Computers & Chemical Engineering最新文献_第6页

Semi-supervised regression based on Representation Learning for fermentation processes 基于表征学习的发酵过程半监督回归

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-30 DOI: 10.1016/j.compchemeng.2024.108856

Jing Liu , Junxian Wang , Jianye Xia , Fengfeng Lv , Dawei Wu

引用次数: 0

On speeding-up modifier-adaptation schemes for real-time optimization 关于加快实时优化的修改器适应方案

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-28 DOI: 10.1016/j.compchemeng.2024.108839

Dominique Bonvin , Gabriele Pannocchia

{"title":"On speeding-up modifier-adaptation schemes for real-time optimization","authors":"Dominique Bonvin , Gabriele Pannocchia","doi":"10.1016/j.compchemeng.2024.108839","DOIUrl":"10.1016/j.compchemeng.2024.108839","url":null,"abstract":"<div><p>The real-time optimization scheme “modifier adaptation” (MA) has been developed to enforce steady-state plant optimality in the presence of model uncertainty. The key feature of MA is its ability to locally modify the model by adding bias and gradient correction terms to the cost and constraint functions or, alternatively, to the outputs. Since these correction terms are static in nature, their computation may require a significant amount of time, especially with slow processes. This paper presents two ways of speeding-up MA schemes for real-time optimization. The first approach proposes to estimate the modifiers from steady-state data via a tailored recursive least-squares scheme. The second approach investigates the estimation of static correction terms during transient operation. The idea is to first develop a calibration model to express the static plant-model mismatch as a function of inputs only. This calibration model can be generated via a single MA run that successively visits various steady states before reaching plant optimality. In addition, to account for process differences between calibration and subsequent operation, bias terms are estimated online from output measurements. Implementation and performance aspects are compared on two pedagogical examples, namely, an unconstrained nonlinear SISO plant and a constrained multivariable CSTR example.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108839"},"PeriodicalIF":3.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424002576/pdfft?md5=5dfae87f49b8531f4dc17c403f0a1dab&pid=1-s2.0-S0098135424002576-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088990","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

Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes 基于机器学习的输入增强型库普曼建模和非线性过程的预测控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-24 DOI: 10.1016/j.compchemeng.2024.108854

Zhaoyang Li , Minghao Han , Dat-Nguyen Vo , Xunyuan Yin

{"title":"Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes","authors":"Zhaoyang Li , Minghao Han , Dat-Nguyen Vo , Xunyuan Yin","doi":"10.1016/j.compchemeng.2024.108854","DOIUrl":"10.1016/j.compchemeng.2024.108854","url":null,"abstract":"<div><p>Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly depends on an appropriate nonlinear mapping from the original state-space to a lifted state space. In this work, we propose an input-augmented Koopman modeling and model predictive control approach. Both the states and the known inputs are lifted using two deep neural networks (DNNs), and a Koopman model with nonlinearity in inputs is trained within the higher-dimensional state space. A Koopman-based model predictive control problem is formulated. To bypass non-convex optimization induced by the nonlinearity in the Koopman model, we further present an iterative implementation algorithm, which approximates the optimal control input via solving a convex optimization problem iteratively. The proposed method is applied to a chemical process and a biological water treatment process via simulations. The efficacy and advantages of the proposed modeling and control approach are demonstrated.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108854"},"PeriodicalIF":3.9,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142075985","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

Resilience-based explainable reinforcement learning in chemical process safety 化学过程安全中基于复原力的可解释强化学习

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-24 DOI: 10.1016/j.compchemeng.2024.108849

Kinga Szatmári , Gergely Horváth , Sándor Németh , Wenshuai Bai , Alex Kummer

引用次数: 0

EPMITS: An efficient prediction method incorporating trends and shapes features for chemical process variables EPMITS：包含化学过程变量趋势和形状特征的高效预测方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-23 DOI: 10.1016/j.compchemeng.2024.108855

Yiming Bai , Huawei Ye , Jinsong Zhao

{"title":"EPMITS: An efficient prediction method incorporating trends and shapes features for chemical process variables","authors":"Yiming Bai , Huawei Ye , Jinsong Zhao","doi":"10.1016/j.compchemeng.2024.108855","DOIUrl":"10.1016/j.compchemeng.2024.108855","url":null,"abstract":"<div><p>With the transformation of industrial production digitization and automation, process monitoring has been an indispensable technical method to realize the safe and efficient production of chemical process. Accurate prediction of process variables in chemical process can indicate the possible system change to reduce the probability of abnormal conditions. Current popular deep learning prediction methods trained with MSE or its variants may exhibit limitations in extracting shape features of chemical process data. In this paper, we proposed an efficient prediction method incorporating trends and shapes features (EPMITS) for chemical process variables. Specifically, we introduced a novel differentiable loss function Efficient Shape Error (ESE) to quantify shape differences between two time series of equal length in chemical process data. Then we trained deep learning models with MSE and ESE as loss function by two steps in training stage, to effectively acquire both trend and shape features of chemical process data. The proposed method was evaluated by the Tennessee Eastman process datasets and a real fluid catalytic cracking dataset from a petrochemical company. The results indicate that EPMITS models exhibit high prediction accuracy and short model training time across various time scales. These findings demonstrate the considerable feasibility and significant potential of EPMITS for future fault prognosis applications.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108855"},"PeriodicalIF":3.9,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099313","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 Gaussian process embedded feature selection method based on automatic relevance determination 基于自动相关性确定的高斯过程嵌入式特征选择方法

