SYSTEM AND PREDICTIVE MODELING METHOD FOR SMELTING PROCESS CONTROL BASED ON MULTI-SOURCE INFORMATION WITH HETEROGENEOUS RELATEDNESS

摘要

The present invention is a system and predictive modeling method specially designed to improve process control and energy efficiency for a smelting process used to produce pure metal from an ore containing said metal. Data is collected from various sensor sources in the smeltering process to predict whether an increase or decrease is needed in controlling two variables comprising temperature and an additive that allows the reaction in the electrolytic process to proceed at a lower bath temperature. The invention provides a generalized framework to learn the complex heterogeneity embedded in the collected data, and employs a regularized non-negative matrix factorization problem, which simultaneously decomposes the instance-feature and instance-label matrices, while enforcing task relatedness, feature type consistency and label correlations on the collected data. The predictive modeling method disclosed herein effectively mines the hidden correlation among the heterogeneous data and improves the prediction accuracy.