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研究生: 陳姿宇
Chen, Zih-Yu
論文名稱: 甲醇蒸汽重組產氫與鈀薄膜純化氫氣之設計與研究
Design and study of hydrogen production by methanol steam reforming and hydrogen purification by palladium membrane
指導教授: 陳維新
Chen, Wei-Hsin
學位類別: 碩士
Master
系所名稱: 工學院 - 航空太空工程學系
Department of Aeronautics & Astronautics
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 86
中文關鍵詞: 甲醇蒸汽重組 、水氣轉移反應 、神經網路 、數據分析 、最佳化分析 、Nelder-Mead 、純化氫氣 、氫氣回收率 、鈀薄膜 、真空 、雜質 、出口
外文關鍵詞: Methanol steam reforming (MSR), Water gas shift reaction (WGS), Neural network (NN), Data analysis, Optimization, Nelder-Mead, Hydrogen purification, Hydrogen recovery, Palladium (Pd) membrane, Vacuum, Impurity, Outlets
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  • 在本研究中,開發了與實驗系統等效之甲醇蒸汽重組(MSR)反應器,以數值模擬分析其對製氫的影響,再使用神經網路預測最佳之參數組合。另外,也探討了使用鈀薄膜在不同的情況下,分離氫氣之效果。因此在本研究分為兩個部分,如下所述。
    在本研究中的第一部分,為了研究MSR對產氫的影響,建立了一個數值模型來預測化學反應現象。在本研究中,設計了一種模型,在該模型中,採用一種優化方法(Nelder-Mead)修改蒸汽管長度,以減少模擬和實驗數據之間的誤差,使模型達到等效直徑,並且討論了各種參數(即溫度、水碳比和雷諾數)對甲醇轉化率和氫氣產率的影響。最後,透過神經網絡(NN)分析了各個參數與甲醇轉化率和氫氣產率之間的關係,並預測出最佳的參數組合。修改蒸汽管長度後之模型與實驗數據進行比較後,平均相對誤差從4.31%降至2.46%,其改善率可高達42.69 %。當進料氣體的入口溫度從200°C升高到300°C時,甲醇轉化率和氫氣產率會隨著溫度升高而增加。而當水碳比達到3.5時,甲醇轉化率接近100%。當雷諾數從10增加到50時,甲醇轉化率從100%降低到98.64 %,但氫氣產率從2.69升高到2.76。然而,當雷諾數大於50後,氫氣產率會降低。最後,使用神經網絡分析,根據最大氫氣產率,可知當溫度在300°C,水碳比為3.5,Re為150時,目標函數(甲醇轉化率和氫氣產率)將獲得最佳結果。此外,在此區間內,NN預測與模擬結果之間的最大誤差僅為5.520 %。
    在本研究中的第二部分,鈀(Pd)膜是分離氫氣的關鍵設備,通常在滲透側為常壓下,且僅單出口的情況下操作。本研究取代這些常見的工作條件,將探討滲透側雙出口和單出口分離氫氣的差別,並研究在滲透側施加四個不同的真空度(15-60 kPa)對氫氣回收率之影響。最後,比較不同原料氣混合物(H2 / N2、H2 / CO2和H2 / CO)對Pd膜性能的影響。結果表明,滲透側為單出口或雙出口對氫氣滲透及氫氣回收率並沒有太大的影響。當在滲透物側施加真空時,氫氣滲透率和氫氣回收率有明顯的上升。另外也發現了雜質(即,N2、CO2和CO)對Pd膜具有負面影響,會導致H2滲透率降低,此外,N2與其他兩種氣體相比,對鈀膜之影響最不顯著。

