室內溫水游泳池為了維持室內的設計條件必須引進適當的新鮮空氣,當水份從池水表面蒸發,使得排出的空氣包含了很多的水份及很高的焓值,為了回收室內空氣的熱量,通常都利用熱泵系統做室內溫水游泳池的熱回收。為了減少能源消耗的費用,本研究使用粒子族群演算法來最佳化熱泵系統。 本研究之最佳化參數包括連續參數及不連續參數,連續參數如外氣進氣比例、熱交換器的熱導度及不連續參數如壓縮機型號及鍋爐型式。在案例探討,以最低能源成本為目標函數,利用粒子族群演算法做外氣進氣比例及加熱系統各元件的設計的最佳化。 經由案例分析得知,所發展出來的最佳化模式在最佳解的一致性及收斂性上有良好的表現,並可成功應用於不同外氣條件下的室內溫水游泳池加熱系統設計。經由案例可得知結論為將粒子族群演算法應用於室內溫水游泳池的加熱系統是可行的方法。
Indoor swimming-pool is usually conducted through a suitable introduction of fresh air to maintain indoor design condition. Since water is evaporated from the pool surface, the exhausted air contains more water and specific enthalpy. In response to this indoor air, heat pump is generally used in heat recovery for indoor swimming pools. This paper utilizes particle swarm algorithm to optimize heat pump system. The optimized parameters include continue parameters: outdoor air mass flow, and heat conductance of heat exchanger; and discrete parameters: compressor type and boiler type. In a case study, life cycle energy cost is considered as an objective function, and optimal introduced mass flow of outdoor air and optimal design for heating system are found by using Particle Swarm algorithm. From the convergence of solution progress and the consistency for finding the best solution, we conclude that the Particle Swarm algorism is effective method for optimizing the heating system of indoor swimming pool.