A membrane computing optimization algorithm with multi-subsystems for parameter estimation of heavy oil thermal cracking model

Heavy oil thermal cracking is a complex multivariable and important petrochemical reaction process. The accurate kinetic models are of great significance for the simulation and analysis of this process. In this paper, a membrane computing optimization algorithm with multi-subsystems (MCOA) is proposed to solve parameter estimation problems of the heavy oil thermal cracking model. In MCOA, the nested membrane structure and rewriting rule, the crossover rule, the communication rule, and the transformation rule that are distributed in every subsystem are combined to improve the global search capacities and convergence accuracy. Studies on five benchmark test functions indicate that the MCOA outperforms the other three methods (RNA-GA, SGA, PSOPS) in terms of convergence speed and solution accuracy. Also, the MCOA is a helpful and reliable technique for estimating the heavy oil thermal cracking model parameters. The experimental results demonstrate that model curve gained by the MCOA is in good agreement with the measured data.