Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub<

The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex...
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Autor*in:	Yutao Zhang [verfasserIn] Xilin Yang [verfasserIn] Chengwei Zhang [verfasserIn] Zhihui Zhang [verfasserIn] An Su [verfasserIn] Yuan-Bin She [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bayesian optimization machine learning reaction optimization photocatalytic reduction design of experiment

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 11(2023), 2614, p 2614
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:2614, p 2614

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr11092614

Katalog-ID:	DOAJ098261215

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520			\|a The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO<sub<2</sub< reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO<sub<2</sub<, partial pressure of H<sub<2</sub<O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH<sub<4</sub<. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction.
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spelling	10.3390/pr11092614 doi (DE-627)DOAJ098261215 (DE-599)DOAJde2a781bc8d1424da416ac10a0464081 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Yutao Zhang verfasserin aut Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub< 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO<sub<2</sub< reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO<sub<2</sub<, partial pressure of H<sub<2</sub<O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH<sub<4</sub<. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction. Bayesian optimization machine learning reaction optimization photocatalytic reduction design of experiment Chemical technology Chemistry Xilin Yang verfasserin aut Chengwei Zhang verfasserin aut Zhihui Zhang verfasserin aut An Su verfasserin aut Yuan-Bin She verfasserin aut In Processes MDPI AG, 2013 11(2023), 2614, p 2614 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:2614, p 2614 https://doi.org/10.3390/pr11092614 kostenfrei https://doaj.org/article/de2a781bc8d1424da416ac10a0464081 kostenfrei https://www.mdpi.com/2227-9717/11/9/2614 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 2614, p 2614
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allfieldsSound	10.3390/pr11092614 doi (DE-627)DOAJ098261215 (DE-599)DOAJde2a781bc8d1424da416ac10a0464081 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Yutao Zhang verfasserin aut Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub< 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO<sub<2</sub< reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO<sub<2</sub<, partial pressure of H<sub<2</sub<O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH<sub<4</sub<. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction. Bayesian optimization machine learning reaction optimization photocatalytic reduction design of experiment Chemical technology Chemistry Xilin Yang verfasserin aut Chengwei Zhang verfasserin aut Zhihui Zhang verfasserin aut An Su verfasserin aut Yuan-Bin She verfasserin aut In Processes MDPI AG, 2013 11(2023), 2614, p 2614 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:2614, p 2614 https://doi.org/10.3390/pr11092614 kostenfrei https://doaj.org/article/de2a781bc8d1424da416ac10a0464081 kostenfrei https://www.mdpi.com/2227-9717/11/9/2614 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 2614, p 2614
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authorswithroles_txt_mv	Yutao Zhang @@aut@@ Xilin Yang @@aut@@ Chengwei Zhang @@aut@@ Zhihui Zhang @@aut@@ An Su @@aut@@ Yuan-Bin She @@aut@@
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callnumber-first	T - Technology
author	Yutao Zhang
spellingShingle	Yutao Zhang misc TP1-1185 misc QD1-999 misc Bayesian optimization misc machine learning misc reaction optimization misc photocatalytic reduction misc design of experiment misc Chemical technology misc Chemistry Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub<
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topic_title	TP1-1185 QD1-999 Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub< Bayesian optimization machine learning reaction optimization photocatalytic reduction design of experiment
topic	misc TP1-1185 misc QD1-999 misc Bayesian optimization misc machine learning misc reaction optimization misc photocatalytic reduction misc design of experiment misc Chemical technology misc Chemistry
topic_unstemmed	misc TP1-1185 misc QD1-999 misc Bayesian optimization misc machine learning misc reaction optimization misc photocatalytic reduction misc design of experiment misc Chemical technology misc Chemistry
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title	Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub<
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title_sort	exploring bayesian optimization for photocatalytic reduction of co<sub<2</sub<
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title_auth	Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub<
abstract	The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO<sub<2</sub< reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO<sub<2</sub<, partial pressure of H<sub<2</sub<O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH<sub<4</sub<. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction.
abstractGer	The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO<sub<2</sub< reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO<sub<2</sub<, partial pressure of H<sub<2</sub<O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH<sub<4</sub<. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction.
abstract_unstemmed	The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO<sub<2</sub< reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO<sub<2</sub<, partial pressure of H<sub<2</sub<O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH<sub<4</sub<. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction.
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title_short	Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub<
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Exploring Bayesian Optimization for Photocatalytic Reduction of CO<sub<2</sub<

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