Novel Computational Methods for Modeling and Control in Chemical and Biochemical Process Systems

Abstract

This chapter is focused on developing more efficient computational schemes for modeling and control of chemical and biochemical process systems. In the first part of the chapter a theoretical framework for estimation of general process kinetic rates based on Artificial Neural Network (ANN) models is introduced. Two scenarios are considered: i) Partly known (measured) process states and completely known kinetic parameters; ii) Partly known process states and kinetic parameters. The two scenarios are successfully tested for two benchmark problems, estimation of the precipitation rate of calcium phosphate and estimation of sugar crystallization growth rate. In the second part of the chapter the developed ANN-based models are integrated into the structure of a nonlinear model predictive control (MPC). The proposed ANN-MPC control scheme is a promising framework when the process is strongly nonlinear and input-output data is the only process information available.