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

摘要：

针对催化裂化反应再生过程难以有效解决提升效率、降低损耗、减少排放的多目标优化问题，利用改进的多目标量子粒子群算法进行求解。建立轻油收率、焦炭产率和硫化物排量的多目标优化模型；引入拥挤熵排序更新外部档案，精确估计非支配解集分布性；构造自适应因子以动态调整吸引子，平衡算法的收敛性和多样性；再引入高斯变异进行分段式扰动，增强算法的局部搜索精度，最后求解该优化模型。对某厂催化裂化进行实验，得到轻质油吸收率76.22%，焦炭产率5.72%和硫化物排放量626 mg/m³的结果，均优于其他比较算法，表明改进后的算法可以快速、准确地获得分布均匀的 Pareto 最优解，能有效解决反应再生过程多目标优化问题。

关键词: 催化, 反应, 控制, 优化, 量子粒子群优化算法, 拥挤熵