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    Analysis of CO selectivity during electroreduction of CO2 in deep eutectic solvents by machine learning
    (Springer, 2023) Guenay, M. Erdem; Tapan, N. Alper
    In this work, supervised and unsupervised machine learning approaches were applied to determine routes to high CO selectivity during the electroreduction of CO2 in deep eutectic solvents (DES) utilizing the molecular, chemical, and physical characteristics of hydrogen bond donors and acceptors, as well as the properties of different electrodes and DES solvents. In addition, effective data visualization and machine learning techniques were employed to identify relationships between descriptor variables and CO faradaic efficiency. First, SHAP (Shapley Additive exPlanations) analysis was applied to determine the positive and negative effects of the descriptor variables on the target, and it was found that urea in HBD (hydrogen bond donor) has the greatest impact on the target. Then, principal component analysis (PCA) was used to identify the combinations that lead to low, medium, and high levels of the target. PCA indicated that high-level clusters may be linked with HBA (hydrogen bond acceptor) molecular properties rather than HBD in addition to choline chloride-type HBA, HBA/HBD ratio, HBD density, HBD melting point, and urea-type HBD. Finally, decision tree classification was used to discover the variables leading to very high levels of the target. The decision tree revealed one pathway with very high CO faradaic efficiency and two pathways with high CO faradaic efficiency. To conclude, future researchers will be able to design new experiments with less effort and time while analyzing the effect of new DES components for high-performance CO2 electrolyzers as a result of the machine learning study and exploratory data analysis performed in this study.
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    Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators
    (Mdpi, 2024) Aksoez, Zafer Yavuz; Guenay, M. Erdem; Aziz, Muhammad; Tunc, K. M. Murat
    In this work, the design features of delta wing vortex generators (DWVGs) on the thermo-hydraulic performance of heat exchangers are investigated using machine learning. Reynolds numbers, attack angle, length, wing-to-width ratio, and relative pitch ratio of DWVGs were used as descriptor variables, with Nusselt numbers, friction factors, and performance evaluation criterion (PEC) serving as target variables. Decision tree classification revealed the pathways leading to high or low values of the performance variables. Among many of those pathways, it was found that high Reynolds numbers (between 8160 and 9800) and high attack angles (greater than or equal to 47.5 degrees) lead to high Nusselt numbers. On the other hand, an attack angle between 41 degrees and 60 degrees, a Reynolds number less than 8510, and a wing-to-width ratio greater than or equal to 0.4 causes a high friction factor. Finally, the PEC is likely to enhance when the Reynolds number is higher than or equal to 10,300 and the attack angle is between 47.5 degrees and 60 degrees. In addition to the decision tree analysis, SHapley Additive exPlanations (SHAP) analysis (a part of explainable machine learning) was also applied to reveal the importance of design features and their positive and negative effects on the target variables. For example, for a Nusselt number as the target variable, the Reynolds number was found to be the most influential variable, followed by the attack angle and the relative pitch ratio, all of which had a positive impact on the target. It was then concluded that machine learning methods could help provide strong insights into the configuration design features of heat exchangers in DWVGs to improve their efficiency and save energy.