Machine learning-based process design of a novel sustainable cooling system

Abstract

An exponential rise in cooling requirements occurred in the last few years because of rising global surface temperatures, population growth, faster urbanization, and income growth. Developing countries are facing major issues because of the larger impact of these cooling drivers. Conventional vapor compression systems are energy-demanding and involve dangerous chemicals. The current paper proposed an innovative indirect evaporative cooling system with high energy performance, less emissions, and chemical-free operation. To map the full-scale performance, a prototype was developed, and tested in a variety of outside air conditions. Then artificial neural network (ANN)-based machine learning model was developed incorporating important input parameters including outdoor air temperature, air flow rate ratio, working air temperature, and working air wet bulb temperature to predict the supply air temperature. The ANN model having nine neurons in the hidden layer exhibits excellent modeling performance with a coefficient of determination (R 2) value of ∼ 1 and root mean square error of 0.046 °C, 0.06 °C, and 0.06 °C in the training, testing, and validation phases respectively. The variable significance analysis carried out by one factor at a time (OFAT) technique reveals that working air inlet temperature is the most important parameter to predict supply temperature with a significance factor of 33 %. According to the combined experimental and ML model, the proposed system generated 130 W of cooling capacity and dropped the temperature by more than 20 °C at 48 °C of outdoor air. The corresponding coefficient of performance achieved (just for cooling) was 32. It is also shown that the enhanced IEC operates steadily in ambient temperatures ranging from 30 to 48 °C and maintains supply air temperatures within the comfort zone of ASHRAE-55 and ISO7730.