Correntropy long short term memory soft sensor for quality prediction in industrial polyethylene process

Abstract

A typical challenge for construction of accurate soft sensors in the process industries is that industrial process data often contains various noise and outliers. Inspired by correntropy in tackling non-Gaussian noise effectively, a maximum correntropy criterion-based long short term memory (MCC-LSTM) neural network is proposed to develop a reliable soft sensor model for quality prediction. Without tedious data preprocessing approaches, the MCC-LSTM adopts the objective function using the maximum correntropy criterion (MCC) centered on a Gaussian kernel, which assigns relatively smaller weights to outliers automatically. Once the MCC-LSTM model is constructed, the outliers can be identified and their negative effects on the prediction can be reduced to some extent. Consequently, the prediction performance can be enhanced for modeling of process data with uncertainties. Additionally, an index is introduced to assess the performance of prediction models when data contain outliers and noise. A numerical example and an industrial polyethylene process demonstrate that the MCC-LSTM soft sensor can achieve more reliable predictive performance compared to traditional LSTM and support vector machine candidates.