Integrating Autoencoder and Heteroscedastic Noise Neural Networks for the Batch Process Soft-Sensor Design

Sam Kay, Harry Kay, Max Mowbray, Amanda Lane, Cesar Mendoza, Philip Martin, Dongda Zhang

Research output: Contribution to journalArticlepeer-review


Viscosity represents a key product quality indicator but has been difficult to measure in-process in real-time. This is particularly true if the process involves complex mixing phenomena operated at dynamic conditions. To address this challenge, in this study, we developed an innovative soft sensor by integrating advanced artificial neural networks. The soft sensor first employs a deep learning autoencoder to extract information-rich process features by compressing high-dimensional industrial data and then adopts a heteroscedastic noise neural network to simultaneously predict product viscosity and associated uncertainty. To evaluate its performance, predictions of product viscosity were made for a number of industrial batches operated over different seasons. Furthermore, probabilistic machine learning techniques, including the Gaussian process and the Bayesian neural network, were selected to benchmark against the heteroscedastic noise neural network. Through comparison, it is found that the proposed soft-sensor has both high accuracy and high reliability, indicating its potential for process monitoring and quality control.

Original languageEnglish
Pages (from-to)13559-13569
Number of pages11
JournalIndustrial & Engineering Chemistry Research
Issue number36
Early online date2 Sept 2022
Publication statusPublished - 14 Sept 2022


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