Probabilistic machine learning based soft-sensors for product quality prediction in batch processes

The major promise of the 4th industrial revolution is encompassed by the utilization of real-time process data to inform operational decision-making. Research focus relating to the monitoring of batch process end product qualities has typically been dominated by the use of latent variable models. In this work, we combined latent variable modeling with probabilistic machine learning methods to construct novel soft-sensors, which are able to synchronously capture nonlinearities expressed within process data, as well as identify accurate uncertainty estimates for predictions. Specifically, we explored the combination of multiway projection to latent structures (MPLS) with Gaussian processes (GPs), Bayesian neural networks (BNNs) and heteroscedastic noise neural networks (HNNs). We demonstrated performance of the soft-sensors for industrial batch process quality control, which involves estimating the end viscosity of different variants of a non-newtonian liquid product manufactured over different periods. Through experimental validation, it is concluded that the use of the MPLS-HNN soft-sensor provides particular promise for industrial batch process monitoring given its high accuracy and reliability, as well as ease for practical implementation.