Affiliation:
1. College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China
2. Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology Lanzhou China
3. National Experimental Teaching Center of Electrical and Control Engineering Lanzhou University of Technology Lanzhou China
Abstract
AbstractIn most batch processes, the correlations of process variables present multi‐stage characteristic as the process progress and operating conditions change. The methods building a local model at each stage ignore the potential correlations among stages, resulting in poor quality prediction of batch process. To solve this problem, a batch process quality prediction method based on multi‐stage fusion regression network (MSFRN) is proposed. First, the affine propagation clustering (AP) algorithm is used to automatically divide the stages for batch process without relying on prior knowledge. Second, the input reconstruction error and quality prediction error are organically combined to develop a stacked isomorphic and quality‐driven autoencoder (SIQAE) for each stage, which fully extracts the quality‐related features for each stage while reducing the input cumulative loss. Then, the self‐attention mechanism is used to integrate the quality‐related features of each stage so as to obtain global features which consider the correlations among stages. Finally, the global features are input into the fully connected regression layer to predict the quality variables of batch process. The effectiveness of the proposed method was verified by applying on penicillin fermentation process.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Subject
General Chemical Engineering