Adaptive Neural Architecture Search Using Meta-Heuristics: Discovering Fine-Tuned Predictive Models for Photocatalytic CO 2 Reduction

Ümit Işıkdağ, Gebrail Bekdaş (), Yaren Aydın, Sudi Apak, Junhee Hong and Zong Woo Geem ()
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Ümit Işıkdağ: Department of Architecture, Mimar Sinan Fine Arts University, 34427 Istanbul, Turkey
Gebrail Bekdaş: Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey
Yaren Aydın: Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey
Sudi Apak: Department of Industrial Engineering, İstanbul Esenyurt University, 34510 Istanbul, Turkey
Junhee Hong: College of IT Convergence, Gachon University, Seongnam 13120, Republic of Korea
Zong Woo Geem: College of IT Convergence, Gachon University, Seongnam 13120, Republic of Korea

Sustainability, 2024, vol. 16, issue 23, 1-29

Abstract: This study aims to contribute to the reduction of carbon dioxide and the production of hydrogen through an investigation of the photocatalytic reaction process. Machine learning algorithms can be used to predict the hydrogen yield in the photocatalytic carbon dioxide reduction process. Although regression-based approaches provide good results, the accuracy achieved with classification algorithms is not very high. In this context, this study presents a new method, Adaptive Neural Architecture Search (NAS) using metaheuristics, to improve the capacity of ANNs in estimating the hydrogen yield in the photocatalytic carbon dioxide reduction process through classification. The NAS process was carried out with a tool named HyperNetExplorer, which was developed with the aim of finding the ANN architecture providing the best prediction accuracy through changing ANN hyperparameters, such as the number of layers, number of neurons in each layer, and the activation functions of each layer. The nature of the NAS process in this study was adaptive, since the process was accomplished through optimization algorithms. The ANNs discovered with HyperNetExplorer demonstrated significantly higher prediction performance than the classical ML algorithms. The results indicated that the NAS helped to achieve better performance in the estimation of the hydrogen yield in the photocatalytic carbon dioxide reduction process.

Keywords: photocatalytic; hydrogen; machine learning; hyperparameter optimization; classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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