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-23 DOI: 10.1016/j.compchemeng.2024.108852

Yushi Deng, Mario Eden, Selen Cremaschi

引用次数: 0

A decision support system for cooling tower technologies evaluation in the oil and gas industry 石油天然气行业冷却塔技术评估决策支持系统

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-23 DOI: 10.1016/j.compchemeng.2024.108853

Abdolvahhab Fetanat , Mohsen Tayebi

引用次数: 0

Decoupling framework for large-scale energy systems simultaneously addressing carbon emissions and energy flow relationships through sector units: A case study on uncertainty in China's carbon emission targets 大规模能源系统的解耦框架，通过部门单元同时处理碳排放和能源流关系：中国碳排放目标不确定性案例研究

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-22 DOI: 10.1016/j.compchemeng.2024.108840

Chenxi Li , Nilay Shah , Zheng Li , Pei Liu

{"title":"Decoupling framework for large-scale energy systems simultaneously addressing carbon emissions and energy flow relationships through sector units: A case study on uncertainty in China's carbon emission targets","authors":"Chenxi Li , Nilay Shah , Zheng Li , Pei Liu","doi":"10.1016/j.compchemeng.2024.108840","DOIUrl":"10.1016/j.compchemeng.2024.108840","url":null,"abstract":"<div><p>The energy system requires meticulous planning to achieve low-carbon development goals cost-effectively. However, optimizing large-scale energy systems with high spatial-temporal resolution and a rich variety of technologies has always been a challenge due to limited computational resources. Therefore, this study proposes a soft-linkage framework to deconstruct large-scale energy system optimization models based on sectors while ensuring the total carbon emission limit and the electricity supply-demand balance. Using China's energy system as a case study, the impact of uncertainty on emission reduction targets is analyzed. A long-term emission target curve is only described by the total carbon budget and its temporal distribution. Results show that different carbon budget time series can lead to total transition cost variations of up to nearly 100 trillion yuan. Moreover, although a lower carbon budget would increase the total cumulative transition cost quadratically, excessively high carbon budgets raise national natural gas demand, threatening energy security.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108840"},"PeriodicalIF":3.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049599","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 and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model 基于混合代用优化模型的连续催化重整工艺建模与优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-22 DOI: 10.1016/j.compchemeng.2024.108841

Xiang C. Ma, Chang He, Qing L. Chen, Bing J. Zhang

{"title":"Modeling and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model","authors":"Xiang C. Ma, Chang He, Qing L. Chen, Bing J. Zhang","doi":"10.1016/j.compchemeng.2024.108841","DOIUrl":"10.1016/j.compchemeng.2024.108841","url":null,"abstract":"<div><p>To address the modeling and optimization challenges of the complex reaction system in the continuous catalytic reforming process, a new integrated simulation and optimization framework is presented. First, a detailed mechanism model is established based on a reaction network involving 32 components and 50 reactions, coupled with mass transfer, heat transfer, pressure drop, and catalyst deactivation equations. Then, to solve the differential-algebraic equations in the mechanism model, a multi-objective hybrid optimization method with the adaptive infill strategy is introduced. GAMS and MATLAB are integrated to perform a joint iterative solution. Finally, two cases are conducted with the proposed algorithm. Results show that the mechanism model calculation deviations are below 4 % of reactor temperature, pressure, and composition distribution, and the Pareto front of various production plans is obtained. The accurate simulation and rapid trade-off optimization among the key goals can be achieved to provide scientific decision support for enterprise production.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108841"},"PeriodicalIF":3.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058428","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

Optimization of sustainable supply chain for bio-based isopropanol production from sugar beet using techno-economic and life cycle analysis 利用技术经济分析和生命周期分析优化以甜菜为原料生产生物基异丙醇的可持续供应链

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-22 DOI: 10.1016/j.compchemeng.2024.108836

Ching-Mei Wen, Marianthi Ierapetritou

{"title":"Optimization of sustainable supply chain for bio-based isopropanol production from sugar beet using techno-economic and life cycle analysis","authors":"Ching-Mei Wen, Marianthi Ierapetritou","doi":"10.1016/j.compchemeng.2024.108836","DOIUrl":"10.1016/j.compchemeng.2024.108836","url":null,"abstract":"<div><p>This study examines the techno-economic and life cycle analysis of bio-based isopropanol (IPA) production from sugar beet, utilizing a Geographical Information System (GIS)-enabled framework. By focusing on the innovative IPA production technology, the research demonstrates the economic and environmental feasibility of converting first-generation biomass into sustainable chemicals through the optimization of the Sugar Beet-to-Isopropanol supply chain. Findings highlight a cost-optimal production capacity of 55,800 mt/year with significant potential for reducing emissions and operational costs. The production cost of bio-IPA is potentially 70 % less than the fossil-derived IPA price. Additionally, the potential profits from bio-based IPA are estimated to be nearly double the market price of its primary raw material, sugar, demonstrating the economic feasibility of converting the first-generation biomass for sustainable IPA production. The study also explores the impact of facility clustering on transportation emissions and costs, revealing strategic approaches to expanding plant capacities in response to increasing demand. This research provides insights for designing sustainable industrial practices using first-generation biomass in the chemical industry.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108836"},"PeriodicalIF":3.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142075986","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