A process knowledge-based hybrid method for univariate time series prediction with uncertain inputs in process industry

Abstract

In process industry, accurate and efficient univariate time series prediction (TSP) of key process parameters is crucial for optimizing production processes. However, noisy data and varying working conditions introduce uncertainties that hinder the modeling process. Data-driven methods have been proposed to address input-uncertain TSP models, but they often fail to obtain sufficient and high-quality industrial data for model establishment in complex and dynamic production process. The paper presents a process knowledge-based hybrid method for univariate TSP that handles noisy inputs in different working conditions. Primarily, a knowledge-embedded deep kernel network (KE-DKN) model is proposed that approximates the outputs to the knowledge space in noisy industrial environment, by embedding instantiated process knowledge as soft constraints into the conventional DKN network. Further, a two-stage training schema is designed for KE-DKN to reduce the interference superposition on the predicted variable from random noisy data and operational interferences. Moreover, the pre-trained parameters transferring is conducted from stable working conditions to mitigate the low modeling efficiency in unstable working conditions. In two cases from process industry, the proposed method delivers higher prediction accuracy and robustness than baseline methods in both working conditions, and the knowledge transfer significantly enhances the modeling efficiency in unstable working conditions.