臺灣博碩士論文加值系統

自工業革命以來，大量燃燒化石燃料如煤炭和石油，導致大氣中的二氧化碳濃度增加，已被認為是引起全球暖化和氣候變遷的主要原因。因此，減少化石燃料的使用和增加再生能源的應用以降低溫室氣體排放變得至關重要。本文考慮的再生能源是生質能，但生質能源有相當成本是在生質物種類、生質物運輸以及新建生質物發電廠的資本支出，而且生質物供應也存在不穩定性。因此，本論文將通過數學規劃法，並同時考慮不確定性，來評估生質物供應鏈的最適化。研究結果發現，當生質物工廠與發電廠的距離越近時會被優先選擇，因為運輸成本較低。在考慮新建發電廠時，鍋爐系統發電設備最受青睞，因其具有最高的電力轉換效率。不確定性的方法將使用隨機規劃法和模糊最適化來定義不確定參數。結果表明，當生質物供應的不確定性增大時，容易讓更多的生質物流入新建發電廠，導致最終供應鏈成本上升。所提出的模型將通過兩個文獻案例進行演示和說明。

Since the Industrial Revolution, the extensive burning of fossil fuels such as coal and oil has led to an increase in atmospheric carbon dioxide concentrations, which is widely recognized as a primary cause of global warming and climate change. Therefore, reducing the use of fossil fuels and increasing the application of renewable energy to lower greenhouse gas emissions has become crucial. This paper focuses on bioenergy as a renewable energy source. However, the costs associated with bioenergy are considerable, including the types of biomass, transportation of biomass, and capital expenditures for constructing new biomass power plants. Additionally, the supply of biomass is unstable. Thus, this paper aims to evaluate the optimization of the biomass supply chain through mathematical programming technique, taking uncertainty into account. The research findings indicate that biomass factories and power plants located closer together are preferred due to lower transportation costs. When considering the construction of new power plants, boiler system power generation equipment is favored for its highest power conversion efficiency. Methods to account for uncertainty will include stochastic programming and fuzzy optimization to define uncertain parameters. The results show that as the uncertainty in biomass supply increases, more biomass tends to flow into newly constructed power plants, leading to higher overall supply chain costs. The proposed model will be demonstrated and illustrated through two literature cases.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 ix
1 第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 2
2 第二章 文獻回顧 4
2.1 生質能源 4
2.2 生質物供應鏈 4
2.3 程序整合 5
2.3.1 狹點分析 6
2.3.2 數學規劃法 7
2.4 隨機規劃 8
2.5 模糊最適化 9
3 第三章 數學模型 10
3.1 問題陳述 10
3.2 符號定義 12
3.3 數學模型 14
3.3.1 生質物供應不確定性 17
3.3.1.1 隨機規劃法 17
3.3.1.2 模糊最適化 17
3.3.2 目標函數 18
3.3.2.1 昂貴生質物最小化 18
3.3.2.2 生質物運輸與消耗之成本最小化 18
3.3.2.3 生質物供應鏈總成本最小化 18
3.3.2.4 生質物供應鏈不確定性 19
4 第四章 案例研究 20
4.1 案例一 20
4.1.1 昂貴生質物最小化 21
4.1.2 生質物運輸與消耗之成本最小化 22
4.1.3 結果分析與討論 27
4.2 案例二 28
4.2.1 生質物供應鏈總成本最小化 32
4.2.2 生質物供應鏈不確定性 39
4.2.2.1 隨機規劃法 39
4.2.2.2 模糊最適化 45
4.2.3 結果分析與討論 49
5 第五章 結論 50
參考文獻 52

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