Optimal De-Batch Bioprocess Control Via Intelligent System Based on Hybrid Techniques

Abstract

This paper deals with the design of intelligent control for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize some bioprocess pattern evolution using especially trained neural network, to estimate the modeling parameters and to globally control the bioprocesses with hierarchically put into operation: an expert system and a fuzzy controller. The design of the control algorithm is presented as well as its tuning through realistic simulations. Taking in consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic AI, in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.