過度使用石化燃料造成大量製造溫室氣體，促使全球暖化現象持續惡化。目前研究傾向於更有效回收廢熱能源再發電，提升能源使用效率；發展再生能源，減少石化燃料用量。為適應不同溫度條件，目前最常用來提升發電效率的方式為，將不同種熱力循環銜接，因此增加可變參數的數量及優化的複雜程度。為了能夠系統性解決多參數優化問題，本研究開發一套系統能優化現有熱力循環，在已知參數(壓力、溫度、工作流體濃度)範圍內，優化實際運行的汽電共生循環及常見之地熱發電循環(有機朗肯循環與卡琳娜循環)，以提升發電效率。本研究將上述熱力循環各自建立成數值化的熱力模型，其計算結果各別與前人文獻相符，以此實證該熱力模型之準確性，且結合自行撰寫之程式碼(基因演算法)分別優化熱力模型之重要參數。最終各循環皆可獲得一組最佳化參數：汽電共生循環的最佳發電效率約為55.2%，與優化前相比約提升3.5%。另外，在給定同樣條件下優化，有機朗肯循環最佳發電效率為15.88%，而卡琳娜循環優化後所得之最佳發電效率為14.53%。此兩者與優化前相比，前者約提升2.8%，後者約提升4.9%。證明基因演算法能有效解決多重參數優化問題，且幫助設計循環系統取得最佳效率。

Global warming becomes worse due to the rapidly increasing demand for fossil fuels. It can be decrease by utilization of low - grade heat sources such as low - temperature geothermal and waste heat sources. Combined cycles such as combined cycle power plant (CCP), Organic Rankine cycle (ORC), and Kalina cycle (KC) have been proposed in recent years. Using multicomponent working fluid in combined cycle can fit different heat sources. To improve the efficiency of thermodynamic cycle, the genetic algorithm(GA) is chosen to optimize cycles which have multiple parameters. The optimized CCP efficiency is 55.2%, ORC efficiency is 15.88%, and KC efficiency is 14.53%. The efficiency is all improve in this work.

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