Performance evaluation of artificial neural networks in sustainable modelling biodiesel synthesis

Mark Treve, Indrajit Patra, P. Prabu, S. Rama Sree, N. Keerthi Kumar, Yousef Methkal Abd Algani, B. Kiran Bala, S. Balaji

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Biodiesel is a characteristic and inexhaustible homegrown fuel removed from creature fats or vegetable oil and liquor through a transesterification response. The exploration work means to assess the exhibition of biodiesel blend. In this paper, biodiesel was displayed and improved by utilizing a hereditary calculation and Artificial Neural Network (ANN). In AI, hereditary calculations and counterfeit neural organizations assume a significant part in displaying biodiesel blend. To upgrade an excellent arrangement hereditary calculation was created. The mix of ANN and Genetic Algorithm gives the ideal condition as the temperature of methanol molar proportion, impetus fixation. It tentatively decides the exhibition trademark like the Coefficient of determination and Absolute Average deviation (AAD). It predicts the Fatty Acid Methyl Ester (FAME) model productively than Response Surface Methodology (RSM). The exhibition examination is reenacted and hypothetical outcomes are recorded then it is contrasted with constant information to decide the exactness of ANN.

Original languageEnglish
Article number102098
JournalSustainable Energy Technologies and Assessments
StatePublished - Aug 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd


  • Absolute average deviation (AAD)
  • Biodiesel
  • Catalyst concentration
  • Fatty acid methyl ester (FAME)
  • Genetic algorithm
  • Machine learning (ML)
  • Response surface methodology (RSM)
  • Transesterification


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