Performance Assessment of Thermally-Driven Adsorption Cooling Systems

This study investigates the dynamic performance of thermally-driven adsorption cooling systems using a factorial design of experiments. A MATLAB code is developed to study the temporal behavior of the system's primary components. System performance is analyzed through analysis of variance (ANOVA) and optimized using the desirability method, focusing on operational conditions relevant to solar energy and waste heat recovery applications. Regression models for cooling capacity (CC) and coefficient of performance (COP) are developed, allowing accurate predictions across a range of input variables. These models incorporate key parameters, including the inlet temperatures of hot, cooling, and chilled water, as well as adsorption, desorption, and switching times. A multi-objective optimization is conducted to maximize CC and COP simultaneously, yielding optimal values of 20.9 kW and 58.9%, respectively, within the studied parameter space. These findings offer valuable insights into enhancing the efficiency of adsorption cooling systems under varying operating conditions.