A novel sampled-data asynchronous CatBoost model with knowledge-based guidance for product quality estimation in the industrial processes

Abstract

For process industry with asynchronous sampling, delayed laboratory analysis of the product quality restricts operation control and optimization. However, it is challenging to ensure the estimation accuracy due to the fact that online estimation model is disturbed by the dynamic change of production conditions. Therefore, a product quality estimation strategy for asynchronous sampling industrial processes under data-driven and knowledge-based guidance is proposed. First, domain knowledge and process mechanism are used to establish the mechanics models and identify the critical variables. Then, the dimensionality of the samples is reduced to extract effective information which is used to partition data samples. Besides, the data sample is balanced by the adaptive synthetic sampling technique under different production conditions. Finally, a CATBoost estimation model is built for product quality online acquisition to improve interpretability and adaptability. The reliability of the proposed estimation approach is verified by a sodium aluminate solution evaporation application case.