Optimisation of a Chemical Process by Using Machine Learning Algorithms with Surrogate Modeling

Abstract

Process models are getting more detailed, thus computational costs are rising. For this reason, the main aim of process engineering is to provide effective and cost-efficient production processes. Computational methods are important for composing the field of process systems engineering. They are used in process design and simulation with the capability of modeling, prediction, and optimizing processes. Also, machine learning (ML) emerges for enhancing this capability with providing a solution by performing as surrogate models of complex relationships in processes. By this way, accurate and efficient process optimization is presented. In the paper, machine learning algorithms were used on data which is generated through the sampling of varied parts of an ethylene oxide (EO) process plant in Pyomo. The physical system being surrogate modeled is an ethylene oxide plug flow reactor. Also, it is evaluated for accuracy and speed of surrogate modeling for various ML algorithms and various sampling techniques which are random, stratified, latin hypercube.