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研究生: 黃證銘
Huang, Cheng-Ming
論文名稱: 機器學習輔助水熱液化法建模用於微藻精煉廠最佳化設計
Machine learning-assisted hydrothermal liquefaction modeling for optimal design of microalgae biorefinery
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: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 107
中文關鍵詞: 微藻水熱液化法綠色柴油機器學習最佳化MINLP
外文關鍵詞: Microalgae, Hydrothermal Liquefaction, Green Diesel, Machine Learning, Optimization
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  • 隨著全球環境問題的日益嚴重,在未來幾十年發展乾淨和可再生能源技術以實現碳中和已成為全球共識,而微藻因有以下特點被視為極具潛力的可再生能源原料,其具有生長快速、1公斤的藻類生物質可以吸收1.83公斤的CO2、可生長於非耕作用地及較嚴苛的環境中等特性,而微藻透過水熱液化法生產生質柴油的主要優勢是該技術不需要對藻類生物質進行能源密集型脫水和乾燥,因此本研究著重於微藻水熱液化法建模,包含結合最近已被用於各種生物燃料生產系統以促進技術發展的機器學習技術,建立生植物水熱液化法資料集,並以微藻組成 (C、H、O、N、ash)和操作條件(溫度、壓力、反應時間、)變量作為輸入,以預測產油量,生物油高熱值(HHV)資訊,在產油量的預測模型中LSBoost和ANN預測模型有較佳的準確率,模型測試數據集R2分別達0.95和0.94;在高熱值(HHV)的預測模型LSBoost模型獲得最佳的準確率,訓練數據集和測試數據集R2分別為0.98和0.93。
    再進一步以Aspen plus進行程序模擬微藻水熱液化法,而生物油需經加氫脫氧製程以提高燃料特性,如熱值、油品穩定性和黏度,目標為生產與現有石油產品機械相容的液體燃料,因此該模型包括熱液液化、生物原油分離、原油加氫脫氧步驟,並進行經濟評估及排碳量計算,主要基於設備、材料和能源消耗進行分析,最後計算出單位生產成本為每公斤微藻綠色柴油0.774美元,投資回收期為6.22年IRR則為19.39%。最終整合微藻生物精煉框架內製程資料和微藻前處理可行的替代方案,建構一個微藻精煉的上層結構圖,將其描述為一個MINLP問題以GAMS求解器求解,選擇四種不同的目標函數(最大化收益、二氧化碳排放最小化、平衡收益和碳排放量以及生質油產量最大化),建立不同情境下的最佳化生產流程。

    This study focuses on the modeling of microalgal hydrothermal liquefaction. First establish a machine learning prediction model for hydrothermal liquefaction. The model is based on microalgal composition (C, H, O, N, ash) and operating conditions (temperature, pressure, reaction time) variables as input to predict oil yield and bio-oil high heating value (HHV) information. Furthermore, Aspen plus is used to simulate the microalgae hydrothermal liquefaction method. Finally, the process data in the microalgae biorefinery framework and the feasible alternatives for microalgae pretreatment are integrated, and a MINLP model based on the microalgae refining superstructure is constructed. The MINLP problem is solved by using the GAMS solver, and four different target functions (Maximize revenue, minimize CO2 emissions, balance revenue and carbon emissions, and maximize biomass production).
    For oil yield forecasting case, the LSBoost and ANN method can achieve a higher accuracy with R2 of 0.954 and 0.946 on the test dataset, respectively. For HHV case, the LSBoost method obtain a higher accuracy the test dataset R2 is 0.93, and the RMSE is 1.8729. On the results of economic analysis and life cycle analysis the production cost per kilogram green diesel is 0.774USD and the unit equivalent carbon dioxide emission for producing green diesel is 0.0533 (kg eCO2/MJ). The optimized microalgal refinery design for 4 different objective functions is presented in the last part.

    摘要 I Extended Abstract III 誌謝 XIII 目錄 XV 圖目錄 XIX 表目錄 XXII 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目標 3 1.3研究架構 5 第二章 文獻回顧 7 2.1 微藻簡介 7 2.2 微藻培養及前處理 9 2.2.1 培養 9 2.2.2 收穫 10 2.2.3 除水 10 2.2.4 破藻 10 2.2.5 萃取 11 2.2.6 水解 11 2.3 水熱液化法簡介 12 2.4 機器學習簡介 15 2.5 其他微藻製程簡介 17 2.5.1 轉酯化 17 2.5.2 ABE發酵 18 2.5.3 熱裂解 19 2.5.4 厭氧消化 20 2.6 經濟評估簡介 21 2.6.1 設備成本 21 2.6.2 操作成本 22 2.6.3 折舊 22 2.7 生命週期評估 24 第三章 水熱液化法模型建立 26 3.1 機器學習模型建立 26 3.1.1 資料收集和預處理 26 3.1.2 模型訓練和評估 28 3.1.3 模型選擇 29 3.1.4 超參數調整 31 3.2 產油量預測結果 33 3.2.1 集成學習(ensemble)模型結果 33 3.2.2 人工神經網絡(ANN)模型結果 35 3.2.3 支持向量回歸(SVR)模型結果 37 3.2.4 分析 40 3.3 高熱值(HHV)預測結果 43 3.3.1集成學習(ensemble)模型結果 43 3.3.2 人工神經網絡(ANN)模型結果 45 3.3.3 支持向量回歸(SVR)模型結果 46 3.2.4 分析 48 3.4 水熱液化法製程建立 50 3.5 經濟分析 61 3.6 生命週期分析 66 第四章 微藻精煉最佳化設計 68 4.1 MINLP 68 4.2 微藻精煉超結構建立 71 4.2.1 上層結構開發與製程描述 72 4.2.3 技術選項說明 74 4.3 MINLP模型建立 80 4.3.1 問題描述 80 4.3.2 命名法 80 4.3.3 數學模型 82 4.4 結果 85 第五章 結論與建議 96 參考文獻 98 附錄(A) 生物油分布計算邏輯 105 附錄(B)  技術選項編號 106 附錄(C)  原物料價格 107

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