Recursive Gaussian Process Regression Model for Adaptive Quality Monitoring in Batch Processes
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- Le Zhou
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
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- Junghui Chen
- Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li 320, Taiwan
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- Zhihuan Song
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
説明
<jats:p>In chemical batch processes with slow responses and a long duration, it is time-consuming and expensive to obtain sufficient normal data for statistical analysis. With the persistent accumulation of the newly evolving data, the modelling becomes adequate gradually and the subsequent batches will change slightly owing to the slow time-varying behavior. To efficiently make use of the small amount of initial data and the newly evolving data sets, an adaptive monitoring scheme based on the recursive Gaussian process (RGP) model is designed in this paper. Based on the initial data, a Gaussian process model and the corresponding SPE statistic are constructed at first. When the new batches of data are included, a strategy based on the RGP model is used to choose the proper data for model updating. The performance of the proposed method is finally demonstrated by a penicillin fermentation batch process and the result indicates that the proposed monitoring scheme is effective for adaptive modelling and online monitoring.</jats:p>
収録刊行物
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- Mathematical Problems in Engineering
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Mathematical Problems in Engineering 2015 1-9, 2015
Hindawi Limited