A genetic algorithm based approach to intelligent modelling and control of pH in reactors

The present work reports a novel genetic algorithm (GA) based strategy for designing efficient ‘global’ pH controllers in highly nonlinear neutralisation reactors, wherein linear internal model control (IMC) methodology and generic nonlinear compensators defined from acid–base principles are applied. In the study, the GA was used to optimise the IMC and nonlinear compensator parameters by evaluating a first-principles model developed to simulate a pH reactor. The proposed methodology was tested for assumed first- and second-order IMC transfer function models, initially for generality on a highly buffered pH reactor. A variant was developed for the problem of severe reactor nonlinearity, by including one of four defined nonlinear compensators in the optimisation process. Simulation results showed that the general use of the genetic algorithm based internal model control (GAIMC) strategy automated controller tuning that improved control performance, while its use with nonlinear compensators for the severely nonlinear reactor achieved tighter pH control for multiple operating regimes