Bidirectional Minimal Gated Unit-Based Nonlinear Dynamic Soft Sensor Modeling Framework for Quality Prediction in Process Industries

Abstract

Quality prediction plays a crucial role in improving the product quality and economic benefit for modern process industries. The high-dimensional characteristics of process variables and the nonlinear dynamic behaviors of sequential data make the conventional soft sensor modeling approaches encounter difficulties. Towards this end, a novel nonlinear dynamic soft sensor modeling scheme is designed for quality prediction. It will offer a timely reference for judging product quality and adjusting production plans. Specifically, the maximum information coefficient is introduced for measuring the interdependencies between process and quality variables, and an evaluative criteria is designed for quality-related variable selection. Subsequently, a novel bidirectional minimal gated unit structure is proposed for nonlinear dynamic soft sensor modeling, of which the historical and future temporal information of production and quality features is fully used and integrated for quality prediction. In the end, the effectiveness and practicality of the proposed scheme is demonstrated via a challenging process industry, the hot rolling process. The simulation results indicate that the proposed method is superior to other competitive methods.