Deep learning artificial intelligence framework for sustainable desiccant air conditioning system: Optimization towards reduction in water footprints

Desiccant evaporative cooling systems pave the path towards energy and environmental sustainability in buildings especially; however, the direct evaporative coolers in such configurations result in high water consumption. The application of modern computational intelligence tools, including artificial intelligence and meta-heuristic optimization algorithms, can improve the operational comprehension of desiccant cooling systems while addressing the minimization of total water footprints with the maximization of the cooling capacity. The contribution/objective of this research is to address the gaps in understanding through the application of deep learning, genetic algorithm, and multicriteria decision analysis applied to a desiccant cooling system working under real transient experimental conditions of a building located in Austria. Within the methodology, calibrated, experimental, and validated data monitoring system displaying the real desiccant-enhanced cooling system is adapted to generate a set of input-output data sets. The set of data includes ambient temperature, ambient humidity, regeneration temperature, supply airflow rate, and return airflow rate yielding the cooling capacity and total water footprints of the system. The results of deep learning algorithm using an artificial neural network have suggested that the architectures 5-[6]-[6]-1 and 5-[12]-[12]-1 are the best to accurately predict the cooling capacity and total water footprints with a coefficient of determination of 0.98856 and 0.99246, respectively. Secondly, the “white-box model” of the deep learning algorithm is used to develop a digital twin model which helps in the replication of the earlier experimental conditions. The optimization results have suggested that the optimized total water footprints are 45.17 kg/h with a system of 3.32 tons of refrigeration. These optimal values are found in the best combination of design variables in which the ambient temperature is 28 °C, ambient relative humidity is 52.0%, supply airflow rate is 2.13 kg/s, and regeneration flow rate is 2.35 kg/s, and the regeneration temperature is 70.0 °C. It is concluded that the application of data-driven models can extend the interpretation of desiccant cooling systems and can participate in its performance enhancement.