Online optimal control of a batch fermentation process using multiple model approach

Conventional feedback controllers (of PID type) are used to control two fundamental physical state of a batch fermentation process, namely pH and temperature. To improve the efficient operation of the batch process, the set points of PID controllers are updated using an online optimization technique. Using multiple model approach, the whole operating range of the fermentation process is approximated with local linear models. The optimization problem is solved for each local linear model using a linear model predictive control (MPC) design. Each local linear MPC design computes one local optimum trajectory for pH and one for temperature. Local optimum trajectories are multiplied by their relative interpolation functions and the summation of these gives the global optimum trajectories which are used as set points for PID controllers. At every given time instant the entire optimization is repeated giving new set points for the PID controllers. Thus a relative simple method is capable for optimal control of a very complex nonlinear batch fermentation process using simple PID controllers