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

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.

Automated summary

One typical challenge in constructing accurate soft sensors in the process industries is the presence of various noise and outliers in industrial process data. Inspired by the effectiveness of correntropy in tackling non-Gaussian noise, this study proposes a maximum correntropy criterion-based LSTM neural network, MCC-LSTM, to develop a reliable soft sensor model for quality prediction. By adopting the objective function centered on a Gaussian kernel, the MCC-LSTM assigns relatively smaller weights to outliers automatically, reducing their negative effects on the prediction and improving the performance for modeling process data with uncertainties.