Computers & Chemical Engineering最新文献_第9页

Enhancing profitability in p-xylene production via toluene methylation

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-24 DOI: 10.1016/j.compchemeng.2024.108951

Subin Jung, Yuchan Ahn

引用次数: 0

Gas dispersion modeling in stereoscopic space with obstacles using a novel spatiotemporal prediction network 基于时空预测网络的有障碍物立体空间气体分散模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-24 DOI: 10.1016/j.compchemeng.2024.108934

Shikuan Chen, Wenli Du, Xinjie Wang, Bing Wang, Chenxi Cao, Xin Peng

{"title":"Gas dispersion modeling in stereoscopic space with obstacles using a novel spatiotemporal prediction network","authors":"Shikuan Chen, Wenli Du, Xinjie Wang, Bing Wang, Chenxi Cao, Xin Peng","doi":"10.1016/j.compchemeng.2024.108934","DOIUrl":"10.1016/j.compchemeng.2024.108934","url":null,"abstract":"<div><div>Gas leakage can lead to catastrophic consequences on both the environment and human health. To mitigate these losses, it is imperative to develop accurate and efficient spatiotemporal models for gas dispersion. The gas diffusion process occurs in a 3-dimensional (3D) space, but most research has been confined to flat-plane scenarios, neglecting the stereoscopic distribution of gas concentrations. To address this issue, we propose a novel method that combines 3D convolution with a long short-term memory neural network (3DConvLSTM) to forecast the 3D spatiotemporal concentration distribution of gas leakage in obstructed scenes. The 3D convolutional filters fully operate in the spatial domain, capturing spatial features horizontally and vertically. To provide data for the experiment, ethane leak scenarios with different sources, rates and wind directions are simulated by computational fluid dynamics (CFD). The results demonstrate that the 3DConvLSTM exhibits higher accuracy and requires fewer parameters, highlighting the effectiveness of the proposed method.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108934"},"PeriodicalIF":3.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756623","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

Linear and neural network models for predicting N-glycosylation in Chinese Hamster Ovary cells based on B4GALT levels 基于B4GALT水平预测中国仓鼠卵巢细胞n -糖基化的线性和神经网络模型

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-23 DOI: 10.1016/j.compchemeng.2024.108937

Pedro Seber, Richard D. Braatz

引用次数: 0

Designing a sustainable-resilient vaccine cold chain network in uncertain environments 在不确定环境下设计可持续弹性疫苗冷链网络

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-23 DOI: 10.1016/j.compchemeng.2024.108936

Yanju Chen , Mengxuan Chen , Tianran Hu

引用次数: 0

A robust batch-to-batch optimization framework for pharmaceutical applications 一个用于制药应用的健壮的批对批优化框架

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-22 DOI: 10.1016/j.compchemeng.2024.108935

Ali Ghodba , Anne Richelle , Chris McCready , Luis Ricardez-Sandoval , Hector Budman

引用次数: 0

Real-time update of data-driven reduced and full order models with applications 实时更新数据驱动的简化和全订单模型与应用程序

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-22 DOI: 10.1016/j.compchemeng.2024.108923

Om Prakash, Biao Huang

引用次数: 0

Virtual sample generation for soft-sensing in small sample scenarios using glow-embedded variational autoencoder 在小样本场景下使用发光嵌入变分自编码器进行软测量的虚拟样本生成

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-22 DOI: 10.1016/j.compchemeng.2024.108925

Yan Xu , Qun-Xiong Zhu , Wei Ke , Yan-Lin He , Ming-Qing Zhang , Yuan Xu

{"title":"Virtual sample generation for soft-sensing in small sample scenarios using glow-embedded variational autoencoder","authors":"Yan Xu , Qun-Xiong Zhu , Wei Ke , Yan-Lin He , Ming-Qing Zhang , Yuan Xu","doi":"10.1016/j.compchemeng.2024.108925","DOIUrl":"10.1016/j.compchemeng.2024.108925","url":null,"abstract":"<div><div>In industrial processes, limitations of the physical environment, sensors drop-out, and repetitive sampling often lead to insufficient and unevenly distributed representative instances, which greatly hinders the accuracy of soft-sensing models. This paper presents a novel virtual sample generation method based on Glow-embedded variational autoencoder (GVAE-VSG), aimed at enhancing data richness and diversity to improve the modeling performance. Specifically, GVAE-VSG embeds the Glow model from flow transformations into the variational autoencoder. This allows for the derivation of a more generalized posterior distribution without reducing sample dimensionality, thereby ensuring the generation of higher-quality virtual input samples. Subsequently, a nonlinear iterative partial least squares regression framework, incorporating a sparse constrained error matrix, is employed to generate virtual output samples that more closely resemble actual data. Finally, by a synthetic nonlinear function and an actual purification terephthalic acid (PTA) solvent system, the generative and modeling performance of the proposed method are comprehensively assessed.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108925"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743283","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

