Computers & Chemical Engineering最新文献_第2页

A multi-objective robust scenario-based stochastic chance constrained programming model for sustainable closed-loop agri-food supply chain

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-20 DOI: 10.1016/j.compchemeng.2024.108914

Misagh Rahbari, Alireza Arshadi Khamseh, Mohammad Mohammadi

{"title":"A multi-objective robust scenario-based stochastic chance constrained programming model for sustainable closed-loop agri-food supply chain","authors":"Misagh Rahbari, Alireza Arshadi Khamseh, Mohammad Mohammadi","doi":"10.1016/j.compchemeng.2024.108914","DOIUrl":"10.1016/j.compchemeng.2024.108914","url":null,"abstract":"<div><div>The agri-food supply chain management plays a crucial role in ensuring the interests of supply chain components and food security in society. Additionally, due to the nature of agri-food products, sustainability dimensions have always been of concern to organizations engaged in this field. The importance of the timely and quality provision of agri-food products has doubled after the global crisis. Therefore, this study focuses on optimizing and analyzing the sustainable multi-objective closed-loop supply chain network for agri-food products, with a case study on the canned food under uncertainty. Strategic and operational decisions and other features are considered to achieve more accurate results. To address the various dimensions of sustainability, the problem is considered as a four-objective one, aiming to maximize the use of available production throughput for factories, maximize job opportunities created, minimize supply chain costs, and ultimately minimize unmet demands. The carbon cap and trade mechanism is used to control greenhouse gas emissions in the supply chain network. A robust scenario-based stochastic chance constrained programming approach is employed to deal with the uncertainty, and also validation is performed using various criteria. Moreover, an augmented ε-constraint optimization approach is used to solve the multi-objective problem and achieve Pareto optimal solutions. Finally, sensitivity analysis is employed to prepare for potential changes in some problem parameters.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108914"},"PeriodicalIF":3.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747135","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

An optimization approach for sustainable and resilient closed-loop floating solar photovoltaic supply chain network design 可持续弹性闭环浮动太阳能光伏供应链网络设计的优化方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-19 DOI: 10.1016/j.compchemeng.2024.108927

Maryam Nili , Mohammad Saeed Jabalameli , Armin Jabbarzadeh , Ehsan Dehghani

{"title":"An optimization approach for sustainable and resilient closed-loop floating solar photovoltaic supply chain network design","authors":"Maryam Nili , Mohammad Saeed Jabalameli , Armin Jabbarzadeh , Ehsan Dehghani","doi":"10.1016/j.compchemeng.2024.108927","DOIUrl":"10.1016/j.compchemeng.2024.108927","url":null,"abstract":"<div><div>Growing energy demand and its consequences, such as fossil fuel depletion, greenhouse gas emissions, and global warming, prompted the need for large-scale solar power plants. Floating photovoltaic systems have many advantages over ground-mounted systems, including methods and resources, reducing costs, and improving efficiency. In this regard, this study aims at presenting an optimization model for developing a sustainable and resilient floating solar photovoltaic supply chain network design. The concerned model's objective function is minimizing the total supply chain costs in addition to maximizing greenhouse gas emissions reduction. To identify the most suitable dams for establishing the floating photovoltaic system, the hybrid approach by applying the fuzzy best-worst method and the TOPSIS technique is first exploited. Thereinafter, the selected dams are exerted in the presented mathematical model. Eventually, a real case study is implemented on floating photovoltaic systems to assess the proposed model's performance, from which important managerial insights are attained.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108927"},"PeriodicalIF":3.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722609","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

Highly accelerated kinetic Monte Carlo models for depolymerisation systems 解聚系统的高度加速动力学蒙特卡洛模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-19 DOI: 10.1016/j.compchemeng.2024.108945

Dominic Bui Viet, Gustavo Fimbres Weihs, Gobinath Rajarathnam, Ali Abbas

引用次数: 0

Multilevel on-the-fly sparse grids for coupling coarse-grained and high fidelity models in heterogeneous catalysis

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-18 DOI: 10.1016/j.compchemeng.2024.108922

Tobias Hülser , Bjarne Kreitz , C. Franklin Goldsmith , Sebastian Matera

引用次数: 0

A semi-supervised learning algorithm for high and low-frequency variable imbalances in industrial data 针对工业数据中高频和低频变量不平衡的半监督学习算法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-17 DOI: 10.1016/j.compchemeng.2024.108933

Jiannan Zhu , Chen Fan , Minglei Yang , Feng Qian , Vladimir Mahalec

引用次数: 0

Extracting key temporal and cyclic features from VIT data to predict lithium-ion battery knee points using attention mechanisms

