Multivariate Trajectory-Based Local Monitoring Method for Multiphase Batch Processes

Shen, Feifan; Ge, Zhiqiang; Song, Zhihuan

doi:10.1021/ie503921t

Cited by 13 publications

(8 citation statements)

References 31 publications

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“…it cannot work well in multiple normal states and nonlinear process. For these problems, many advanced methods [24,[26][27][28] have been put forward and they could be integrated into TS-PCA. In recent years, the two-dimensional system [29][30][31] has drawn much attention, so applying TS-PCA in two-dimensional systems may be a promising direction.…”

Section: Discussionmentioning

confidence: 99%

Two‐step principal component analysis for dynamic processes monitoring

2017

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In this study, a two-step principal component analysis (TS-PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross-correlation structure and dynamic auto-correlation structure in process data simultaneously, TS-PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS-PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS-PCA still can achieve better performance than the existing dynamic approaches.

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Section: Discussionmentioning

confidence: 99%

Two‐step principal component analysis for dynamic processes monitoring

2017

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“…Therefore, the second JITL strategy takes the information of the angle into consideration as a supplement of the distance measurement. Given the same dataset as the first measurement, an additional angle measurement is defined based on the distance measurement as [23]…”

Section: ) Similarity Measurementsmentioning

confidence: 99%

LSTM Soft Sensor Development of Batch Processes With Multivariate Trajectory-Based Ensemble Just-in-Time Learning

Shen

Zheng

et al. 2020

IEEE Access

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To implement the quality prediction scheme for batch processes, long short-term memory (LSTM) neural network is a feasible tool to handle with the process dynamics and nonlinearity. However, a global LSTM soft sensor suffers a decline in performance facing batch-to-batch variations. To overcome the batch diversity problem and take advantage of LSTM model, a multivariate trajectory based ensemble just-in-time learning strategy is proposed in this paper. Different trajectory based similarity measurements are designed to extract historical batch trajectories which share similar spatial positions and trends. For each selected trajectory, an online local LSTM soft sensing model is constructed and the real-time quality prediction result for each local model can be obtained. Then, a weighting parameter is determined for each model by cross validation. Bringing together quality prediction results from different local models, the ensemble prediction result can be finally figured out. Two case studies are carried out to prove the effectiveness of the proposed methodology including a fed-batch reactor and the fed-batch penicillin fermentation process.INDEX TERMS Batch production systems, Ensemble just-in-time learning, long short-term memory, multivariate trajectory analysis, soft sensor, quality prediction.

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“…Over the past few decades, various approaches have been proposed to solve the problem using multivariate statistical process monitoring (MSPM) methods, such as multiway principal component analysis (MPCA) and multiway partial least squares (MPLS) [8,9]. These traditional data-based methods are developed for monitoring purposes based on historical batch data [10,11]. Other approaches have also been proposed for quality-relevant monitoring of batch processes to improve performance [12,13].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

Shen

Zheng

et al. 2020

Processes

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To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.

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Multivariate Trajectory-Based Local Monitoring Method for Multiphase Batch Processes

Cited by 13 publications

References 31 publications

Two‐step principal component analysis for dynamic processes monitoring

Two‐step principal component analysis for dynamic processes monitoring

LSTM Soft Sensor Development of Batch Processes With Multivariate Trajectory-Based Ensemble Just-in-Time Learning

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

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