基于遗传算法的水源热泵系统抽灌量优化配置

抽灌井设计是水源热泵系统设计中的关键环节之一,但目前对抽水量和回灌量的配置仍依据经验而行,导致运行成本较高。针对该问题,本文利用抽水和回灌现场试验资料,根据抽水井和回灌井的实际布局情况,建立了不同抽灌模式下水源热泵系统运行能耗最小的数学模型,利用遗传算法求解数学模型以实现抽灌量的优化配置。研究表明:水源热泵系统抽灌井施工完毕后,可根据水文地质条件及管道布局情况来优化配置其抽灌量,以实现低能耗运行;遗传算法具有适应性强、全局优化能力高和参数拾取方便的优点,可用于水源热泵系统抽灌量的合理调配;优化确定的以井5为抽水井、井1和井2及井4为日常回灌井、井3为备用回灌井,井5抽水量为90 m~3/h,井1、井2、井4回灌量分别为42.5 m~3/h、33.4 m~3/h、14.1 m~3/h,为生产中科学快速调度抽灌模式提供了依据。

Optimal pumping and recharging rates allocation for a water-source heat-pump system based on a genetic algorithm

The design of pumping and recharging wells is one of the key issues in designing a wa-ter-source heat-pump system. However, the current allocation of pumping and recharging rates is still based on experience, which leads to higher operating cost. In view of these problems, this study undertook to find a solution based on pumping and recharging test data, building an optimization model of minimum pump energy consumption under different pumping and irrigation modes of a water-source heat pump sys-tem, and obtaining an optimized configuration of the pumping-recharging rates using a genetic algorithm. The result shows that after the construction of pumping-recharging wells in water-source heat-pump sys-tem,the pumping-recharging quantity can be optimized according to the hydrogeological conditions and pipe-line layout to achieve low energy consumption operation; the genetic algorithm has the advantages of strong adaptability,high global optimization ability and convenient parameter picking,which can be used for ratio-nal allocation of pumping-recharging rates in water-source heat-pump system. Then taking well 5 as pump-ing well, well 1, well 2 and well 4 as daily recharging well, and well 3 as standby recharge well. The pumping rates of well 5 is 90 m³/h,and the recharging rates of well 1,well 2 and well 4 are 42.5 m³/h, 33.4 m³/h and 14.1 m³/h, which provides a basis for scientific and rapid dispatch of pumping-recharging mode in production.