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-16 DOI: 10.1016/j.compchemeng.2024.108931

Jaewook Lee , Seongmin Heo , Jay H. Lee

{"title":"Extracting key temporal and cyclic features from VIT data to predict lithium-ion battery knee points using attention mechanisms","authors":"Jaewook Lee , Seongmin Heo , Jay H. Lee","doi":"10.1016/j.compchemeng.2024.108931","DOIUrl":"10.1016/j.compchemeng.2024.108931","url":null,"abstract":"<div><div>Accurate prediction of lithium-ion battery lifespan is crucial for mitigating risks, as battery cycling experiments are time-consuming and costly. Despite this, few studies have effectively leveraged cycling data with minimal information loss and optimized input size. To bridge this gap, we propose three models that integrate attention layers into a foundational model. Temporal attention helps address the vanishing gradient problem inherent in recurrent neural networks, enabling a manageable input size for subsequent networks. Self-attention applied to context vectors, termed cyclic attention, allows models to efficiently identify key cycles that capture the majority of information across cycles, strategically reducing input size. By employing multi-head attention, required input size is reduced from 100 to 30 cycles, significant reduction than single-head approaches, as each head accentuates distinct key cycles from various perspectives. Our enhanced model shows a 39.6 % improvement in regression performance using only the first 30 cycles, significantly advancing our previous method.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108931"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759818","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

Digital twins in process engineering: An overview on computational and numerical methods 过程工程中的数字双胞胎：计算和数值方法概述

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-16 DOI: 10.1016/j.compchemeng.2024.108917

Luisa Peterson , Ion Victor Gosea , Peter Benner , Kai Sundmacher

引用次数: 0

Prediction of viscoelastic and printability properties on binder-free TiO2-based ceramic pastes by DIW through a machine learning approach 通过机器学习方法预测 DIW 不含粘合剂的 TiO2 基陶瓷浆料的粘弹性和可印刷性

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-16 DOI: 10.1016/j.compchemeng.2024.108920

Luis Antonio Pulido-Victoria , Antonio Flores-Tlacuahuac , Alexander Panales-Pérez , Tania E. Lara-Ceniceros , Manuel Alejandro Ávila-López , José Bonilla-Cruz

{"title":"Prediction of viscoelastic and printability properties on binder-free TiO2-based ceramic pastes by DIW through a machine learning approach","authors":"Luis Antonio Pulido-Victoria , Antonio Flores-Tlacuahuac , Alexander Panales-Pérez , Tania E. Lara-Ceniceros , Manuel Alejandro Ávila-López , José Bonilla-Cruz","doi":"10.1016/j.compchemeng.2024.108920","DOIUrl":"10.1016/j.compchemeng.2024.108920","url":null,"abstract":"<div><div>Ceramic 3D printing has become an increasingly popular manufacturing technique due to its potential to create complex geometries with high precision. However, predicting the printability of ceramic pastes remains a challenge, as it depends on various rheological properties. In this study, we propose a feed-forward deep neural network model that predicts the printability of ceramic pastes based on two suggested criteria, a shear-thinning ability and a gel point greater than <span><math><mrow><mn>1</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> Pa. The model is trained on a dataset built from rheological and viscoelastic characterizations of pastes, and validated on a separate test set. Our results show that the proposed learning model achieves small relative error in predicting the gel point of the ceramic pastes, with a mean value of 8.99181 and a standard deviation of 1.812864. This model has the potential to improve the efficiency and quality of ceramic 3D printing by enabling rapid and accurate predictions of paste printability.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108920"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722672","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

Advancing machine learning in Industry 4.0: Benchmark framework for rare-event prediction in chemical processes

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-15 DOI: 10.1016/j.compchemeng.2024.108929

Vikram Sudarshan, Warren D. Seider

{"title":"Advancing machine learning in Industry 4.0: Benchmark framework for rare-event prediction in chemical processes","authors":"Vikram Sudarshan, Warren D. Seider","doi":"10.1016/j.compchemeng.2024.108929","DOIUrl":"10.1016/j.compchemeng.2024.108929","url":null,"abstract":"<div><div>Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics: <em>RMSE,</em> model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108929"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747143","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

Methodology for hyperparameter tuning of deep neural networks for efficient and accurate molecular property prediction 深度神经网络超参数调整方法，用于高效准确的分子特性预测

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-14 DOI: 10.1016/j.compchemeng.2024.108928

Xuan Dung James Nguyen, Y.A. Liu

引用次数: 0