Deep learning-based optimization of a hydrogen and oxygen production system for patients in hospital with alkaline electrolyzer

Abstract

The hybrid renewable system's potential to create standard type E capsules for Covid-19 patients was explored in this study. In addition to delivering the requisite energy to the building, standard oxygen capsules were produced using the electrolysis of water using nanomaterial-supported electrolysis in the hydrogen storage system. In addition to the simulation, multi-objective optimization was done using a deep learning neural network and a genetic algorithm to maximize the number of oxygen capsules generated in a year and the system price, and the system's front beam was acquired. The system can produce 19530 units of type E oxygen capsules in a year, and the price of the electrolyzer and fuel cell is 120296 Euros at the best point of the front beam, considering both the objective variable of price and the number of produced oxygen capsules. In this scenario, the electrolyzer and fuel cell have rated powers of 61.9 kW and 15.3 kW, respectively. After determining the optimal point, researchers investigatedthe connection between meteorological data and other system characteristics including the amount of hydrogen in the tank, the number of oxygen capsules generated each hour, fuel cell power, and the electrolyzer. Lastly, the system's capacity to lower the amount of power required for the office building from the municipal network was investigated, indicating the system's excellent capability in this respect.