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研究生: 張宸語
Chang, Chen-Yu
論文名稱: 低碳煉鐵製程生產直接還原鐵之程序設計、最適化與生命週期評估
Design, Optimization and Life Cycle Assessment of Low-Carbon Ironmaking Process for Producing Direct Reduced Iron
指導教授: 吳煒
Wu, Wei
學位類別: 碩士
Master
系所名稱: 工學院 - 化學工程學系
Department of Chemical Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 149
中文關鍵詞: 直接還原程序Direct Reduced IronAspen PlusRist Diagram焦爐氣Different Ore GradeMOPSOTOPSISSimaProLCA
外文關鍵詞: Direct Reduced Iron, Aspen Plus, Rist Diagram, Different Ore Grade, MOPSO, TOPSIS, SimaPro, LCA, Direct Reduction Process, Coke Oven Gas
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  • 近年來全球的環保意識逐漸提升,世界上的各個國家也開始規範碳排量,如歐盟的碳稅制度與台灣的2050淨零碳排政策,鋼鐵業作為世界上前幾大的碳排放源,開發減碳方法或設計低碳製程係鋼鐵業必須採取的行動。直接還原程序(Direct Reduction Process)作為新世代的低碳煉鐵製程,可生產直接還原鐵(Direct Reduced Iron, DRI),DRI還可加工為熱壓鐵塊(Hot Briquetted Iron, HBI),HBI則用以降低傳統高爐製程的碳排量。此外,直接還原程序也可與電弧爐(Electric Arc Furnace, EAF)進行整合,進行煉鋼作業。因此,直接還原程序具有相當高的可塑性,被視為未來主流的煉鐵製程之一。
    本研究透過Aspen Plus軟體進行直接還原程序的流程設計,包括氣基豎爐(Gas-Based Shaft Furnace)與外循環裝置。氣基豎爐部分,本研究結合Rist Diagram,用以描述鐵礦還原反應在爐體內部的作用情況。外循環裝置部分主要由洗滌器、重組反應器與燃燒爐組成,用以轉化爐頂氣成為還原氣體。與實廠數據相比,本研究建置的直接還原程序模型之標準化方均根誤差(NRMSE)皆低於5%,證明模型具有一定的可信度。其次,本研究改變天然氣與焦爐氣的用量,執行了靈敏度分析。結果顯示,天然氣最大用量為161.32 kg/tDRI,而焦爐氣最大用量為276.61 kg/tDRI,且兩種氣體皆能使DRI產品的金屬化率提升至100%,碳含量也有小幅度提升,然而生產每噸DRI產品的碳排量亦有些許增加。此外,本研究也執行了高品位與中低品位鐵礦投入直接還原程序的分析,分別探討兩者生產DRI產品的效益。
    接著,本研究藉由多目標粒子群演算法(Multiple-Objective Particles Swarm Optimization, MOPSO)進行模型的最適化分析,並整合理想解相似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution, TOPSIS)對帕雷托前沿(Pareto Frontier)中各式情境的理想度排序,篩選瑞士碳稅制度與波蘭碳稅制度兩種不同方案下的最佳解。最後,本研究利用SimaPro軟體之IMPACT2002+評估方法進行各情境的生命週期評估(Life Cycle Assessment, LCA),分析直接還原程序的環境衝擊程度。觀察圖表後發現,焦爐氣使用量越多的情境,其環境衝擊的數值將會越高。

    The steel industry is responsible for 7-9% of global carbon dioxide emissions, with a significant portion from the blast furnace process. Ironmaking through direct reduction (DR) process with the gas-based shaft furnace is a promising path with lower CO2 emissions. In this study, DR process was modeled using Aspen Plus software, including the gas-based shaft furnace and external circulation units. The study incorporated the Rist Diagram to describe the reduction reactions of iron ore within the reactor.
    A sensitivity analysis was conducted by varying the consumption of natural gas and coke oven gas. The results indicated that the maximum consumption of natural gas was 161.32 kg/tDRI, while coke oven gas reached 276.61 kg/tDRI. Subsequently, model optimization was carried out using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, and TOPSIS principle was applied to rank the Pareto frontier solutions based on their ideality under two different carbon tax schemes: Switzerland and Poland.
    Finally, the IMPACT2002+ method within the SimaPro software was used to perform a Life Cycle Assessment (LCA) for each scenario, evaluating the environmental impact of the DRP. The analysis revealed that scenarios with higher usage of coke oven gas resulted in greater environmental impacts.

    摘要 I Extended Abstract III 誌謝 XV 目錄 XVII 圖目錄 XXI 表目錄 XXV 第一章、緒論 1 1-1 前言 1 1-2 研究背景與動機 3 1-3 研究架構 5 第二章、簡介與文獻回顧 6 2-1 直接還原程序簡介 6 2-1-1 氣基豎爐(Gas-Based Shaft Furnace) 8 2-1-2 外循環裝置 8 2-1-3 操作流程 8 2-2 氣基豎爐模型建置方法 10 2-3 天然氣直接還原程序 13 2-4 焦爐氣直接還原程序 16 2-5 直接還原程序最適化 18 2-6 生命週期評估 19 2-6-1目標與範疇定義(Goal and Scope Definition) 19 2-6-2生命週期清單分析(Life Cycle Inventory, LCI) 19 2-6-3生命週期衝擊評估(Life Cycle Impact Assessment, LCIA) 19 2-6-4闡釋(Interpretation) 20 2-6-5直接還原程序生命週期評估 20 第三章、程序建置方法 22 3-1 模型假設 22 3-2 物性方法 23 3-3 直接還原程序建置 27 3-3-1 直接還原程序流程圖 28 3-3-2 過渡區(Transition zone) 35 3-3-3 Rist Diagram 39 3-3-4 還原區(Reduction zone) 48 3-3-5 冷卻區(Cooling zone) 54 3-3-6 洗滌器(Scrubber) 55 3-3-7 重組反應器(Reformer) 56 3-3-8 燃燒爐(Burner) 60 3-3-9 重組反應器之天然氣與空氣進料量設定 61 3-4 直接還原程序建置流程 62 第四章、結果與討論 63 4-1 模型驗證 64 4-1-1 氣基豎爐驗證 65 4-1-2 外循環裝置驗證 68 4-2 靈敏度分析 70 4-2-1 天然氣流量分析 71 4-2-2 焦爐氣流量分析 78 4-2-3 改變入料鐵礦品位 86 4-3 最適化 93 4-3-1 多目標粒子群優化演算法(Multiple-Objective Particles Swarm Optimization, MOPSO) 96 4-3-2 理想解相似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) 103 4-4 生命週期評估(Life Cycle Assessment) 106 4-4-1 中點指標分析(Midpoint Analysis) 108 4-4-2 末點指標分析(Endpoint Analysis) 112 第五章、結論 114 參考資料 116

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