A digital twin–driven method for online quality control in process industry

Abstract

To ensure the stability of product quality and production continuity, quality control is drawing increasing attention from the process industry. However, current methods cannot meet requirements with regard to time series data, high coupling parameters, delayed data acquisition, and ambiguous operation control. A digital twin–driven (DTD) method is proposed for real-time monitoring, evaluation, and optimization of process parameters that are strongly related to product quality. Based on a process simulation model, production status information and quality related data are obtained. Combined with an improved genetic algorithm (GA), a time sequential prediction model of bidirectional gated recurrent unit (bi-GRU) with attention mechanism (AM) is built to flexibly allocate parameter weights, accurately predict product quality, timely evaluate technical process, and rapidly generate optimized control plans. A typical case study and relevant field tests from the process industry are presented to prove the effectiveness of the method. Results indicate that the proposed method clearly outperforms its competitors.