Computers & Chemical Engineering最新文献_第8页

Integrating supervised and unsupervised learning approaches to unveil critical process inputs 整合监督和非监督学习方法，揭示关键流程输入

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-09-03 DOI: 10.1016/j.compchemeng.2024.108857

Paris Papavasileiou , Dimitrios G. Giovanis , Gabriele Pozzetti , Martin Kathrein , Christoph Czettl , Ioannis G. Kevrekidis , Andreas G. Boudouvis , Stéphane P.A. Bordas , Eleni D. Koronaki

{"title":"Integrating supervised and unsupervised learning approaches to unveil critical process inputs","authors":"Paris Papavasileiou , Dimitrios G. Giovanis , Gabriele Pozzetti , Martin Kathrein , Christoph Czettl , Ioannis G. Kevrekidis , Andreas G. Boudouvis , Stéphane P.A. Bordas , Eleni D. Koronaki","doi":"10.1016/j.compchemeng.2024.108857","DOIUrl":"10.1016/j.compchemeng.2024.108857","url":null,"abstract":"<div><p>This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters that influence the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective involves merging subject matter expertise and clustering techniques exclusively on the process output, here, coating thickness measurements at various positions in the reactor. This approach identifies groups of production runs that share similar qualitative characteristics, such as film mean thickness and standard deviation. In particular, the differences of the outcomes represented by the different clusters can be attributed to differences in specific inputs, indicating that these inputs are potentially critical to the production outcome. Shapley value analysis corroborates the formed hypotheses. Leveraging this insight, we subsequently implement supervised classification and regression methods using the identified critical process inputs. The proposed methodology proves to be valuable in scenarios with a multitude of inputs and insufficient data for the direct application of deep learning techniques, providing meaningful insights into the underlying processes.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108857"},"PeriodicalIF":3.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424002758/pdfft?md5=5b97cf2052fa37b1ae7b0760796c20ca&pid=1-s2.0-S0098135424002758-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158021","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

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification BO4IO：采用贝叶斯优化方法进行不确定性量化的逆向优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-09-02 DOI: 10.1016/j.compchemeng.2024.108859

Yen-An Lu , Wei-Shou Hu , Joel A. Paulson , Qi Zhang

{"title":"BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification","authors":"Yen-An Lu , Wei-Shou Hu , Joel A. Paulson , Qi Zhang","doi":"10.1016/j.compchemeng.2024.108859","DOIUrl":"10.1016/j.compchemeng.2024.108859","url":null,"abstract":"<div><p>Data-driven inverse optimization (IO) aims to estimate unknown parameters in an optimization model from observed decisions. The IO problem is commonly formulated as a large-scale bilevel program that is notoriously difficult to solve. We propose a derivative-free optimization approach based on Bayesian optimization, BO4IO, to solve general IO problems. The main advantages of BO4IO are two-fold: (i) it circumvents the need of complex reformulations or specialized algorithms and can hence enable computational tractability even when the underlying optimization problem is nonconvex or involves discrete variables, and (ii) it allows approximations of the profile likelihood, which provide uncertainty quantification on the IO parameter estimates. Our extensive computational results demonstrate the efficacy and robustness of BO4IO to estimate unknown parameters from small and noisy datasets. In addition, the proposed profile likelihood analysis effectively provides good approximations of the confidence intervals on the parameter estimates and assesses the identifiability of the unknown parameters.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108859"},"PeriodicalIF":3.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424002771/pdfft?md5=2b1fcb630ba3141652b16ea5b79fc168&pid=1-s2.0-S0098135424002771-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158020","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

Integrating smart manufacturing techniques into undergraduate education: A case study with heat exchanger 将智能制造技术融入本科教育：热交换器案例研究

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-08-31 DOI: 10.1016/j.compchemeng.2024.108858

Mrunal Sontakke , Lucky E. Yerimah , Andreas Rebmann , Sambit Ghosh , Craig Dory , Ronald Hedden , B. Wayne Bequette

{"title":"Integrating smart manufacturing techniques into undergraduate education: A case study with heat exchanger","authors":"Mrunal Sontakke , Lucky E. Yerimah , Andreas Rebmann , Sambit Ghosh , Craig Dory , Ronald Hedden , B. Wayne Bequette","doi":"10.1016/j.compchemeng.2024.108858","DOIUrl":"10.1016/j.compchemeng.2024.108858","url":null,"abstract":"<div><p>The process systems domain is undergoing the fourth industrial revolution, which is helping industries digitize and optimize their production techniques. Concurrently, the field of data-based modeling has been expanding, leading to the proposal of many fault detection models. However, the rapid expansion has created gaps in the field. For instance, Smart Manufacturing (SM) methodologies have yet to be incorporated into undergraduate chemical engineering education. Additionally, only a few developed fault detection models have been deployed for real-time usage and practical applications. This study takes a crucial step toward bridging the two mentioned gaps by enabling undergraduate students to learn SM techniques and developing a safe and controlled academic environment for deploying fault detection models. The demonstration is implemented on a shell and tube heat exchanger, taught in a senior year laboratory course, using the Smart Manufacturing Innovation Platform (SMIP). The implementation provides an easily customizable pipeline for SM applications involving human-in-the-loop decision-making on a real-life hardware system. Actual data from heat exchanger equipment is used to train and compare the performances of several state-of-the-art fault detection models, including fully connected, convolutional, and recurrent neural networks. Current work also presents tutorials on deploying models for practical real-time applications using the SMIP. The overall architecture is a plug-and-play package that will motivate students to learn about SM and catalyze their interest in developing and deploying fault detection models using real-world data.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"191 ","pages":"Article 108858"},"PeriodicalIF":3.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148362","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

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