Phase Partition and Online Monitoring for Batch Process Based on Multiway BEAM

Guo, Runxia; Guo, Kaizhong; Dong, Jie

doi:10.1109/tase.2016.2542102

Cited by 15 publications

(13 citation statements)

References 24 publications

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“…The information increment matrix utilizes the covariance matrix of process variables to capture the dynamic characteristics of the system along the time direction. The existing information increment matrix‐based methods only concentrate on the process variables for fault diagnosis . In our article, we integrate quality‐related variables into time slices in order to emphasize the time varying relationship between the process variables and the product quality in batch processes.…”

Section: Methodsmentioning

confidence: 99%

“…However, these adaptive model methods are always time consuming when applied online, influencing the real time performance of the quality control system. A reasonable alternative method that is well suited to the process characteristic is to divide the batch process procedure into multiple phases and assign each phase a static model, which is referred to as phase‐based model methods or phase partition methods . Clustering‐based methods, the Gaussian mixture model (GMM), and the hidden Markov model (HMM) are widely used phase partition methods.…”

Section: Introductionmentioning

confidence: 99%

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Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Wang

Gao

et al. 2019

Can J Chem Eng

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Batch processes inherently have multiple operation phases; different phases exhibit different characteristics. Hence, it is reasonable to partition the process into phases and build sub‐phase models for online quality prediction. To this end, a sequential phase partition method based on the information increment is proposed. To address the multiphase behaviours in batch processes, this work utilizes a new information increment index to capture the dynamic characteristics of batch processes along a time direction and divides the process into sub‐phases. Next, phase‐based multiway partial least squares (MPLS) models are built to model within‐phase characteristics and predict the quality‐related variables online. Information increment is able to exploit the process evolution by focusing on the changing variable correlations derived from two adjacent extend time slice. It directly utilizes the available process measurements of successful history batch processes without data transformation or dimensionality reduction. The method is sequential and can overcome the limits of some phase partition methods that may divide the samples with discontinuous time sequence but similar characteristics into the same phase. In addition, the information increment is capable of reflecting the change of the process intuitively with high computation efficiency. Advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli (E. coli) fermentation, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Wang

Gao

et al. 2019

Can J Chem Eng

View full text Add to dashboard Cite

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“…In addition, a soft transition multiple PCA (STMPCA) method is proposed by Zhao and Gao [8] and Zhang et al [9] to detect transitions between different sub-phases. Guo et al [10] introduced an innovative algorithm, namely multiway information increment matrix (MIIM), which directly extracts effective information from the covariance matrix. Moreover, the algorithm can separate the process into several different phases based on the accurate capture of variables' correlation changes.…”

Section: Introductionmentioning

confidence: 99%

“…In order to automatically identify each sub-phase according to the time sequence, a phase identification and online monitoring method based on moving window-based MIIM for the uneven batch processes (MWMIIM) is proposed in this paper, which extends the multiway information increment matrix (MIIM) algorithm to the uneven batch processes [10]. Different from the existing MIIM algorithm, the innovative points of this paper are as follows: First, phase identification of each batch is carried out separately by combining moving window technique with MIIM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Phase Identification and Online Monitoring for the Uneven Batch Processes

Guo

Jin

2019

IEEE Access

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In practice, the batch processes are usually uneven and show significantly different variables' characteristics in different sub-phases. Therefore, it is necessary to divide each batch into several sub-phases separately. In this paper, a moving window-based multiway information increment matrix (MWMIIM) algorithm for the uneven batch processes is proposed for single-batch phase identification and online monitoring by combining the moving window technique with an information increment matrix (IIM) algorithm. Similar to the IIM algorithm, the MWMIIM algorithm captures variables' correlation changes by calculating the increment matrix between adjacent covariance matrices, which does not need to extract feature from data or carry out complicated matrix decomposition, thus improving the computational efficiency. Besides, the influence of several vital parameters on the phase identification performance is discussed in detail. After phase identification, the partition points of each sub-phase need not to be strictly aligned. Furthermore, a batch process is divided into three types of regions, namely common region, transition region, and end region. Next, fine modeling and online monitoring strategies are adopted in different regions separately. The comparative experiment is conducted by the window-based stepwise sequential phase partition method for nonlinear uneven batch processes (WNSSPP-U). A practical application on batch processes, namely aircraft steering gear system fault diagnosis experiment, is given to confirm the feasibility and effectiveness of the proposed method. INDEX TERMS Phase identification, online monitoring, uneven batch processes, moving window-based multiway information increment matrix (MWMIIM), fine modeling.

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“…7 Recently, Cubature Kalman Filter (CKF) based on the third-degree spherical–radial cubature rule is proposed and expectations have been achieved in many applications, such as positioning, sensor data fusion and attitude estimation. 8–10 Afterward, cubature particle filter (CPF) is proposed based on CKF technique. Experimental results have showed that the accuracy of CPF is higher than EKPF and the precision is similar with UPF.…”

Section: Introductionmentioning

confidence: 99%

Huber cubature particle filter and online state estimation

Guo

Gan

Zhang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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The cubature particle filter, which utilizes the third-degree spherical–radial cubature rule to generate proposal distribution, performs inaccurately when the measurement noises deviate from the Gaussian probability distribution. To improve the performance of cubature particle filter, the update process is recast by making use of the Huber technique which could generate robustness with respect to deviations from the assumed Gaussian noise. Moreover, the general resampling process is replaced by fine variety resampling approach, and as a result the degeneracy phenomenon is effectively reduced. The improved state estimation algorithm is named as Huber cubature particle filter (HCPF). The effectiveness of the proposed algorithm is illustrated via an aircraft actuator model and the experimental results are provided to support the theoretical results.

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Phase Partition and Online Monitoring for Batch Process Based on Multiway BEAM

Cited by 15 publications

References 24 publications

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Phase Identification and Online Monitoring for the Uneven Batch Processes

Huber cubature particle filter and online state estimation

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