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multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis

NEUROCOMPUTING 2018年 293卷 64-86页

作者： Zhang, Hanyuan Tian, Xuemin Deng, Xiaogang Cao, Yuping Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Shangdong Peoples R China

Most previous studies have shown that the multiphase characteristics of batch processes are critical for process monitoring;however, revealing and utilizing the information of multiplicity in multiphase batch process monitoring remains challenging. In this paper, statistics slow feature analysis (SSFA) is developed to extract slowly varying information at the same sampling time among different batches based on various statistics of the original variables. Then, a new index called the phase recognition factor (PRF), which is based on SSFA, is introduced to automatically achieve phase division. The proposed PRF takes advantage of the time sequence of process phases and does not require predefined parameters. After phase division, global preserving SSFA (GSSFA) is proposed not only to explore the time-varying dynamic information of batch processes but also to consider the mining of global data structure information. Furthermore, a novel process monitoring strategy based on the GSSFA model is developed to monitor batch processes with transitions. In each steady phase, a representative GSSFA model is built for process monitoring;in the transition period, different local GSSFA models are constructed online to monitor the test samples based on the just-in-time learning method. Two case studies, one simple numerical multiphase system and a benchmark fed-batch penicillin fermentation process, are used to demonstrate the effectiveness and superiority of the proposed method. (c) 2018 Elsevier B.V. All rights reserved.

关键词： multiphase batch process Transition Phase division Slow feature analysis process monitoring

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An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第11期46卷 2118-2128页

作者： Zhu, Jikun Xiong, Weili Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China

For multiphase batch process, the characteristics of process data under various batches differ. Consequently, the soft sensor model built for a particular working condition is inapplicable to other working conditions. Besides, each batch can be divided into several phases whose characteristics are probably different. To address the above problems, a soft sensor model based on phase division and transfer learning strategy is proposed. First, transfer learning strategy is adopted to construct a soft sensor model applicable to various working conditions. Specifically, geodesic flow kernel based on linear local tangent space alignment (LLTSA-GFK) algorithm is designed. By projecting process data to the common manifold subspace, the distribution difference of data between various batches is reduced and the performance of the soft sensor model is enhanced. In addition, sequence-based fuzzy clustering and just-in-time learning (JITL) are adopted to solve the multistage characteristic for batch process. The root-mean-square error (RMSE), coefficient of determination ( R 2 ) , and mean absolute error (MAE) are adopted to compare the conventional soft sensing approach (i.e., partial least-square regression based on JITL, support vector regression, and back propagation neural network) with the proposed approach. The superiority of the proposed model is verified by a fed-batch penicillin fermentation process.

关键词： Transfer learning geodesic flow kernel multiphase batch process multiple working conditions soft sensor

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Phase division and transition modeling based on the dominant phase identification for multiphase batch process quality prediction

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第5期42卷 1022-1036页

作者： Tang, Xiaochu Li, Yuan Shenyang Aerosp Univ Sch Automat Shenyang 110136 Liaoning Peoples R China Shenyang Univ Chem Technol Coll Informat Engn Shenyang Liaoning Peoples R China

batch processes are carried out from one steady phase to another one, which may have multiphase and transitions. Modeling in transitions besides in the steady phases should also be taken into consideration for quality prediction. In this paper, a quality prediction strategy is proposed for multiphase batch processes. First, a new repeatability factor is introduced to divide batch process into different steady phases and transitions. Then, the different local cumulative models that considered the cumulative effect of process variables on quality are established for steady phases and transitions. Compared with the reported modeling methods in transitions, a novel just-in-time model can be established based on the dominant phase identification. The proposed method can not only consider the dynamic characteristic in the transition but also improve the accuracy and the efficiency of transitional models. Finally, online quality prediction is performed by accumulating the prediction results from different phases and transitions. The effectiveness of the proposed method is demonstrated by penicillin fermentation process.

关键词： multiphase batch process quality prediction phase division transition modeling dominant phase identification

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Fault Detection for multiphase batch process 42

Fault Detection for Multiphase Batch Process

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42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Zhang, Yingwei Zhai, Lirong Xie, Ying Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Liaoning Univ Coll Light Ind Shenyang 110036 Liaoning Peoples R China Shenyang City Univ Coll Mech & Elect Engn Shenyang 110112 Liaoning Peoples R China

ISBN: (纸本)9781509034741

In this paper, a fault detection method based on intermediate phase dependency analysis for multiphase batch process is proposed. The contributions of the proposed method are as follows: 1) multi-way kernel independent component analysis (MKICA) are used to extract the principal information;2) the subspace separation is completed by establishing the relationship of intermediate phase using MKICA;after that, the relative changes (increased part, decreased part and unchanged part) from previous phase to latter phase are obtained;3) the detection model establishing by the proposed approach is more accurately comparing with the conventional method. The advantage and effectiveness of the proposed method are illustrated with penicillin fermentation process.

