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An ensemble just‐in‐time learning soft‐sensor model for residual lithium concentration prediction of ternary cathode materials
Author(s) -
Chen Jiayao,
Gui Weihua,
Dai Jiayang,
Yuan Xiaofeng,
Chen Ning
Publication year - 2020
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3225
Subject(s) - soft sensor , ternary operation , residual , principal component analysis , computer science , flexibility (engineering) , adaptability , ensemble forecasting , process (computing) , ensemble learning , artificial intelligence , biological system , algorithm , mathematics , statistics , programming language , ecology , biology , operating system
The surface‐free lithium content, which is a critical index that reflecting the quality of ternary cathode materials, usually cannot be monitored in real time due to technical restriction. To this end, soft sensor technique is applied to predict the surface‐free lithium content by readily available process variables, making timely control on operation parameters. In this paper, a modeling method based on ensemble just‐in‐time learning (JITL) soft‐sensor is developed. In the model, data feature indices are firstly designed to properly extract the real‐time and short‐time features between the batching and the loading procedure. Accordingly, ensemble JITL soft‐sensor model is constructed with the semisupervised local weighted probability principal component regression. Moreover, considering varying working conditions, an adaptive moving window technique is adopted to improve the adaptability of the model. The validation and the flexibility of the developed modeling method are testified with the practical manufacturing data.