期刊
AICHE JOURNAL
卷 62, 期 3, 页码 717-725出版社
WILEY
DOI: 10.1002/aic.15090
关键词
process control; soft sensor; just-in-time; locally weighted partial least squares; ensemble learning
资金
- Japan Society for the Promotion of Science (JSPS) [24760629]
- Core Research for Evolutionary Science and Technology (CREST) project Development of a knowledge-generating platform driven by big data in drug discovery through production processes of the Japan Science and Technology Agency (JST)
- Mitsui Chemicals Inc
The predictive ability of soft sensors, which estimate values of an objective variable y online, decreases due to process changes in chemical plants. To reduce the decrease of predictive ability, adaptive soft sensors have been developed. We focused on just-in-time soft sensors, especially locally weighted partial least squares (LWPLS) regression. Since a set of hyperparameters in an LWPLS model has to be set beforehand and there is only onedataset, a traditional LWPLS model is difficult to accurately predict y-values in multiple process states. In this study, we propose to combine LWPLS and ensemble learning, and predict y-values with multiple LWPLS models, whose datasets and sets of hyperparameters are different. The weights of LWPLS models are determined based on Bayes' theorem, considering their predictive ability. We confirmed that the proposed model has higher predictive accuracy than traditional models through numerical simulation data and two industrial data analyses. (c) 2015 American Institute of Chemical Engineers AIChE J, 62: 717-725, 2016
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