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Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models.

ABSTRACT: The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning method, the valuable information hidden in unlabeled data can be explored and absorbed into the prediction model. The application results to an industrial blast furnace show that BLSM has better prediction performance compared with other supervised soft sensors.

SUBMITTER: He X

PROVIDER: S-EPMC6749592 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models.

He Xing X Ji Jun J Liu Kaixin K Gao Zengliang Z Liu Yi Y

Sensors (Basel, Switzerland) 20190903 17

The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning ...[more]

PMID: 31484466

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