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Multilevel on-the-fly sparse grids for coupling coarse-grained and high fidelity models in heterogeneous catalysis 多相催化中粗粒度高保真模型耦合的多层动态稀疏网格
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
{"title":"Multilevel on-the-fly sparse grids for coupling coarse-grained and high fidelity models in heterogeneous catalysis","authors":"Tobias Hülser ,&nbsp;Bjarne Kreitz ,&nbsp;C. Franklin Goldsmith ,&nbsp;Sebastian Matera","doi":"10.1016/j.compchemeng.2024.108922","DOIUrl":"10.1016/j.compchemeng.2024.108922","url":null,"abstract":"<div><div>Coupling microscopic high-fidelity models, such as microkinetic models, into continuum scale simulations can easily become intractable in practice due to the costs of the high-fidelity model evaluation. To lift this burden, we present a novel multilevel self-consistent on-the-fly sparse grid approach, which integrates the construction of surrogates of the high-fidelity model in a multilevel fashion into the continuum solution process. Besides its efficiency, an appealing feature of the approach is its simplicity and robustness. A single hyperparameter controls the whole workflow, from training set design to the accuracy of the reactor model. We demonstrate the methodology on a recent microkinetic model for catalytic combustion in a fixed-bed reactor model as a representative example. Already with modest numbers of data, the approach achieves sufficient accuracy, reducing the effort by orders of magnitude compared to a direct coupling.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108922"},"PeriodicalIF":3.9,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747017","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 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
{"title":"A semi-supervised learning algorithm for high and low-frequency variable imbalances in industrial data","authors":"Jiannan Zhu ,&nbsp;Chen Fan ,&nbsp;Minglei Yang ,&nbsp;Feng Qian ,&nbsp;Vladimir Mahalec","doi":"10.1016/j.compchemeng.2024.108933","DOIUrl":"10.1016/j.compchemeng.2024.108933","url":null,"abstract":"<div><div>This work introduces a semi-supervised learning algorithm to estimate missing data for processes where measured data is comprised of variables that are measured at high frequency and low frequency. A semi-supervised learning algorithm named “Weight-Adjusted Consistency Regularization Algorithm for Semi-Supervised Learning” (WACR-SSL) based on consistency regularization is proposed. The algorithm splits the irregular unbalanced data set into three parts and processes them separately. To address the loss balancing problem, five loss balancing methods have been tested: Uncertainty Weights (UW), Random Loss Weighting (RLW), Dynamic Weight Average (DWA), Geometric Loss Strategy (GLS) and the logarithmic transformation (LogT). When applied to data from a hydrocracking process, the algorithm effectively leverages partially labeled data. With carefully chosen noise scales and the coefficient for the unsupervised loss, the uncertainty weight (UW) variant performs the best when compared to the other loss balancing methods.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108933"},"PeriodicalIF":3.9,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722607","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
Extracting key temporal and cyclic features from VIT data to predict lithium-ion battery knee points using attention mechanisms 从VIT数据中提取关键的时间和循环特征，利用注意力机制预测锂离子电池的膝点
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 ,&nbsp;Seongmin Heo ,&nbsp;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
{"title":"Digital twins in process engineering: An overview on computational and numerical methods","authors":"Luisa Peterson ,&nbsp;Ion Victor Gosea ,&nbsp;Peter Benner ,&nbsp;Kai Sundmacher","doi":"10.1016/j.compchemeng.2024.108917","DOIUrl":"10.1016/j.compchemeng.2024.108917","url":null,"abstract":"<div><div>A digital twin (DT) is an automation strategy that combines a physical plant with an adaptive real-time simulation environment, where both are connected by bidirectional communication. In process engineering, DTs promise real-time monitoring, prediction of future conditions, predictive maintenance, process optimization, and control. However, the full implementation of DTs often fails in reality. To address this issue, we first examine various definitions of DTs and its core components, followed by a review of its current applications in process engineering. We then turn to the computational and numerical challenges for building the simulation environments necessary for successful DTs implementation</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108917"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722673","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
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 ,&nbsp;Antonio Flores-Tlacuahuac ,&nbsp;Alexander Panales-Pérez ,&nbsp;Tania E. Lara-Ceniceros ,&nbsp;Manuel Alejandro Ávila-López ,&nbsp;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 在工业4.0中推进机器学习：化学过程中罕见事件预测的基准框架
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,&nbsp;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
{"title":"Methodology for hyperparameter tuning of deep neural networks for efficient and accurate molecular property prediction","authors":"Xuan Dung James Nguyen,&nbsp;Y.A. Liu","doi":"10.1016/j.compchemeng.2024.108928","DOIUrl":"10.1016/j.compchemeng.2024.108928","url":null,"abstract":"<div><div>This paper presents a methodology of hyperparameter optimization (HPO) of deep neural networks for molecular property prediction (MPP). Most prior applications of deep learning to MPP have paid only limited attention to HPO, thus resulting in suboptimal values of predicted properties. To improve the efficiency and accuracy of deep learning models for MPP, we must optimize as many hyperparameters as possible and choose a software platform to enable the parallel execution of HPO. We compare the random search, Bayesian optimization, and hyperband algorithms, together with the Bayesian-hyperband combination within the software packages of KerasTuner and Optuna for HPO. We conclude that the hyperband algorithm, which has not been used in previous MPP studies, is most computationally efficient; it gives MPP results that are optimal or nearly optimal in terms of prediction accuracy. Based on our case studies, we recommend the use of the Python library KerasTuner for HPO.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108928"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722671","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
