Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems

Hafiz M Asfahan,Redmond R Shamshiri,Muhammad Sultan,Muhammad Usman Khan,Mubasher Ali,Uzair Sajjad,Imtiyaz Hussain,Chi-Chuan Wang,Khalid Hamid

doi:10.3390/en14133946

Abstract

The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Toutdb, wout, and Eoutair). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.

Highlights

The development of low-cost, energy-efficient air-conditioning (AC) systems or devices has received noticeable attention, especially in developing countries due to the fuel poverty [1], energy scarcity [2], and inherently perceived environmental problems [3]
Data data normalization, normalization, data data visualization, visualization,and andevaluaevaluIn this this section, section, the the data data collection, the data collection, data normalization, data visualization, and evaluation theDEC, direct evaporative cooling (DEC),indirect evaporative cooling (IEC), IEC, and systems accomplished through feature engineering tion ofofthe and systems areare accomplished through feature engineering and ation of the DEC, IEC, and Maisotsenko evaporative cooling (MEC) systems are accomplished through feature engineering and are provided
An artificial intelligence (AI)-based deep learning (DL) algorithm was developed in order to assess the thermal performance of evaporation-assisted air conditioning (E-AC) systems

Summary

Introduction

The development of low-cost, energy-efficient air-conditioning (AC) systems or devices has received noticeable attention, especially in developing countries due to the fuel poverty [1], energy scarcity [2], and inherently perceived environmental problems [3]. Mechanical vapor compression (MVC) based AC (MVCAC) systems are extensively used in residential and commercial buildings to maintain living comfort with respect to the temperature. Researchers have employed various adsorbents to evaluate their applicability in TVC systems. In this context, silica gel has been extensively studied as an adsorbent. It was reported that hydrophilic polymeric sorbents surpassed silica gel in dehumidification applications [16]. These materials are regenerated at high temperatures (~70–95 ◦ C) [10]. A substantial amount of heat is required to regenerate the adsorbent bed, stressing the need to use renewable energy sources

Objectives

Methods

Results

Conclusion