,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509479","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

Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis 基于物理信息的VGAE因果发现和故障诊断中的熵增强批抽样和保形学习

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-20 DOI: 10.1016/j.compchemeng.2025.109053

Mohammadhossein Modirrousta, Alireza Memarian, Biao Huang

{"title":"Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis","authors":"Mohammadhossein Modirrousta, Alireza Memarian, Biao Huang","doi":"10.1016/j.compchemeng.2025.109053","DOIUrl":"10.1016/j.compchemeng.2025.109053","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109053"},"PeriodicalIF":3.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471227","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

Noninvasive inline imaging and computer vision-based quality variable estimation for continuous slug-flow crystallizers 连续段塞流结晶器的无创在线成像和基于计算机视觉的质量变量估计

IF 3.9 2区工程技术

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

{"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 活性物质的多尺度因果关系

IF 3.9 2区工程技术

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

{"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 基于cubatu的非线性回归模型的不确定性估计

IF 3.9 2区工程技术

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

引用次数: 0

ChemBERTa embeddings and ensemble learning for prediction of density and melting point of deep eutectic solvents with hybrid features ChemBERTa嵌入和集成学习用于预测具有混合特征的深共晶溶剂的密度和熔点

IF 3.9 2区工程技术

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

{"title":"ChemBERTa embeddings and ensemble learning for prediction of density and melting point of deep eutectic solvents with hybrid features","authors":"Ting Wu , Peilin Zhan , Wei Chen , Miaoqing Lin , Quanyuan Qiu , Yinan Hu , Jiuhang Song , Xiaoqing Lin","doi":"10.1016/j.compchemeng.2025.109065","DOIUrl":"10.1016/j.compchemeng.2025.109065","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109065"},"PeriodicalIF":3.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429981","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

From automated to autonomous process operations 从自动化到自主流程操作

IF 3.9 2区工程技术

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

引用次数: 0

Piecewise linear approximation using J1 compatible triangulations for efficient MILP representation 使用J1兼容三角剖分的分段线性逼近，用于高效的MILP表示

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