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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 闕蓓德 | zh_TW |
dc.contributor.advisor | Pei-Te Chiueh | en |
dc.contributor.author | 陳郁慈 | zh_TW |
dc.contributor.author | Yu-tzu Chen | en |
dc.date.accessioned | 2023-10-03T16:36:48Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-04 | - |
dc.identifier.citation | Blank, J., & Deb, K. (2020). Pymoo: Multi-objective optimization in python. Ieee access, 8, 89497-89509.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90555 | - |
dc.description.abstract | 氣候變遷目前已為眾所矚目之議題,全球暖化亦為不容忽視之重要挑戰,為應對氣候變化,全球各地都需要共同努力,須採取有效的減排措施和氣候適應策略,如:聯合國氣候變化綱要公約第26次締約方大會呼籲各締約國在2050年達到淨零階段,臺灣為此將2050年達淨零排放放入氣候變遷因應法中。本研究建構溫室氣體減量最佳化模型,以臺灣鋼鐵業為研究範疇,涵蓋環境與經濟、技術以及政策各面向等相關參數,以能源密集度最小化、排放密集度最小化、經濟成本最小化作為目標,並將模型模擬基準年設置為2021年,其優化年設置為2025年、2030年、2050年,透過多目標最佳化模擬出各情境下溫室氣體減量措施滲透率的最佳配置,探討設置以大型設備汰換小型設備之措施、節能減排措施包含節能技術措施、可再生能源使用措施、副產物回收再利用措施,並依據各措施之使用率及滲透率進行最佳化分析。
結果顯示,各優化年設備使用率隨大型設備使用率隨優化年增長而上升,而中型設備與小型設備使用率隨之降低,顯示優化年增長大型設備具有優勢,其中煉焦製程之使用率在2030年、2050年維持在70%,這是因為各項設備因有其對應的使用率上限,且隨使用率上升達到設備自身極限時,會維持原最高使用率。根據各優化年節能減排措施滲透率進行分析,結果顯示可再生能源使用措施相較於節能技術措施、副產物回收再利用措施,是最具有潛力發展性,其在2030年之滲透率平均可以增長31%。而各優化年節能成本曲線結果可知,高爐製程之節能技術措施最具有節能貢獻、副產物回收再利用措施具有經濟效益,建議未來可先從高爐製程進行溫室氣體減量措施之優先順位。根據減排成本曲線模擬結果顯示,電爐製程之節能技術措施最具減碳貢獻外,高爐製程之節能技術措施具有經濟效益,建議未來可以優先選擇高爐、電爐製程單元進行減量之優先順位。 最後根據情境分析結果顯示,在不考慮經濟成本情況下,以設備使用率探討降低能源密集度及排放密集度,建議未來可優先選擇高爐製程進行溫室氣體減量之優先順位。以節能措施使用率探討降低能源密集度而言,應以高爐製程單元作為優先溫室氣體減量對象;以降低排放密集度而言,建議應以電爐製程單元作為優先溫室氣體減量對象。 | zh_TW |
dc.description.abstract | Climate change is currently a topic of public attention, and global warming is also an important challenge that cannot be underestimated. In order to deal with climate change, all parts of the world need to work together and adopt effective emission reduction measures and climate adaptation strategies, such as: the United Nations The 26th Conference of the Parties to the Framework Convention on Climate Change called on all signatories to reach net-zero by 2050. For this purpose, Taiwan included the goal of net-zero emissions by 2050 in the Climate Change Response Act.
