Microwave-assisted transesterification of Litchi chinensis seed oil using extracted KOH from potato waste for sustainable development

被引:2
|
作者
Asif, Tahir [1 ]
Noor, Fahad [1 ]
Imran, Shahid [1 ]
Mujtaba, M. A. [1 ]
Farooq, Muhammad [1 ]
Fouad, Yasser [2 ]
Kalam, M. A. [3 ]
Uddin, Ghulam Moeen [1 ]
机构
[1] Univ Engn & Technol, Fac Mech Engn, Lahore, Pakistan
[2] King Saud Univ, Coll Appl Engn, Dept Appl Mech Engn, Muzahimiyah Branch, Riyadh, Saudi Arabia
[3] Univ Technol Sydney, FEIT, Sch Civil & Environm Engn, Sydney, NSW, Australia
关键词
Litchi chinensis; biodiesel; potato waste; response surface methodology and artificial neural network; microwave; sustainable environment; RESPONSE-SURFACE METHODOLOGY; BIODIESEL PRODUCTION; DIESEL-ENGINE; NEURAL-NETWORK; SUNFLOWER OIL; RAPESEED OIL; OPTIMIZATION; PALM; FUEL; ANN;
D O I
10.3389/fenrg.2023.1339601
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The fast depletion of conventional fuel supplies has forced the world to find suitable substitutes to overcome the expected energy crisis. Fossil fuels also contribute to global warming because of their harmful emissions. Biofuels are sustainable and environment friendly. Biodiesel can be sourced from both edible and non-edible oils to replace fossil fuels. To avoid a shortage of food supply, it is preferred to produce biodiesel from non-edible oils. In this research, Litchi chinensis seed oil (LSO) is used as a feedstock to synthesize biodiesel employing transesterification using a microwave oven. The catalyst, potassium hydroxide (KOH), used in this research was extracted from potato waste. Sun-dried potato waste was converted into ash. The produced ash is then dissolved in distilled water, leading to a 34% yield of KOH. The transesterification achieves a 92.9% conversion rate under the conditions: 30% microwave power utilization, a catalyst loading of 15% (W/W), a stirring speed of 700 RPM, and a methanol concentration of 70% (V/V) with an 8-min reaction time. Response surface methodology (RSM), in comparison with artificial neural networks (ANNs), has been utilized for the optimization of biodiesel yield, giving efficient results with errors of 0.003% for RSM and 0.005% for ANN. Consequently, the study reports optimized biodiesel yields of 92.9% (experimental), 93.27% (RSM), and 92.40% (ANN). Physicochemical properties such as kinematic viscosity (4.4 mm(2)/s) at 40 degrees C, density (875 kg/m(3)) at 15 degrees C, cetane number (53.2), calorific value (38.8 MJ/kg), flash point (175 degrees C), oxidative stability (6.1 h), and cold flow properties were determined with respect to the ASTM and EN standards. The findings reveal that biofuels primarily support Sustainable Development Goals (SDGs) 7 and 13, with the prime focus on "affordable and clean energy" and "climate action," respectively.
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页数:15
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