TY - JOUR
T1 - Integrating Taguchi method and artificial neural network for predicting and maximizing biofuel production via torrefaction and pyrolysis
AU - Aniza, Ria
AU - Chen, Wei Hsin
AU - Yang, Fan Chiang
AU - Pugazhendh, Arivalagan
AU - Singh, Yashvir
N1 - Funding Information:
The authors acknowledge the financial support of the Ministry of Science and Technology, Taiwan, ROC, under contracts MOST 109-2221-E-006-040-MY3, MOST 110-3116-F-006-003-, and MOST 110-2622-E-006-001-CC1 for this research. The authors gratefully acknowledge the use of EM000700 of 110 -2731 -M-006 -001 belonging to the Core Facility Center of National Cheng Kung University.
Funding Information:
The authors acknowledge the financial support of the Ministry of Science and Technology, Taiwan, ROC, under contracts MOST 109-2221-E-006-040-MY3, MOST 110-3116-F-006-003-, and MOST 110-2622-E-006-001-CC1 for this research. The authors gratefully acknowledge the use of EM000700 of 110 -2731 -M-006 -001 belonging to the Core Facility Center of National Cheng Kung University.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/1
Y1 - 2022/1
N2 - Artificial neural network (ANN) is one kind of artificial intelligence in the computing system that aims to process information as the way neurons in the human brain. In this study, the combination of the Taguchi method and ANN are used to maximize and predict biofuel yield from spent mushroom substrate torrefaction and pyrolysis via microwave irradiation. The Taguchi method is utilized to design the multiple factors (particle size, catalyst, power, and magnetic agent) and levels of experiment parameters. The highest total biofuel yield (biochar + bio-oil) is 99.42%, accomplished by a combination of 355 µm particle size, 300 mg·g-SMS-1 catalyst, 900 W power, and 300 mg·g-SMS-1 magnetic agent. ANN with one hidden layer shows the outstanding linear regression predictions for the highest biofuel yields (biochar 0.9999 and bio-oil 0.9998). This high linear regression indicates that ANN with a quick propagation algorithm is an appropriate approach for predicting biofuel conversion.
AB - Artificial neural network (ANN) is one kind of artificial intelligence in the computing system that aims to process information as the way neurons in the human brain. In this study, the combination of the Taguchi method and ANN are used to maximize and predict biofuel yield from spent mushroom substrate torrefaction and pyrolysis via microwave irradiation. The Taguchi method is utilized to design the multiple factors (particle size, catalyst, power, and magnetic agent) and levels of experiment parameters. The highest total biofuel yield (biochar + bio-oil) is 99.42%, accomplished by a combination of 355 µm particle size, 300 mg·g-SMS-1 catalyst, 900 W power, and 300 mg·g-SMS-1 magnetic agent. ANN with one hidden layer shows the outstanding linear regression predictions for the highest biofuel yields (biochar 0.9999 and bio-oil 0.9998). This high linear regression indicates that ANN with a quick propagation algorithm is an appropriate approach for predicting biofuel conversion.
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U2 - 10.1016/j.biortech.2021.126140
DO - 10.1016/j.biortech.2021.126140
M3 - Article
C2 - 34662739
AN - SCOPUS:85117685980
VL - 343
JO - Agricultural Wastes
JF - Agricultural Wastes
SN - 0960-8524
M1 - 126140
ER -