 This paper proposes a new approach for optimizing the performance of thermal energy storage systems using two droplets of ethanol hitting a molten paraffin wax pool. The authors use a combination of high-speed video and infrared thermography to measure the impact parameters such as Weber number, impact spacing, and pool temperature. They then use these measurements to train an artificial neural network, ANN, model which can be used to optimize the performance of the system. Finally, the authors use a genetic algorithm to find the optimal parameters for the system. This article was authored by Shaheen Fagiri, Poram Puruslami, Hadi Pataviyariya, and others.