Robust model predictive control of an industrial partial combustion fluidized-bed catalytic cracking converter

Modelling, control and supervisory optimization of generalized predictive control in catalytic cracking reactor

As gasoline demand increases, the efficiency of operation of Fluidized Catalytic Cracking Unit (FCCU) becomes paramount importance. In this paper, a dynamic model for FCCU is simulated and integrated with yield model in order to estimate the yield of products namely gasoline, light gases and coke. Conventional PI controllers are designed for the control of reactor and regenerator temperature. Since, the complete reaction occurs in a very short duration, the controllers are tuned so as to achieve shorter settling time and minimum overshot. Further in order to increase the yield, optimization of FCCU using Generalized Predictive Controller (GPC) at supervisory level is attempted. Through optimization of objective function, the GPC will provide optimized set point for the PI controller in order to maintain maximum gasoline yield.

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.