引用本文:卫浪,蒲红宇,向辉,田戬,杨冬磊. 基于改进神经网络的丙烷回收流程多目标优化[J]. 石油与天然气化工, 2021, 50(1): 66-71.
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基于改进神经网络的丙烷回收流程多目标优化
卫浪1,蒲红宇1,向辉1,田戬1,杨冬磊2
1.西南石油大学土木工程与测绘学院;2.中国石油天然气股份有限公司塔里木油田分公司油气运销部
摘要:
利用流程模拟软件HYSYS,根据某处理厂的实际运行数据,模拟不同操作参数下丙烷回收的直接换热流程(DHX),分析了低温分离温度、DHX塔顶温度、回流罐温度对丙烷收率及系统能耗的影响规律。以改进后的BP神经网络建立流程多目标优化模型,采用NSGA-Ⅱ算法对其进行多目标求解。其结果表明:改进后的BP神经网络对丙烷收率及系统能耗的预测精度高,相对误差均在2%以下。用NSGA-Ⅱ算法得到的Pareto解集能够为流程的设计与实际生产提供指导性作用。 
关键词:  DHX流程  BP神经网络  多目标遗传算法  Pareto前沿
DOI：10.3969/j.issn.1007-3426.2021.01.011
分类号:
基金项目:
Multi-objective optimization of propane recovery process based on improved BP neural network
Wei Lang1, Pu Hongyu1, Xiang Hui1, Tian Jian1, Yang Donglei2
1. School of Civil Engineering and Surveying and Mapping, Southwest Petroleum University, Chengdu, Sichuan, China;2. Oil and Gas Transportation and Marketing Department of PetroChina Tarim Oilfield Company, Kuerle, Xinjiang, China
Abstract:
Based on the actual operating data of a processing plant, the direct heat exchange process(DHX) of propane recovery under different operating parameters is simulated by using the process simulation software HYSYS. This paper analyzes the influence of low temperature separation temperature, DHX top temperature and reflux tank temperature on propane yield and system energy consumption. An improved BP neural network is used to establish a multi-objective optimization model of the process, and NSGA-Ⅱ (non-dominated sorting genetic algorithm) is used to solve the multi-objective solution. The results show that the improved back propagation(BP) neural network has high prediction accuracy for propane yield and system energy consumption, and the relative errors are all below 2%. The Pareto solution set obtained by the NSGA-Ⅱcan provide a guiding role for process design and actual production.
Key words:  DHX process  BP neural network  multi-objective genetic algorithm  Pareto frontier