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This paper investigated the cooling performance of a high-efficiency dew-point evaporative cooler with optimised air and water flow arrangement using the combined experimental and numerical simulation method. The experimental results showed that the wet-bulb efficiency of the dew-point evaporative cooler was increased by 29.3% and COP was increased by 34.6%, compared to the existing commercial dew point air cooler of the same capacity. An improved two-dimensional, multi-factor engaged numerical model which can scale up and optimize the size and capacity of the cooler was developed. The numerical predictions agreed well with the experimental results, indicating that the cooling rate of the dew-point evaporative cooler is influenced by the dew-point evaporative cooler structure. The cooling efficiency of the dew-point evaporative cooler with corrugated plates is more than 10% higher than with flat plates and the cooling efficiency of the dew-point evaporative cooler with the actual flow arrangement is only 62%–67% that of a dew-point evaporative cooler with an ideal counter-flow arrangement. The cooling efficiency can be improved by increasing the channel length and the air entrance length, and decreasing the channel width and channel gap within a reasonable range. [ABSTRACT FROM AUTHOR]

• A statistical model for a novel dehumidification system is developed. • A new application of Gaussian process regression (GPR) is presented. • Validity of the model for any random operating conditions is investigated. • Several applications of the model in performance prediction of the system are presented. Swift performance assessment of dehumidification systems, in design stage and while operation of the system is of substantial importance for commercialization and wide implementation of this technology. This paper presents a novel statistical model, employing Gaussian Process Regression (GPR) to investigate performance of a solar/waste energy driven dehumidification/regeneration cycle with a solid adsorbent bed. The statistical model takes thousands of operating conditions derived from a numerical model to predict the performance of the system. This predictive tool directly correlates the main operating parameters with the performance parameters of the system. The operating parameters considered in this study are: temperature, relative humidity and flow rate of process air, temperature of regeneration air, length of the desiccant bed, solar radiation intensity and operating time, and the selected performance parameters are: moisture extraction efficiency for the dehumidification cycle and moisture removal efficiency for the regeneration cycle. The model is evaluated by three metrics, namely: root mean square error (RSME), mean absolute percentage error (MAPE), and coefficient of determination (R2). The maximum RSME and MAPE for moisture extraction are only 0.045, 0.21%, and for moisture removal efficiencies are 0.082 and 0.39%, respectively, while the R2 value is derived as 0.97. The developed model is used to investigate the impact of four selected operating parameters on system performance. Additionally, the system performance is predicted for randomly generated operating conditions as well as warm and humid climates. The developed GPR model provides a swift and highly accurate predictive tool for design of the dehumidification systems and for commercialization of the investigated dehumidification systems. [ABSTRACT FROM AUTHOR]