基于PSO算法的GSP流程C(3+)轻烃回收参数优化

Optimization of C3+light hydrocarbon recovery parameters in GSP process based on PSO algorithm

天然气加工回收轻烃过程中,影响轻烃回收率和装置能耗的因素较多,且各因素之间往往相互影响,因此常规的单因素、单目标优化难以实现轻烃回收流程收益的最大化。为了解决此问题,采用流程模拟软件HYSYS,选取影响GSP(气体过冷流程)轻烃回收流程的能耗和C(3+)产品收率的关键参数进行特性分析。在此基础上,依据响应面法建立各关键参数与能耗和C(3+)产品回收率的多目标优化模型,其直观反映各关键参数对能耗和收率的影响程度,并根据自适应粒子群(PSO)算法对其进行优化求解得到Pareto解集。结果表明:在不同的需求下,Pareto解集对应的优化参数可有效地降低能耗和提高收率,为轻烃回收参数优化提供了有效的方法。

In the process of light hydrocarbon recovery from processing natural gas,there are many factors that affect the recovery rate of light hydrocarbon and energy consumption of the unit,and the factors often affect each other.Therefore,the conventional single factor and single objective optimization is difficult to maximize the benefits of light hydrocarbon recovery process.In order to solve this problem,the process simulation software HYSYS was used to select the key parameters that affect the energy consumption and C3+product yield of the GSP(gas subcooled process)light hydrocarbon recovery process.On this basis,based on the response surface method,a multi-objective optimization model for each key parameter,energy consumption and C3+product recovery rate was established,which can directly reflects the impact of key parameters on energy consumption and yield,and the Pareto solution set was obtained by the adaptive particle swarm optimization(PSO)algorithm.The results showed that under different requirements,the corresponding optimization parameters of Pareto solution set can effectively reduce energy consumption and improve yield,which provides an effective method for the optimization of light hydrocarbon recovery parameters.