应用NSGA-Ⅱ算法的硫酸分解过程多目标优化

Multi-objective optimization of sulfuric acid decomposition process using NSGA-Ⅱ algorithm

基于有限时间热力学理论分析管式活塞流反应器的进口初始条件对硫酸(SO2)分解反应过程的影响,并运用第二代非支配解排序遗传算法(NSGA-Ⅱ),以反应物进口温度、反应物进口压力和反应物进口总摩尔流率Ftot,in为优化变量,对参考反应器的总熵产生率最小和SO2产率最大进行多目标优化。结果表明,参考反应器的总熵产生率最小和SO2产率最大之间相互制约,两者无法同时取得最优值;多目标优化可以得到参考反应器的总熵产生率最小和SO2产率最大在占不同权重时的最优决策,即Pareto最优前沿,对参考反应器进行Pareto改进,可得到较好的优化结果。文中研究结果对实际管式活塞流反应器的设计与优化具有一定的理论指导意义。

Based on the finite-time thermodynamic theory,the influences of initial inlet conditions on sulfuric acid(SO2)decomposition reaction in tubular piston flow reactor are analyzed.The second generation non-dominated solution sequencing genetic algorithm(NSGA-Ⅱ)is used to optimize the inlet temperature,pressure and total molar flow rate of the reactant.Multi-objective optimization ia carried out to minimize the total entropy production rate and maximize the SO2 production rate of the reference reactor.The results show that the minimum total entropy production rate and the maximum SO2 production rate of the reference reactor are mutually restricted,and they cannot obtain the optimal value at the same time.Multi-objective optimization can get the optimal decision-making when the total entropy production rate of the reference reactor is the smallest and the SO2 production rate is the largest with different weights,i.e.the Pareto optimal frontier.The Pareto improvement of the reference reactor can get a better optimization result.This paper has a certain theoretical guiding significance for the optimization of actual tubular piston flow reactors.