关键词： Intermediate Phase Dependency Analysis Multi-way Kernel Independent Component Analysis (MKICA) Fault Detection multiphase batch process

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Real-time synchronization with expected distribution of synchronized index for on-line monitoring of uneven multiphase batch process

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COMPUTERS & CHEMICAL ENGINEERING 2021年 154卷

作者： Lee, Hye Ji Bae, Jaehan Jeong, Dong Hwi Lee, Jong Min Seoul Natl Univ Sch Chem & Biol Engn Inst Chem Proc 1 Gwanak Ro Seoul 08826 South Korea Univ Ulsan Sch Chem Engn 93 Daehak Ro Ulsan 44610 South Korea

Because of the inconsistent batch duration, identifying the progress of an ongoing batch highly affects the performance of the on-line monitoring. In this paper, a real-time synchronization scheme is proposed based on relative proceeding rates, newly defined as the time difference between the trajectory of an ongoing batch and the nominal trajectory. As the relative proceeding rate indicates how early the point attains its desired value compared to the nominal trajectory, the synchronized index is obtained online by estimating the relative proceeding rate. Compared with the conventional methods that optimize the synchronized index with local constraints, the proposed method can estimate its distribution using the relative proceeding rate without applying local constraints. It can reduce synchronization errors and increase the accuracies of the state estimation and fault detection. The utility of the proposed method is verified via a case study of industrial penicillin production with a real-time unmeasurable variable. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： On-line monitoring multiphase batch process On-line synchronization Fault detection Multivariate statistical process control Penicillin fermentation

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Between-Phase Nonlinear Correlation Analysis-Based Modeling and Online Monitoring for multiphase batch process With Transitions

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IEEE ACCESS 2019年 7卷 158951-158968页

作者： Tang, Xiaochu Li, Yuan Shenyang Aerosp Univ Coll Automat Shenyang 110136 Liaoning Peoples R China Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110142 Liaoning Peoples R China

In order to provide better monitoring performance, between-phase nonlinear correlations and differences should be considered and separately monitored in multiphase batch processes. However, it is a more challenging task for batch processes to extract the nonlinear correlation information because the large-size samples may bring the computational complexity and high-dimensional kernel instability. To address the above issue, a feature vector selection with kernel vector correlation analysis (FVS-KVCA) method is developed for batch processes. In this paper, a novel two-level phase division method is firstly proposed to divide batch processes into the steady phases and the transitions. Then, the local phase models are constructed based on the FVS-KVCA method to separate process information into the common information and the specific information, which represent the nonlinear correlation between two neighboring phases and within only one phase, respectively. Based on such an information separation, the transition can be further divided into the transitional phases and the mixing phases by the second-level division. Also, a dynamic transition modeling method is introduced to solve the transition monitoring problem. Finally, online process monitoring can be conducted based on the defined score features to select the right model. The proposed algorithm is applied to the penicillin fermentation process to illustrate the effectiveness and feasibility.

关键词： batch production systems Correlation Feature extraction Kernel Indexes multiphase batch process nonlinear correlation analysis transition modeling two-level phase division

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A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty

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CHEMICAL ENGINEERING SCIENCE 2013年 93卷 96-109页

作者： Yu, Jie Chen, Kuilin Rashid, Mudassir M. McMaster Univ Dept Chem Engn Hamilton ON L8S 4L7 Canada

batch processes are characterized by inherent nonlinearity, multiplicity of operating phases, between-phase transient dynamics and batch-to-batch uncertainty that pose significant challenges for accurate state estimation and quality prediction. Conventional multi-model strategies, however, may be ill-suited for multiphase batch processes because the localized models do not specially take into account the complex transient dynamics between two consecutive operating phases. In this study, a novel Bayesian model averaging based multi-kernel Gaussian process regression (BMA-MKGPR) approach is proposed for state estimation and quality prediction of nonlinear batch processes with multiple operating phases and between-phase transient dynamics. A kernel mixture model strategy is first used to identify the different operating phases of batch processes and then the multi-kernel GPR models are built for all the identified phases. Further, the between-phase transitional stage is determined by the posterior probabilities of measurement samples with respect to the two consecutive phases so that the Bayesian model averaging strategy can be designed to incorporate the two localized GPR models for handling the between-phase transient dynamics. For an arbitrary test sample within the transitional stage, its posterior probabilities with respect to the local models corresponding to the two consecutive phases are set as the adaptive weightings to integrate the corresponding local GPR models for state estimation and quality prediction. The proposed BMA-MKGPR approach is applied to a multiphase batch polymerization process and the result comparison demonstrates that the presented method can effectively handle multiple nonlinear operating phases, between-phase transient dynamics and process uncertainty with fairly high prediction accuracies. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Nonlinear state estimation Quality prediction multiphase batch process Between-phase transient dynamics Multi-kernel Gaussian process regression Bayesian model averaging