This study constructs an optimal model for greenhouse gas reduction, taking Taiwan's steel industry as the research area, covering relevant parameters such as environment, economy, technology, and policy aspects, and minimizing energy intensity, emission intensity, and economic costs. As the goal, the model simulation base year is set as 2021, and its optimization years are set as 2025, 2030, and 2050. Through multi-objective optimization, the optimal configuration of the permeability of greenhouse gas reduction measures under each scenario is simulated. This study sets up measures to replace small equipment with large equipment, energy-saving and emission-reduction measures include energy-saving technical measures, renewable energy use measures, by-product recovery and reuse measures, and optimize analysis based on the utilization rate and penetration rate of each measure. According to the results of each optimized annual equipment utilization rate, the utilization rate of large-scale equipment increases with the annual growth of optimization, while the utilization rate of medium-sized and small-scale equipment decreases. , Maintained at 70% in 2050, this is because each device has its corresponding upper limit of usage rate, and when the usage rate rises to the limit of the device itself, the original maximum usage rate will be maintained. According to the analysis of the penetration rate of energy saving and emission reduction measures in each optimized year, the analysis results show that compared with energy saving technical measures and by-product recovery and reuse measures, renewable energy use measures have the most potential for development, and their penetration rates in 2030 are on average Can increase by 31%. According to the results of each optimized annual energy-saving cost curve, it can be seen that the energy-saving technical measures of the blast furnace process have the most energy-saving contribution, and the by-product recovery and reuse measures have economic benefits. According to the simulation results of the emission reduction cost curve, the energy-saving technical measures of the electric furnace process have the greatest contribution to carbon reduction, and the energy-saving technical measures of the blast furnace process have economic benefits. Finally, according to the results of the scenario analysis, discussing the reduction of energy intensity and emission intensity based on the utilization rate of equipment without considering the economic cost, it is suggested that the priority of blast furnace process for greenhouse gas reduction can be selected in the future. In terms of reducing energy intensity based on the utilization rate of energy-saving measures, the blast furnace process unit should be the priority object of greenhouse gas reduction; in terms of reducing emission intensity, it is suggested that the electric furnace process unit should be the priority object of greenhouse gas reduction. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:36:48Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T16:36:48Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 致謝 i
摘要 iii Abstract v 目錄 vii 圖目錄 x 表目錄 xi 第一章 緒論 1 1.1 研究背景 1 1.2 研究目的 2 1.3 研究架構 3 第二章 文獻回顧 5 2.1 鋼鐵業簡介 5 2.1.1 產業背景 5 2.1.2 產業地位 5 2.1.3 產業製程概述 6 2.1.4 產業相關耗能單元概述 7 2.2 臺灣鋼鐵業溫室氣體排放現況 8 2.3 臺灣鋼鐵業淨零轉型之減碳路徑做法 11 2.4 各國鋼鐵業溫室氣體減量之規劃與應用 14 2.5 多目標規劃於鋼鐵業溫室氣體減量之應用 15 2.6 多目標規劃於基因演算法之應用 17 第三章 研究方法 20 3.1 研究流程 20 3.2 範疇界定 21 3.3 鋼鐵業溫室氣體減量最佳化模型之建構 22 3.3.1 決策變數與參數定義 28 3.3.2 目標函數 32 3.3.3 限制條件 38 3.4 模型假設與限制 39 3.5 模型參數蒐集與前處理 39 3.5.1 情境設定 39 3.5.2 最佳化模型相關資料蒐集與前處理 39 3.6 建立減排成本曲線 40 3.7 不確定性分析 41 3.8 敏感度分析 43 3.9 未來情境模擬 43 第四章 結果與討論 44 4.1 多目標最佳化模型 44 4.2 各節能減排措施減排成本結果 51 4.2.1 節能成本曲線結果 51 4.2.2 減排成本曲線結果 53 4.3 不確定性分析結果 56 4.4 敏感度分析結果 59 4.5 情境分析結果 63 第五章 結論與建議 73 5.1 結論 73 5.2 未來研究建議 75 參考文獻 77 附錄一:各製程單元之設備資料 81 附錄二:各製程單元之各節能減排措施資料 84 附錄三:各製程單元設備最佳化結果 88 附錄四:各製程單元節能減排措施最佳化結果 92 附錄五:2025年各節能減排措施之節能成本結果 97 附錄六:2030年各節能減排措施之節能成本結果 102 附錄七:2050年各節能減排措施之節能成本結果 107 附錄八:2025年各節能減排措施之減排成本結果 112 附錄九:2030年各節能減排措施之減排成本結果 117 附錄十:2050年各節能減排措施之減排成本結果 122 附錄十一:能源密集度不確定性分析結果 127 附錄十二:排放密集度不確定性分析結果 129 附錄十三:經濟成本不確定性分析結果 131 附錄十四:能源密集度敏感度分析結果 133 附錄十五:排放密集度敏感度分析結果 136 附錄十六:經濟成本敏感度分析結果 139 附錄十七各優化年之設備情境分析-能源密集度結果 142 附錄十八各優化年之設備情境分析-排放密集度結果 144 附錄十九各優化年之節能措施情境分析節能量結果 146 附錄二十各優化年之節能措施情境分析減排量結果 149 | - |
dc.language.iso | zh_TW | - |
dc.title | 鋼鐵業淨零轉型下溫室氣體減量的綜合最佳化模型 | zh_TW |
dc.title | A Comprehensive Optimization Model for Greenhouse Gas Reduction in the Iron and Steel Industry under the Net-Zero Transition | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 馬鴻文;胡明哲 | zh_TW |
dc.contributor.oralexamcommittee | Hwong-wen Ma;Ming-Che Hu | en |
dc.subject.keyword | 鋼鐵業,溫室氣體減量,節能減排,最佳化分析,碳費, | zh_TW |
dc.subject.keyword | iron and steel industry,greenhouse gas reduction,energy conservation and emission reduction,optimization,carbon price, | en |
dc.relation.page | 151 | - |
dc.identifier.doi | 10.6342/NTU202302682 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-08-08 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 環境工程學研究所 | - |
顯示於系所單位: | 環境工程學研究所 |
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