臺灣能源期刊發行
- 創刊日期：
102年11月30日
- 發行所：
經濟部能源署
- 發行人：
游振偉
- 地址：
台北市復興北路2號13樓
- 電話：
02-2772-1370
- 執行單位：
財團法人工業技術研究院
- 地址：
新竹縣竹東鎮中興路四段195號26館
- 服務專線：
03-5916006
- 服務信箱：
- 總編輯：
王漢英胡均立
- 顧問：
王運銘童遷祥楊日昌
- 執行主編：
劉子衙陳志臣
- 編輯委員：
方良吉王錫福李堅明李叢禎林師模馬鴻文陳介力陳希立廖芳玲廖肇寧劉文獻蕭志同顧洋(依筆畫順序排列)
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臺灣能源期刊論文全文
臺灣能源期刊第7卷第3期內容
出刊日期：September, 2020
- 題目
- 以基因演算法優化低溫熱源卡琳娜循環發電效率
- Title
- Optimization Kalina Cycle for Power Generation Efficiency Using Genetic Algorithm
- 作者
- 楊文豪、林明誼、施威宏、劉宗鑫、吳文傑、陳玉彬
- Authors
- Wun-Hao Yang, Ming-Yi Lin, Wei-Hung Shih, Zong-Sin Liou, Wun-Jie Wu, Yu-Bin Chen
- 摘要
- 工業廢熱、生質熱、地熱與太陽熱能等，均屬於低溫熱能，因其轉換電能效率不高，常常被浪 費掉。若能提升轉換效率，將能減少臺灣對其他能源的依賴，亦可減少溫室氣體排放與空氣汙染。 由於中低溫熱源溫度、發電系統工作元件的熱效率、環境條件等皆不盡相同，常用來提升發電效率 的方式為將不同種熱力循環銜接，透過循環中不同的工作流體，提升發電效率，卻因此增加可變參 數的範圍、數量及優化的複雜程度。本研究為了能夠系統性解決多參數優化問題，結合最佳化理論 與熱力性質資料庫以撰寫程式，在已知參數(膨脹機入口壓力、熱源溫度、工作流體濃度)範圍內， 優化常見之低溫熱源發電循環(卡琳娜循環)，以提升發電效率。本研究先將上述熱力循環建立成數 值化的熱力模型，重現前人文獻結果，以此實證該熱力模型之準確性，接著，模型中結合自行撰寫 之基因演算法程式碼，分別優化熱力模型之重要參數。最終獲得一組最佳化參數，使卡琳娜循環所 得之最佳發電效率為14.652%。本研究成果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。
- 關鍵字
- 低溫熱源發電，氨水，卡琳娜循環，基因演算法，熱效率
- Abatract
- Industrial waste heat, biomass heat, geothermal heat, and solar thermal energy belong to lowtemperature thermal energy resources. They were rarely used for power generation in the past and were completely wasted due to their low power conversion efficiency. However, their utilization can reduce the proportion of thermal power generation in Taiwan if the power generation efficiency can be enlarged. The utilization can even further reduce greenhouse gas emissions and air pollution. Because the temperature of the medium and low temperature heat source, the thermal efficiency of the working elements of the power generation system, and environmental conditions are all different, the commonly used way to improve the power generation efficiency is to connect different types of thermal cycles to improve the power generation efficiency through different working fluids in the cycle. However, it will increase the scope, number and complexity of variable parameters. In order to systematically solve the multi-parameter optimization problem, this study combined the optimization theory with the thermal properties database to write a program. It will optimize the common low-temperature heat source power generation cycle (Kalina cycle) within the known parameters (expander inlet pressure, temperature, working fluid concentration) to improve power generation efficiency. In this study, the above thermal cycle is first established into a numerical thermal model, which reproduces the results of previous literature to verify the accuracy of the thermal model. Then, the model is combined with a self-written genetic algorithm code to optimize the important parameters of thermal model. Finally, a set of optimized parameters is obtained, so that the optimal power generation efficiency obtained by the Karina cycle is 14.652%. The results of this research prove that the genetic algorithm can effectively solve the multi-parameter optimization problem and help design the cycle system to achieve the best efficiency.
- Keywords
- low-temperature heat source power generation, ammonia solution, Kalina cycle, genetic algorithm, thermal efficiency