A hybrid GA-SQP multi-objective optimization methodology for carbon monoxide pollution minimization in Fluid Catalytic Cracking Process

摘要

In this work a multi-objective hybrid optimization strategy was developed considering genetic algorithms (GA) in series with sequential quadratic programming (SQP). This methodology is used to minimize carbon monoxide emissions of regenerator dense phase at the same time that maximize process conversion in Fluid Catalytic Cracking (FCC). The process is characterized for being a highly nonlinear with strong interactions between process variables. The combination of those optimization algorithms was developed considering final values of GA optimization as initial estimative of SQP algorithm. The reason for that is because initial estimative determined by a stochastic technique is not subject to local minimums and additionally, deterministic technique speed up the calculations and reach the final solution in shorter times in order to obtain optimization objectives with low computational burden and time.