关键词: 催化, 反应, 控制, 优化, 量子粒子群优化算法, 拥挤熵

Abstract:

It is difficult to solve the multi-objective optimization problem of improving efficiency, reducing loss and reducing emissions for the catalytic cracking reaction regeneration process. The improved multi-objective quantum-based particle swarm optimization-crowding entropy sorting (MQPSO-CES) is used to solve the problem. A multi-objective optimization model is established to maximize the light oil absorption rate and synchronously minimize the coke yield and sulfide emissions. Particularly, crowding entropy sorting is used to update the archive, which accurately estimates the distribution of the non-dominated solutions. Afterwards, an adaptive factor is introduced to self-adaptively and dynamically adjust the construction of the attractor, which can balance the convergence and diversity of the proposed algorithm. In addition, with the application of a piecewise Gauss mutation operator, the precision of the local search can be enhanced. Finally, the multi-objective model is resolved with the novel algorithm. The results indicate that the improved algorithm can outperform other algorithms with convergent and well-distributed approximate Pareto fronts when dealing with ZDT3-4 and DTLZ1-2 benchmark problems. In addition, the proposed algorithm can obtain 76.22% of light oil absorption rate, 5.72% of coke yield and 626 mg/m³ of sulfide emissions in the reaction and generation process, illustrate its superiority compared with other algorithms.

Key words: catalysis, reaction, control, optimization, quantum-based particle swarm optimization, crowding entropy