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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.
Pakistan is facing a severe energy crisis due to its heavy dependency on the import of costly fossil fuels, which ultimately leads to expansive electricity generation, a low power supply, and interruptive load shedding. In this regard, the utilization of available renewable energy resources within the country for production of electricity can lessen this energy crisis. Livestock waste/manure is considered the most renewable and abundant material for biogas generation. Pakistan is primarily an agricultural country, and livestock is widely kept by the farming community, in order to meet their needs. According to the 2016–2018 data on the livestock population, poultry held the largest share at 45.8%, followed by buffaloes (20.6%), cattle (12.7%), goats (10.8%), sheep (8.4%), asses (1.3%), camels (0.25%), horses (0.1%), and mules (0.05%). Different animals produce different amounts of manure, based upon their size, weight, age, feed, and type. The most manure is produced by cattle (10–20 kg/day), while poultry produce the least (0.08–0.1 kg/day). Large quantities of livestock manure are produced from each province of Pakistan; Punjab province was the highest contributor (51%) of livestock manure in 2018. The potential livestock manure production in Pakistan was 417.3 million tons (Mt) in 2018, from which 26,871.35 million m3 of biogas could be generated—with a production potential of 492.6 petajoules (PJ) of heat energy and 5521.5 MW of electricity. Due to its favorable conditions for biodigester technologies, and through the appropriate development of anaerobic digestion, the currently prevailing energy crises in Pakistan could be eliminated.
Muhammad Khan; Muhammad Ahmad; Muhammad Sultan; Ihsanullah Sohoo; Prakash Ghimire; Azlan Zahid; Abid Sarwar; Muhammad Farooq; Uzair Sajjad; Peyman Abdeshahian; Maryam Yousaf. Biogas Production Potential from Livestock Manure in Pakistan. Sustainability 2021, 13, 6751 .
AMA StyleMuhammad Khan, Muhammad Ahmad, Muhammad Sultan, Ihsanullah Sohoo, Prakash Ghimire, Azlan Zahid, Abid Sarwar, Muhammad Farooq, Uzair Sajjad, Peyman Abdeshahian, Maryam Yousaf. Biogas Production Potential from Livestock Manure in Pakistan. Sustainability. 2021; 13 (12):6751.
Chicago/Turabian StyleMuhammad Khan; Muhammad Ahmad; Muhammad Sultan; Ihsanullah Sohoo; Prakash Ghimire; Azlan Zahid; Abid Sarwar; Muhammad Farooq; Uzair Sajjad; Peyman Abdeshahian; Maryam Yousaf. 2021. "Biogas Production Potential from Livestock Manure in Pakistan." Sustainability 13, no. 12: 6751.
Food and energy requirements are increasing globally, and the challenge is to meet these demands in a sustainable manner. Oil palm has a relatively high productivity, but produces the lignocellulosic residue of empty fruit bunches (OPEFB). In this study, wet oxidation pretreatment is utilized to overcome the recalcitrance of OPEFB during semi-continuous anaerobic digestion (AD) with between 19.7 and 52.7% improvement over the control, and near total cellulose and hemicellulose content could be degraded. Clarified manure, the water phase of cattle and dairy manure after filtration, is further tested for its effect on methane production by providing necessary micronutrients and vitamins. An increase of 49% was found after addition of clarified manure to OPEFB compared to without this addition.
Jonathan T.E. Lee; Muhammad Usman Khan; Yanjun Dai; Yen Wah Tong; Birgitte K. Ahring. Influence of wet oxidation pretreatment with hydrogen peroxide and addition of clarified manure on anaerobic digestion of oil palm empty fruit bunches. Bioresource Technology 2021, 332, 125033 .
AMA StyleJonathan T.E. Lee, Muhammad Usman Khan, Yanjun Dai, Yen Wah Tong, Birgitte K. Ahring. Influence of wet oxidation pretreatment with hydrogen peroxide and addition of clarified manure on anaerobic digestion of oil palm empty fruit bunches. Bioresource Technology. 2021; 332 ():125033.
Chicago/Turabian StyleJonathan T.E. Lee; Muhammad Usman Khan; Yanjun Dai; Yen Wah Tong; Birgitte K. Ahring. 2021. "Influence of wet oxidation pretreatment with hydrogen peroxide and addition of clarified manure on anaerobic digestion of oil palm empty fruit bunches." Bioresource Technology 332, no. : 125033.