Gray wolf optimizer with bubble-net predation for modeling fluidized catalytic cracking unit main fractionator

Abstract

Abstract Fluidized catalytic cracking unit (FCCU) main fractionator is a complex system with multivariable, nonlinear and uncertainty. Its modeling is a hard nut to crack. Ordinary modeling methods are difficult to estimate its dynamic characteristics accurately. In this work, the gray wolf optimizer with bubble-net predation (GWO_BP) is proposed for solving this complex optimization problem. GWO_BP can effectively balance the detectability and exploitability to find the optimal value faster, and improve the accuracy. The head wolf has the best fitness value in GWO. GWO_BP uses the spiral bubble predation method of whale to replace the surrounding hunting scheme of the head wolf, which enhances the global search ability and speeds up the convergence speed. And Lévy flight is applied to improve the wolf search strategy to update the positions of wolfpack for overcoming the disadvantage of easily falling into local optimum. The experiments of the basic GWO, the particle swarm optimization (PSO) and the GWO_BP are carried out with 12 typical test functions. The experimental results show that GWO_BP has the best optimization accuracy. Then, the GWO_BP is used to solve the parameter estimation problem of FCCU main fractionator model. The simulation results show that the FCCU main fractionator model established by the proposed modeling method can accurately reflect the dynamic characteristics of the real world.