Machine learning algorithms for process analytical technology

Abstract

Increased globalisation and competition are drivers for process analytical technologies (PAT) that enable seamless process control, greater flexibility and cost efficiency in the process industries. The paper will discuss process modelling and control for industrial applications with an emphasis on solutions enabling the real-time data analytics of sensor measurements that PAT demands. This research aims to introduce an integrated process control approach, embedding novel sensors for monitoring in real time the critical control parameters of key processes in the minerals, ceramics, non-ferrous metals, and chemical process industries. The paper presents a comparison of machine learning algorithms applied to sensor data collected for a polymerisation process. Several machine learning algorithms including Adaptive Neuro-Fuzzy Inference Systems, Neural Networks and Genetic Algorithms were implemented using MATLAB® Software and compared in terms of accuracy (MSE) and robustness in modelling process progression. The results obtained show that machine learning-based approaches produce significantly more accurate and robust process models compared to models developed manually while also being more adaptable to new data. The paper presents perspectives on the potential benefits of machine learning algorithms with a view to their future in the industrial process industry.