基于BP神经网络与遗传算法的液烃回收装置参数预测与优化  

Parameter prediction and optimization of liquid hydrocarbon recovery unit based on BP neural network and genetic algorithm

作　　者：王梓龙 刘桂莲[1] Zilong WANG;Guilian LIU(School of Chemical Engineering and Technology,Xi'an Jiaotong University,Xi'an,Shaanxi 710049,China)

机构地区：[1]西安交通大学化学工程与技术学院,陕西西安710049

出　　处：《过程工程学报》2024年第11期1284-1296,共13页The Chinese Journal of Process Engineering

基　　金：国家自然科学基金资助项目(编号:22078259)。

摘　　要：天然气液烃回收装置中,各操作参数间关联密切,混合制冷剂的组成和配比直接影响系统能耗和产品质量。基于某液烃回收装置的实际生产数据建立了该系统的BP神经网络模型,可根据天然气进料和生产要求变化优化预测混合制冷剂配比及其他关键操作参数。该模型整体预测精度较高,大多输出参数的平均绝对百分比误差小于5%,最小误差低至0.118%。用遗传算法对预测效果不理想的输出参数进行优化,制冷剂分离器液相流量误差由9.208%降低至3.321%,塔顶一板压差误差由9.602%减小为4.051%。基于所建立的GA-BP神经网络模型在夏、冬两季不同进料条件下,对制冷剂组分和制冷剂分离器液相流量、压力两项关键操作参数进行优化。优化结果表明,在夏季工况下应适当增加混合制冷剂中甲烷、丙烷和异丁烷的摩尔分率和液相制冷剂流量,并减少制冷剂中乙烯的摩尔分率。在冬季工况中,应适当减少异丁烷摩尔分率,并降低液相制冷剂压力。以夏季进料条件为例,优化混合制冷剂配比和各项操作参数,优化后制冷系统能耗降低518.12kW。The natural gas light hydrocarbon recovery unit contains a number of key operation parameters,including the composition of the mixedrefrigerant,the temperature of cryogenic separator,plate pressure,etc.These parameters directly affect the energy consumption and product quality of the system.The relationship among these parameters are complex and interrelated,which makes it complicated to build theoretical models systematically.Based on the actual production data of a liquid hydrocarbon recoveryy unit,a BP neural network model for optimizing and predicting the mixed refrigerant composition and other key operation parameters was established to achieve the goal of saving energy and increasing efficiency.The model can adapt to the changes in natural gas feed and production requirements and the overall prediction accuracy was high.Most of the mean absolute percentage error(MAPE)of the output parameters was less than 5%,and the minimum error was as low as 0.118%.The output parameters with unsatisfactory prediction effects were optimized by genetic algorithm(GA).After the optimization,the error of the liquid phase flow rate of the refrigerant separator decreased from 9.208%to 3.321%,and the error of plate pressure of the demethanizer reduced from 9.602%to 4.051%.Based on the established GA-BP neural network model,the refrigerant components and liquid phase flow rate and pressure of the refrigerant separator were optimized under different feeding conditions in summer and winter.The optimization results showed that the molar fraction of methane,propane,and isobutane in mixed-refrigerant and the flow rate of refrigerant separator should be appropriately increased in summer,and the molar fraction of ethylene should be reduced.In winter,the molar fraction of isobutane and the pressure of liquid refrigerant should be properly reduced.Taking the summer feed conditions as an example,the optimization of the mixed refrigerant proportion and various operating parameters resulted in a reduction of the refrigeration system's energy

关 键 词：天然气 轻烃回收 混合制冷剂 参数优化 BP神经网络 遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TE644[自动化与计算机技术—控制科学与工程] TB64[石油与天然气工程—油气加工工程]