Experimental fuzzy modeling and control of a once-through boiler

Increasing demand for electricity and growing need for more and safer power generation has motivated investigation into dynamic analysis of power plants to design more sophisticated and reliable control systems. In this paper, simple first order models are developed for the subsystems of a subcritical once through boiler, based on thermodynamics principles and energy-mass balance, together with parameter estimation routines. These routines are applied on the experimental data obtained from a complete set of field experiments. However, since most processes in a boiler are categorized as multi input and multi output systems, mathematical boiler models which are derived from physical structure and parameters estimation routines lead to a time consuming procedure, and employing such models in control algorithms becomes so complex. Therefore, to improve the dynamics modeling, a concise multilayer neuro fuzzy model of the boiler is developed. Next, these two models are compared based on the performance of the real system. This comparison validates the accuracy of both original and neuro fuzzy models, while the latter can be successfully employed in modern model-based control systems. Finally, a new fuzzy P²ID controller is developed to use for superheaters temperature control. Simulation results show very good performance of this controller in terms of more accurate and less fluctuation in the temperature of corresponding subsystems, compared to the existing classic controllers.