Data-driven robust optimization for pipeline scheduling under flow rate uncertainty 流量不确定情况下管道调度的数据驱动稳健优化
IF 3.9 2区 工程技术
Computers & Chemical Engineering Pub Date : 2024-11-14 DOI: 10.1016/j.compchemeng.2024.108924
Amir Baghban , Pedro M. Castro , Fabricio Oliveira
{"title":"Data-driven robust optimization for pipeline scheduling under flow rate uncertainty","authors":"Amir Baghban ,&nbsp;Pedro M. Castro ,&nbsp;Fabricio Oliveira","doi":"10.1016/j.compchemeng.2024.108924","DOIUrl":"10.1016/j.compchemeng.2024.108924","url":null,"abstract":"<div><div>Frequently, parameters in optimization models are subject to a high level of uncertainty coming from several sources and, as such, assuming them to be deterministic can lead to solutions that are infeasible in practice. Robust optimization is a computationally efficient approach that generates solutions that are feasible for realizations of uncertain parameters near the nominal value. This paper develops a data-driven robust optimization approach for the scheduling of a straight pipeline connecting a single refinery with multiple distribution centers, considering uncertainty in the injection rate. For that, we apply support vector clustering to learn an uncertainty set for the robust version of the deterministic model. We compare the performance of our proposed robust model against one utilizing a standard robust optimization approach and conclude that data-driven robust solutions are less conservative.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108924"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722674","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
The bullwhip effect, market competition and standard deviation ratio in two parallel supply chains 两条平行供应链中的牛鞭效应、市场竞争和标准偏差率
IF 3.9 2区 工程技术
Computers & Chemical Engineering Pub Date : 2024-11-12 DOI: 10.1016/j.compchemeng.2024.108916
Xuluo Yin, Wenting Tang
{"title":"The bullwhip effect, market competition and standard deviation ratio in two parallel supply chains","authors":"Xuluo Yin,&nbsp;Wenting Tang","doi":"10.1016/j.compchemeng.2024.108916","DOIUrl":"10.1016/j.compchemeng.2024.108916","url":null,"abstract":"<div><div>The bullwhip effect widely exists in supply chains and shows its significance for the competitiveness of enterprises in supply chains. In this study, we analyze the bullwhip effect in two parallel supply chains with competing products, each one consisting of a supplier and a retailer. A model is detailed for measuring the bullwhip effect in which the demand of retailers follows a similar vector autoregressive model (VAR-like) process. The results show that the bullwhip effect can be characterized as a quadratic function of the standard deviation ratio. The impact of market competition on the bullwhip effect of the supply chain may have the opposite result, which depends on some parameters, including lead time and market competition in the parallel supply chain. The parameters have asymmetric influence on bullwhip effect. Compared with VAR(1) and AR(1) model, the empirical results show that our VAR(1)-like model is closer to reality. Furthermore, we discuss the conclusion of research and its inspiration for supply chain management.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108916"},"PeriodicalIF":3.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663979","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
CADET-Julia: Efficient and versatile, open-source simulator for batch chromatography in Julia CADET-Julia：高效、通用、开源的 Julia 批次色谱模拟器
IF 3.9 2区 工程技术
Computers & Chemical Engineering Pub Date : 2024-11-12 DOI: 10.1016/j.compchemeng.2024.108913
Jesper Frandsen , Jan Michael Breuer , Johannes Schmölder , Jakob Kjøbsted Huusom , Krist V. Gernaey , Jens Abildskov , Eric von Lieres
{"title":"CADET-Julia: Efficient and versatile, open-source simulator for batch chromatography in Julia","authors":"Jesper Frandsen ,&nbsp;Jan Michael Breuer ,&nbsp;Johannes Schmölder ,&nbsp;Jakob Kjøbsted Huusom ,&nbsp;Krist V. Gernaey ,&nbsp;Jens Abildskov ,&nbsp;Eric von Lieres","doi":"10.1016/j.compchemeng.2024.108913","DOIUrl":"10.1016/j.compchemeng.2024.108913","url":null,"abstract":"<div><div>This study introduces CADET-Julia, an open-source, versatile and fast chromatography solver implemented in the Julia programming language. The software offers a platform for rapid prototyping and numerical refinement for a range of chromatography models, including the general rate model (GRM). The interstitial column mass balance was spatially discretized using a strong-form discontinuous Galerkin spectral element method (DGSEM) whereas a generalized spatial Galerkin spectral method (GSM) was applied for the particle mass balance. Three different benchmarks showcased the computational efficiency of CADET-Julia: A baseline benchmark was established by comparing the Julia implementation to a C++ implementation that employed the same mathematical methods and time integrator (CADET-DG). Various Julia time integrators were tested, and with the best-performing settings, the Julia implementation was benchmarked against CADET-DG and a finite volume (FV) based implementation in C++ (CADET-FV). Overall, Julia implementations performed better than C++ implementations and Galerkin methods were generally superior to finite volumes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108913"},"PeriodicalIF":3.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663980","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
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