Output‐relevant fault detection and identification of chemical process based on hybrid kernel T‐PLS

Zhao, Xiaoqiang; Xue, Yadong

doi:10.1002/cjce.22031

Cited by 13 publications

(17 citation statements)

References 21 publications

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“…In this paper, the state variables and the control variables are not included in the correlated condition R i (t), therefore, the relationship h i can be set‐up independently. Normally, kernel partial least squares (KPLS) is a method that can describe the complicated nonlinear relationship between input and output data precisely and without mechanism knowledge . Therefore, the KPLS‐based parameter forecast method is adopted to update the α i online.…”

Section: Dynamic Modelling For the Sequential Collaborative Reactors mentioning

confidence: 99%

Dynamic modelling and predictive control for the sequential collaborative reactors of cobalt removal process under time‐varying conditions

et al. 2019

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The accuracy of the process model directly affects the performance of the model‐based controller. In zinc hydrometallurgy, the overall dynamics of the cobalt removal process can hardly be described by a fixed model since there are a series of interconnected reactors working together under time‐varying inlet and reaction conditions. In this study, an interacting continuously stirred tank reactors (ICSTR) model is developed to describe the cooperative relationship of these cascaded reactors. Considering the time‐varying inlet and reaction conditions, the reaction surface conversion coefficient is defined and incorporated into the ICSTR model, and the kernel partial least squares (KPLS) is employed to update the dynamic model online. The effectiveness of the time‐varying ICSTR model is validated using industrial data. Based on the proposed time‐varying ICSTR model, a predictive controller is designed to realize the optimal operation of the cobalt removal process. Simulation results indicate that compared with conventional predictive control and manual manipulation, the time‐varying ICSTR model‐based predictive control method can not only maintain the outlet cobalt ion concentration but also reduce the zinc dust dosage.

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Section: Dynamic Modelling For the Sequential Collaborative Reactors mentioning

confidence: 99%

Dynamic modelling and predictive control for the sequential collaborative reactors of cobalt removal process under time‐varying conditions

et al. 2019

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“…Thus, investigation of historical process data has become an interest of the industrial sector. Different techniques, such as principal component analysis (PCA), partial least‐squares (PLS), and Fisher discriminant analysis (FDA), have been developed for process monitoring based on multivariate data . Moreover, in order to enhance the FD performance, feature extraction methodologies, such as wavelet transformation, PCA, and independent component analysis (ICA), have been integrated with FD tools .…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of chemical processes considering fault frequency via Bayesian network

et al. 2016

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In the present study, data‐driven fault diagnosis (FD) systems of chemical plants dealing with frequent and rare faults are investigated. Although different faults occur with different frequencies in chemical plants, this issue has scarcely been addressed in developing a process FD system. A novel diagnostic framework based on the Bayesian network (BN) is proposed to incorporate fault frequencies. This probabilistic method can readily involve non‐uniform probability distribution of faults and non‐Gaussian probability distribution of features. The proposed approach includes not only a combination of tools but also information management. In fact, the imbalanced dataset, established by frequent and rare faults, promotes recursive updating of prior probabilities of faults. The performance of the BN was evaluated and compared with the conventional C4.5 method in the Tennessee‐Eastman process benchmark. It was shown in this work that the diagnostic performance of the proposed approach versus the C4.5 method is more efficient. The importance of taking into account non‐uniform probability distribution of faults for designing a FD system was highlighted. Furthermore, the effect of independent component analysis (ICA) of the imbalanced dataset on the FD was examined. The proposed framework versus the C4.5 method promises 37 % FD performance improvement for the dataset with a 10:1 imbalance index.

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“…Data‐driven approaches are suitable for fault diagnosis of a batch process because only process data information is used. Multi‐way principal component analysis (MPCA) and multi‐way projection to latent structures (MPLS) algorithms are most widely used . These algorithms assume that data must meet Gaussian distribution, but actual industrial process data do not always meet the assumption.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-way principal component analysis (MPCA) [1][2][3] and multi-way projection to latent structures (MPLS) algorithms are most widely used. [4,5] These algorithms assume that data must meet Gaussian distribution, but actual industrial process data do not always meet the assumption. In order to solve the problem of non-Gaussian processes, independent component analysis (ICA) was proposed and applied to process monitoring and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

MGNPE‐LICA algorithm for fault diagnosis of batch process

2016

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Batch production process data usually contains mixture ingredients of the Gaussian and non-Gaussian distribution.Traditional monitoring methods require data to meet the requirements of Gaussian distribution and do not take into account the global and local features for feature extraction. ICA algorithm can deal with process diagnosis of non-Gaussian distribution but can't deal with process diagnosis of Gaussian distribution. So a multi-way global neighborhood preserving embedding -local independent component analysis (MGNPE-LICA) algorithm is proposed in this paper. Firstly, raw data is divided into Gaussian and non-Gaussian space by D-test. For the Gaussian space, MGNPE algorithm is used to fully extract local structural features and global structural features of data. For the non-Gaussian space, MLICA algorithm is used to solve non-Gaussian problems and at the same time to reserve global and local information of data. Then the monitoring index of two spaces synthesizes a joint monitoring indicator to monitor the process. The contribution plot method is used to diagnose fault variables after detecting faults. The simulation results of penicillin fermentation process verified the effectiveness of the proposed algorithm. This article is protected by copyright.All rights reserved

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Output‐relevant fault detection and identification of chemical process based on hybrid kernel T‐PLS

Cited by 13 publications

References 21 publications

Dynamic modelling and predictive control for the sequential collaborative reactors of cobalt removal process under time‐varying conditions

Dynamic modelling and predictive control for the sequential collaborative reactors of cobalt removal process under time‐varying conditions

Fault diagnosis of chemical processes considering fault frequency via Bayesian network

MGNPE‐LICA algorithm for fault diagnosis of batch process

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