摘要
在天然气轻烃回收的过程中,影响能耗和C^(2+)收率的各因素间存在较强的非线性关系,常规人工调节难以达到两者间的平衡。为降低轻烃回收的工艺能耗,以气体过冷工艺(GSP)为例,在分析不同节点参数变化对能耗和C^(2+)收率影响的基础上,通过单一变量法和敏感性分析确定关键因素,利用Hysys和Matlab的交互作用实现数据互通,进而通过遗传算法实现能耗优化。结果表明,低温分离器温度、气相分流比和膨胀机等熵效率对C^(2+)收率的影响较大,脱甲烷塔塔压对总能耗的影响较大。遗传算法实现了最小总能耗和最大C^(2+)收率的求解,在C^(2+)收率相近的前提下,总能耗降幅为7.89%;在总能耗相近的前提下,C^(2+)收率增幅为1.65%。研究结果可为不同工况下轻烃回收工艺节能降耗措施的制定提供参考。
In the process of light hydrocarbon recovery of natural gas,there is a strong nonlinear relationship between the factors affecting energy consumption and C^(2+)yield,which is difficult to achieve a balance between the two with conventional manual regulation.In order to reduce the energy consumption of light hydrocarbon recovery process,taking gas super-cooling process(GSP)as an example,the key factors are determined by single variable method and sensitivity analysis on the basis of analyzing the energy consumption and C^(2+)yield of parameter changes at different nodes.The interaction of Hysys and Matlab was used to realize data exchange,and then energy consumption optimization was realized by genetic algorithm.The results show that the temperature of the low temperature separator,the gas-phase split ratio and the isentropic efficiency of the expander have a great influence on the C^(2+)yield while the tower pressure has a great influence on the total energy consumption.In addition,the genetic algorithm can be solved the minimum total energy consumption and the maximumC^(2+)yield.Under the premise of similar C^(2+)yield,the total energy consumption can be decreased by 7.89%while under the premise of similar total energy consumption,the yield of C^(2+)yield can be increased by 1.65%.The research results can be provided the reference for the optimization of light hydrocarbon recovery energy consumption under different working conditions.
作者
姜顺姬
JIANG Shunji(No.5 Oil Production Plant of Daqing Oilfield Co.,Ltd.)
出处
《石油石化节能与计量》
CAS
2023年第12期80-84,共5页
Energy Conservation and Measurement in Petroleum & Petrochemical Industry
关键词
气体过冷工艺
遗传算法
能耗
C^(2+)收率
参数优化
gas super-cooling process
genetic algorithm
energy consumption
C^(2+)yield
parameter optimization