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Phase division and process monitoring for multiphase batch processes with transitions

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 145卷 72-83页

作者： Tang, Xiaochu Li, Yuan Xie, Zhi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Provin Peoples R China Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110042 Liaoning Provin Peoples R China

In this paper, a new repeatability factor is introduced to achieve phase division for multiphase batch processes. Then a two-step feature vector selection based kernel variable correlation analysis (TSFVS-KVCA) method is proposed for batch processes with transitions monitoring. The TSFVS-KVCA method not only considers the within-phase nonlinear variable correlation but also between-phase one by extracting the common bases and the specific bases between two neighboring phases. The TSFVS method first selects feature vectors from each steady phase as within-phase subsets. Then between-phase subsets are selected from the within-phase subsets of two neighboring phases. These selected feature vectors have well capacity of description on original data so that they can substitute for original data in a feature space F. Thus the TSFVS method reduces the computational complexity and the instability of high-dimensional kernel in subsequent KVCA method. Moreover, each within-phase subset can be used as sub-bases in a feature space F, which simplify the objective function of KVCA method. In this way, the common bases can be extracted in an easier way. Based on the common bases, two neighboring steady phases can be separated into the common subspace and the specific subspace, in which nonlinear process monitoring method is carried out. Furthermore, an online monitoring method for transitions is proposed based on a just-in-time model. The model can be described by the common bases of two neighboring phases and specific bases of the dominant phase at current time interval. The dominant phase can be dynamically determined according to the correlation between current transition sample and neighboring phases. The results of simulation demonstrate effectiveness and superiority of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

关键词： multiphase batch process Phase division process monitoring Transition

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Inter-batch-evolution-traced process monitoring based on inter-batch mode division for multiphase batch processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 138卷 178-192页

作者： Zhao, Luping Zhao, Chunhui Gao, Furong Zhejiang Univ State Key Lab Ind Control Technol Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Fok Ying Tung Grad Sch Hong Kong Hong Kong Peoples R China

The multiplicity of operation phases is an inherent nature of many batch processes and each phase exhibits significantly different behaviors. Besides process variation along the time direction within each batch, process variation along the batch direction, called inter-batch evolution here, widely exists in batch processes. Inter-batch evolution has caused different operation modes along the batch direction, which may not be well captured by a single model. In this work, the inter-batch evolution is tracked, based on which multiple modes are separated and modeled for online process monitoring along the batch direction. First, a batch cycle is divided into multiple phases. Then sliding windows are constructed for analysis of the inter-batch evolution within each phase. Based on the comparison between reference windows and sliding windows, different process modes are separated in order along the batch direction. Four monitoring subspaces which cover different types of relative variations are used in monitoring. During online monitoring, these different variations are then supervised respectively to trace the inter-batch evolution. The application to a typical multiphase batch process with inter-batch evolution, injection molding start-up process, illustrates the feasibility and performance of the proposed algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Inter-batch evolution process monitoring Sliding window multiphase batch process

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Intra-batch Evolution Based process Monitoring for multiphase batch processes

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IFAC Proceedings Volumes 2014年第3期47卷 6680-6685页

作者： Zhao Luping Zhao Chunhui Gao Furong Department of Chemical and Biomolecular Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University Hangzhou 310027 China (corresponding author Fok Ying Tung Graduate School The Hong Kong University of Science and Technology Hong Kong China

batch-wise variations, called intra-batch evolution here, widely exist in batch processes. In this paper, intra-batch evolution is tracked and monitored for multiphase batch processes. First, a batch cycle is divided into multiple phases. Within each phase, sliding windows are constructed for analysis of intra-batch relative variations, based on which different process modes are separated in order along batch direction. Meanwhile, the part of variations with significant increases in new modes is separated from the other. Consequently, the original two monitoring subspaces are further divided into four subspaces, specifically, two parts which make contribution and no contribution to alarming T 2 monitoring statistic, the part responsible for the out-of-control SPE monitoring statistic and the left final residuals. The application to a typical multiphase batch process with intra-batch evolution, injection molding start-up process, illustrates the feasibility and performance of the proposed algorithm.

关键词： process monitoring multiphase batch process intra-batch evolution relative variation

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