CONTROL OF PH PROCESS IN STIRRED TANK REACTOR USING NEUROEVOLUTIONARY ALGORITHM

Computers & Systems Dept., Faculty of Engineering, Mansoura University

This article investigates the performance of a modified reinforcement evolutionary algorithm

when applied to control the pH process in a continuous stirred tank reactor. This system exhibits

highly nonlinear behavior and has very high gains around the electroneutrality value. The

proposed algorithm works on two levels, neuron-level and blue prints networks-level. The

present algorithm differs from standard evolutionary algorithms in that it evolves a population of

neurons to be combined in a later stage and form the hidden layer instead of evolving complete

networks directly. Moreover, these neurons are combined through different subpopulations. The

formed feed-forward networks are then evaluated on a given problem. The neurocontroller

developed was found to be robust and presents high performance during set point changes with

no oscillations and it proves its robustness through its ability to reject sudden disturbances

resulted from changing acid flow rate.