Optimal Tuning of Model Predictive Controller using Chicken Swarm Optimization for Real-time Cultivation of Escherichia coli

Bioreactor plays a significant role in many industries such as pharmaceuticals, food products, etc. as these processes depend on the microorganisms. High biomass yield can generally be achieved by operating the bioreactor in fed-batch mode, with an effective model and a suitable advanced control scheme. Modeling a fed-batch bioreactor is a challenging task due to its nonlinear and dynamic behavior. In this work, a hybrid model is developed based on the experimental data collected from a bioreactor that describes the dynamic behavior of aerobic fed-batch cultures of Escherichia coli (E. coli). The biomass profile obtained from hybrid model with GA based feed profile input is used as the desired set point for the Model Predictive Controller (MPC). The parameters of MPC are tuned using Chicken Swarm Optimization (CSO) algorithm. The controller thus designed to obtain maximum biomass concentration uses a predictive model and dynamically updates the feed profile. The real-time automation strategy developed by the authors using LabVIEW (Laboratory Virtual Instrument Engineering Workbench) platform is capable of controlling the key variables such as temperature, Dissolved Oxygen (DO), pH, and antifoam simultaneously during fermentation. The implementation of this optimally tuned controller with optimal set point profile improves the biomass concentration significantly during the fed-batch operation of the bioreactor.