    In this research, a methanol steam reforming (MSR) reactor equivalent to the experimental system was developed, and the influence of hydrogen production was analyzed by numerical simulation, and then using the neural network to predict the best combination of parameters. In addition, the effects of palladium (Pd) membranes separate hydrogen under different conditions is also discussed. Therefore, this study is divided into two parts, as described below.
    In the first part of this study, in order to study the effect of MSR on hydrogen production, a numerical model was developed to predict chemical reaction phenomena. In this research, a model is designed in which an optimization method (Nelder-Mead) is used to modify the steam tube length to reduce the error between the simulation and experimental data, and make the model equivalent diameter to experiment. The effects of various parameters (i.e., temperature, S/C ratio and Reynolds number) on CH3OH conversion and H2 yield. Finally, analyzing the relationship between the parameters of CH3OH conversion and H2 yield through the neural network is conducted and used to predict the best parameter combination. After modifying the geometry of the reactor and comparing it with the experimental data, the average relative error drops from 4.31% to 2.46%, which improved 42.69%. When the inlet temperature of the feed gas increases from 200°C to 300°C, the CH3OH conversion and H2 yield increase with increasing temperature. When the S/C ratio reaches 3.5, the CH3OH conversion is close to 100%. When the Reynolds number increases from 10 to 50, the CH3OH conversion decreases from 100% to 98.64%, but the H2 yield increases from 2.69 to 2.76 mol∙mol CH3OH-1. However, when the Reynolds number is higher than 50, the H2 yield will decrease. Finally, using neural network analysis, when the temperature is 300°C, the S/C ratio is 3.5, and the Re is 150, the objective function (CH3OH conversion and H2 yield) will obtain the best results. In addition, in this interval, the maximum error between the NN prediction and the simulation result is only 5.520%.
    In the second part of this study, Palladium (Pd) membranes are a crucial device for separating hydrogen and are usually operated at normal pressure on the permeate side with a single outlet. Instead of these common operating conditions, the difference between using a double outlet and a single outlet is studied. Four different vacuum degrees (15-60 kPa) are applied on the permeate side, and the results are compared with the non-vacuum operations. Situations under the vacuum and the effects of temperatures (300-400 °C) on H2 permeation are discussed. Finally, the influences of different feed gas mixtures (H2/N2, H2/CO2, and H2/CO) on the Pd membrane performance are investigated. The results show that the single outlet or dual outlets on the permeate side have little effect on hydrogen permeation. When a vacuum is applied to the permeate side, the hydrogen permeability and hydrogen recovery rate increase significantly. In addition, it was also found that impurities (ie, N2, CO2, and CO) have a negative effect on the Pd membrane, which will reduce the H2 permeability. In addition, compared with the other two gases, N2 has the least significant effect on the palladium membrane.

    中文摘要 ii Abstract iv 誌謝 vi Table of Contents vii List of Tables x List of Figures xi Chapter 1 Introduction 1 1.1. Background 1 1.2. Motivation and objectives 4 1.3. A schematics of experimental procedure 5 Chapter 2 Literature Review 7 2.1. MSR reactor system 7 2.2. The membrane system under vacuum on the permeate side 8 Chapter 3 Theory and Methodology 11 3.1. Design and optimization of a methanol steam reforming reactor 11 3.1.1. Geometry model and operating conditions 11 3.1.2. Governing equations and boundary conditions 14 3.1.3. Chemical reactions 17 3.1.4. Numerical method and grid system 18 3.1.5. Optimization method 20 3.1.6. Data analysis 23 3.2. Hydrogen permeation in a palladium membrane tube: Impacts of single and double outlets and vacuum degree 24 3.2.1. Geometry model 24 3.2.2. Governing equations and boundary conditions 26 3.2.3. Properties of gas mixtures 27 3.2.4. Grid system and numerical method 28 3.2.5. Operating conditions and membrane properties 32 Chapter 4 Results and Discussion 34 4.1. Design and optimization of a methanol steam reforming reactor 34 4.1.1. Optimization of the reaction system arrangement 34 4.1.2. Effect of inlet temperatures of the feed gases 37 4.1.3. Effect of S/C ratio 43 4.1.4. Effect of Reynolds number 47 4.1.5. Data analysis in neural network (NN) 50 4.2. Hydrogen permeation in a palladium membrane tube: Impacts of single and double outlets and vacuum degree 55 4.2.1. Velocity, H2 partial pressure, and H2 concentration contours 55 4.2.2. Influence of Reynolds number and the number of outlets 58 4.2.3. Effect of vacuum degree 62 4.2.4. Effect of system temperature 67 4.2.5. Effect of feed gases 71 Chapter 5 Conclusions and Future Works 75 5.1. Conclusions 75 5.2. Future works 77 References 78 自述 85

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