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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 (
Hafiz Asfahan; Uzair Sajjad; Muhammad Sultan; Imtiyaz Hussain; Khalid Hamid; Mubasher Ali; Chi-Chuan Wang; Redmond Shamshiri; Muhammad Khan. Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems. Energies 2021, 14, 3946 .
AMA StyleHafiz Asfahan, Uzair Sajjad, Muhammad Sultan, Imtiyaz Hussain, Khalid Hamid, Mubasher Ali, Chi-Chuan Wang, Redmond Shamshiri, Muhammad Khan. Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems. Energies. 2021; 14 (13):3946.
Chicago/Turabian StyleHafiz Asfahan; Uzair Sajjad; Muhammad Sultan; Imtiyaz Hussain; Khalid Hamid; Mubasher Ali; Chi-Chuan Wang; Redmond Shamshiri; Muhammad Khan. 2021. "Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems." Energies 14, no. 13: 3946.
In Pakistan, many subsurface (SS) drainage projects were launched by the Salinity Control and Reclamation Project (SCARP) to deal with twin problems (waterlogging and salinity). In some cases, sump pumps were installed for the disposal of SS effluent into surface drainage channels. Presently, sump pumps have become dysfunctional due to social and financial constraints. This study evaluates the alternate design of the Paharang drainage system that could permit the discharge of the SS drainage system in the response of gravity. The proposed design was completed after many successive trials in terms of lowering the bed level and decreasing the channel bed slope. Interconnected MS-Excel worksheets were developed to design the L-section and X-section. Design continuity of the drainage system was achieved by ensuring the bed and water levels of the receiving drain were lower than the outfalling drain. The drain cross-section was set within the present row with a few changes on the service roadside. The channel side slope was taken as 1:1.5 and the spoil bank inner and outer slopes were kept as 1:2 for the entire design. The earthwork was calculated in terms of excavation for lowering the bed level and increasing the drain section to place the excavated materials in a specific manner. The study showed that modification in the design of the Paharang drainage system is technically admissible and allows for the continuous discharge of SS drainage effluent from the area.
Muhammad Imran; Jinlan Xu; Muhammad Sultan; Redmond Shamshiri; Naveed Ahmed; Qaiser Javed; Hafiz Asfahan; Yasir Latif; Muhammad Usman; Riaz Ahmad. Free Discharge of Subsurface Drainage Effluent: An Alternate Design of the Surface Drain System in Pakistan. Sustainability 2021, 13, 4080 .
AMA StyleMuhammad Imran, Jinlan Xu, Muhammad Sultan, Redmond Shamshiri, Naveed Ahmed, Qaiser Javed, Hafiz Asfahan, Yasir Latif, Muhammad Usman, Riaz Ahmad. Free Discharge of Subsurface Drainage Effluent: An Alternate Design of the Surface Drain System in Pakistan. Sustainability. 2021; 13 (7):4080.
Chicago/Turabian StyleMuhammad Imran; Jinlan Xu; Muhammad Sultan; Redmond Shamshiri; Naveed Ahmed; Qaiser Javed; Hafiz Asfahan; Yasir Latif; Muhammad Usman; Riaz Ahmad. 2021. "Free Discharge of Subsurface Drainage Effluent: An Alternate Design of the Surface Drain System in Pakistan." Sustainability 13, no. 7: 4080.