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研究生：	黃偉綸 Huang, Wei-Lun
論文名稱：	貝氏優化導引設計含鎳層狀矽酸鹽衍生之氧化矽擔載鎳觸媒 Bayesian Optimization-Guided Design of Silica-Supported Nickel Catalysts from Nickel Phyllosilicates
指導教授：	林裕川 Lin, Yu-Chuan
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：	英文
論文頁數：	103
中文關鍵詞：	貝氏優化、主動式學習、鎳層狀矽酸鹽、氫化反應
外文關鍵詞：	Bayesian optimization, Active learning, Nickel phyllosilicate, Hydrogenation
相關次數：	點閱：12 下載：1
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本研究提出一種結合貝氏優化（Bayesian Optimization, BO）的策略，用於優化由還原鎳層狀矽酸鹽（rNiPS）衍生的Ni/SiO₂催化劑的合成過程。透過系統性地調整關鍵合成變數——包括鍛燒溫度與時間，以及還原溫度與時間——我們旨在促進析出Nix+與 Ni0的形成，這些皆為將乙醯丙酸（Levulinic Acid, LA）轉化為γ-戊內酯（γ-Valerolactone, GVL）的活性位點。本研究首先合成了15種在不同條件下製備的rNiPS樣品（rNiPS-1至rNiPS-15），並利用基於高斯過程回歸（Gaussian Process Regression, GPR）的貝氏優化，僅用三個優化循環便高效鎖定了最佳合成條件。這些條件成功提高了最佳催化劑（rNiPS-18）中 Nix+（x ≈ 1.66）總濃度以及Ni0/10的含量，相較於基準樣品有明顯提升。對rNiPS-18進一步的表徵分析顯示，其孔隙結構、結晶性、還原性、表面酸性及鎳的局部配位環境均與基準樣品存在顯著差異，尤其體現為較高比例的Nix+和較低含量的Ni0。值得注意的是，rNiPS-18在LA氫化反應中的轉化率（Turnover Frequency, TOF）提升近50%，凸顯了貝氏優化在理性設計富含催化活性物種的鎳基催化劑中的應用價值。

In this work, we present a Bayesian Optimization (BO)-assisted strategy for optimizing the synthesis of Ni/SiO₂ catalysts derived from reduced nickel phyllosilicate (rNiPS). By systematically adjusting key synthesis variables—namely calcination temperature and time, as well as reduction temperature and time—we aimed to enhance the formation of exsolved Nix+ species and Ni0 nanoparticles, which serve as active sites for converting levulinic acid (LA) into γ-valerolactone (GVL). Starting with 15 rNiPS samples (rNiPS-1 to -15) synthesized under varying conditions, Gaussian Process Regression-based BO enabled us to efficiently pinpoint optimal synthesis conditions within just three optimization cycles. These conditions led to an increase in the total concentration of Nix+ (x ≈ 1.66) and Ni0/10 in the best-performing catalyst (rNiPS-18), relative to the benchmark. Further characterization of rNiPS-18 revealed notable differences in porosity, crystallinity, reducibility, surface acidity, and the local coordination environment of Ni, with a higher proportion of Nix+ and a reduced amount of Ni0 compared to the reference sample. Importantly, rNiPS-18 exhibited nearly 50% higher turnover frequency in LA hydrogenation, highlighting the value of BO in the rational design of Ni-based catalysts enriched with catalytically active species.

摘要	i
Abstract	ii
Acknowledgements	iii
Table of Contents	iv
List of Tables	vii
List of Figures	viii
Chapter 1 Preface	1
1-1 Introduction	1
1-2 Research Motivation and Design	2
Chapter 2 Literature Review	3
2-1 Overview of Bayesian Optimization	3
2-2 Gaussian Process and Gaussian Process Regression	5
2-2-1 Gaussian Process	5
2-2-2 Gaussian Process Regression	6
2-3 Bayesian Optimization with Acquisition Function	7
2-4 Applications of Bayesian Optimization	10
2-5 Applications of Biomass-Derived Hydrogenation Catalysis	13
2-5-1 Research on the Hydrogenation of LA to GVL	14
2-6 Introduction to Phyllosilicates	16
2-7 Applications of Phyllosilicates in Catalytic Catalysis	17
Chapter 3: Experiment	20
3-1 X-ray Diffraction	20
3-2 Nitrogen Physisorption Analysis	22
3-3 Fourier-Transform Infrared Spectroscopy	24
3-4 Automated Chemical Adsorption/Desorption Analyzer	27
3-4-1 Hydrogen Temperature-Programmed Reduction	28
3-4-2 Ammonia Temperature-Programmed Desorption	29
3-4-3 Carbon Monoxide Pulse Chemisorption	30
3-5 X-ray Absorption Spectroscopy	31
3-6 Gas Chromatograph	32
3-7 Catalyst Preparation	33
3-7-1 Catalyst Naming	34
3-8 Catalytic Activity Test	35
3-9 Product Qualitative and Quantitative Analysis	37
3-10 Intrinsic Activity Calculation	38
3-11 Lab Instruments and Chemicals	39
3-12 Objective Function Formulation and Variable Selection for Bayesian Optimization Modeling	41
3-12-1 Mechanism for the Conversion of LA to GVL	43
3-13 Experimental Workflow and Optimization Strategy	44
3-13-1 Design of Experiment	44
3-13-2 Gaussian process regression	44
3-13-3 Bayesian Optimization and Expected Improvement	45
3-13-4 Model Convergence and Validation of Optimal Catalyst Conditions	46
Chapter 4 Results and Discussion	48
4-1 X-ray Diffraction Analysis of Catalysts	48
4-2 Surface Area and Pore Size Distribution of Catalysts	50
4-3 Pyridine-IR Analysis of Catalysts	53
4-4 H₂-Temperature Programmed Reduction of Catalysts	55
4-5 NH₃-Temperature Programmed Desorption of Catalysts	56
4-6 Calculation of Catalytic Active Sites	58
4-7 Active Learning-Based Experimentation Using GPR and EI	60
4-7-1 Leave-One-Out Cross-Validation	63
4-7-2 Accuracy Test	64
4-7-3 3D Surfaces Before and After Optimization	69
4-7-4 Calibration Curve	70
4-7-5 Partial Dependence Plot	72
4-7-6 Sensitivity Analysis	76
4-8 X-ray Absorption Spectroscopy	77
4-9 Catalytic Activity Evaluation	80
Chapter 5 Conclusion	82
Chapter 6 References	83
                                    

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