Exploring Bayesian Optimization for Photocatalytic Reduction of CO2
Abstract
:1. Introduction
2. Methods and Models
2.1. Optimization Methods
2.1.1. Bayesian Optimization
2.1.2. Surrogate Model
2.1.3. Acquisition Function
2.1.4. Algorithm
- (1)
- Begin with t = 1;
- (2)
- Pre-sample, build initial samples [38], train and update the chosen surrogate model , ;
- (3)
- For i = 1, 2,..., measure the CO2 photocatalytic properties represented by known parameter values [39] (partial pressure and/or deactivation time h) ;
- (4)
- Maximize the acquisition function to determine the next evaluated process parameter value : ;
- (5)
- Evaluate the objective function value ;
- (6)
- Fitting data: , update the probability surrogate model;
- (7)
- Update t = t + 1;
- (8)
- BO actively iterates increases from t times to N times in the feedback loop until it finds the optimal global value (see Equation (7)).
2.1.5. Design of Experiments (DOE)
2.1.6. Langmuir–Hinshelwood (L-H) Mechanism
2.2. Kinetic Models
2.2.1. L-H-Based Kinetic Model
2.2.2. A Probabilistic L-H-Based Dynamic Kinetic Model
2.3. CO2 Photoreduction Kinetic Model
2.3.1. Tan
2.3.2. Khalilzadeh
2.3.3. Thompson
3. Results
3.1. Comparing Different Traditional DOE Methods in Two-Dimensional Space
3.2. Comparing BO and DOE-OLHS
3.3. Investigating the Effects of Different Combinations of Surrogate Models and Acquisition Functions
3.4. Optimization of Product Selectivity including Catalyst Deactivation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Kinetic Model Name | Catalyst | Catalyst Shape | Reaction Time (h) | Photoreactor | Type of Light Source |
---|---|---|---|---|---|
Tan [2] | 5GO-OTiO2 | Yellowish solid powder, binary nanocomposites, hybrid heterostructures | 8 | Continuous gas flow reactor | Xenon arc lamp |
Khalilzadeh [3] | 0.12%Fe-0.5%N/ TiO2 | Nanoparticles, crystal structure | 1 | Pyrex vessel and quartz tube | 70W mercury lamp |
Thompson [5] | P25 TiO2 | Coating method, pure and cracks, similar coverage | 5 | Photo differential photoreactor | OmniCure S2000 |
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Zhang, Y.; Yang, X.; Zhang, C.; Zhang, Z.; Su, A.; She, Y.-B. Exploring Bayesian Optimization for Photocatalytic Reduction of CO2. Processes 2023, 11, 2614. https://doi.org/10.3390/pr11092614
Zhang Y, Yang X, Zhang C, Zhang Z, Su A, She Y-B. Exploring Bayesian Optimization for Photocatalytic Reduction of CO2. Processes. 2023; 11(9):2614. https://doi.org/10.3390/pr11092614
Chicago/Turabian StyleZhang, Yutao, Xilin Yang, Chengwei Zhang, Zhihui Zhang, An Su, and Yuan-Bin She. 2023. "Exploring Bayesian Optimization for Photocatalytic Reduction of CO2" Processes 11, no. 9: 2614. https://doi.org/10.3390/pr11092614