Modeling of hydrogen liquefaction process parameters using advanced artificial intelligence technique

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-22 DOI: 10.1016/j.compchemeng.2024.108950

A. Abdallah El Hadj , A. Ait Yahia , K. Hamza , M. Laidi , S. Hanini

{"title":"Modeling of hydrogen liquefaction process parameters using advanced artificial intelligence technique","authors":"A. Abdallah El Hadj , A. Ait Yahia , K. Hamza , M. Laidi , S. Hanini","doi":"10.1016/j.compchemeng.2024.108950","DOIUrl":"10.1016/j.compchemeng.2024.108950","url":null,"abstract":"<div><div>The main subject of this work is the application of advanced artificial intelligence (AI) techniques to accurately predict the parameters of the hydrogen liquefaction process. This study employs a comparative analysis of the most reliable AI techniques: Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), support vector machines (SVM), perturbed chain statistical associated fluid theory (PCSAFT) equation of state and Hybrid technique based on the combination of ANN model and perturbed chain statistical associated fluid theory (AI-PCSAFT). The training and validation strategy focuses on using a validation agreement vector, determined through linear regression analysis of the predicted versus reference outputs, as an indication of the predictive ability of the studied models. A dataset collected from scientific papers containing hydrogen liquefaction process data was utilized in the modeling process. The modeling strategy is performed using the temperature (T), pressure (P), and mass flow rate (m) as input parameters and the stream energy (E) as output parameters.</div><div>The results show high predictability of the optimized ANFIS model followed by AI-PACSAFT model compared to ANN, SVM models and PCSAFT equation of state with coefficient of correlation (R) and absolute relative deviation (AARD) equal to 0.9988 and 0.98% respectively.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108950"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136632","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

Multiscale optimization of formic acid dehydrogenation process via linear model decision tree surrogates 基于线性模型决策树的甲酸脱氢过程多尺度优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-22 DOI: 10.1016/j.compchemeng.2024.108921

Ethan M. Sunshine , Giovanna Bucci , Tanusree Chatterjee , Shyam Deo , Victoria M. Ehlinger , Wenqin Li , Thomas Moore , Corey Myers , Wenyu Sun , Bo-Xun Wang , Mengyao Yuan , John R. Kitchin , Carl D. Laird , Matthew J. McNenly , Sneha A. Akhade

{"title":"Multiscale optimization of formic acid dehydrogenation process via linear model decision tree surrogates","authors":"Ethan M. Sunshine , Giovanna Bucci , Tanusree Chatterjee , Shyam Deo , Victoria M. Ehlinger , Wenqin Li , Thomas Moore , Corey Myers , Wenyu Sun , Bo-Xun Wang , Mengyao Yuan , John R. Kitchin , Carl D. Laird , Matthew J. McNenly , Sneha A. Akhade","doi":"10.1016/j.compchemeng.2024.108921","DOIUrl":"10.1016/j.compchemeng.2024.108921","url":null,"abstract":"<div><div>Multiscale optimization problems require the interconnection of several models of distinct phenomena which occur at different scales in length or time. However, the best model for any particular phenomenon may not be amenable to rigorous optimization techniques. For instance, molecular interactions are often modeled by computational chemistry software packages that cannot be easily converted into optimization constraints. Data-driven surrogate models can overcome this problem. By choosing surrogates with functional forms that are convertible to a mixed-integer linear model, one can connect and optimize these surrogates instead of the underlying models. We demonstrate the interconnection of linear model decision trees to optimize across three scales of a formic acid dehydrogenation process. We show that optimizing across all three scales simultaneously leads to a 40% cost savings compared to optimizing each model independently. Furthermore, the surrogates retain some relevant physical behaviors and provide insights into the optimal design of this process.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108921"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747138","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

A multi-objective robust scenario-based stochastic chance constrained programming model for sustainable closed-loop agri-food supply chain 可持续闭环农业食品供应链的多目标鲁棒场景随机机会约束规划模型

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