Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks

Abstract

Slow feature analysis is a technique that extracts slowly varying latent variables from a dataset. These latent variables, known as slow features, can capture underlying dynamics when applied to process data, leading to improved generalisation when a data-driven model is built with these slow features. A method utilising slow feature analysis with neural networks is proposed in this paper for improving generalisation in nonlinear dynamic process modelling. Additionally, a method for selecting the number of dominant slow features using changes in slowness is proposed. The proposed method is applied to creating a soft sensor for estimating polymer melt index in an industrial polymerisation process to validate the method’s performance. The proposed method is compared with principal component analysis-neural network and a neural network without any latent variable method. The results from this industrial application demonstrate the effectiveness of the proposed method for improving model generalisation capability and reducing dimensionality.