Integrating Taguchi method and artificial neural network for predicting and maximizing biofuel production via torrefaction and pyrolysis

Ria Aniza, Wei Hsin Chen, Fan Chiang Yang, Arivalagan Pugazhendh, Yashvir Singh

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號126140
期刊Bioresource technology
343
DOIs
出版狀態Published - 2022 1月

All Science Journal Classification (ASJC) codes

  • 生物工程
  • 環境工程
  • 可再生能源、永續發展與環境
  • 廢物管理和處置

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