A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum. (19th March 2018)

• Record Type: Journal Article
• Authors: Al-kaf, Hasan Ali Gamal; Chia, Kim Seng; Alduais, Nayef Abdulwahab Mohammed
• Is Part Of: Petroleum science and technology. Volume 36:Number 6(2018)
• Page Start: 411
• Page End: 418
• Publication Date: 2018-03-19
• Subjects: artificial neural network -- diesel fuel properties -- near infrared spectroscopy -- prediction -- single layer; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2018.1425717
• Languages: English
• ISSNs: 1091-6466

Abstract

ABSTRACT: The implementation of near infrared spectroscopy in monitoring diesel fuel properties is highly dependent on the capability of its predictive model. This study investigates the feasibility of a single layer artificial neural networks among various predictive models in predicting the diesel fuel properties using near infrared spectrum. Results were compared and discussed with that reported in previous studies that used the same data in predicting the diesel fuel properties. Findings show that the proposed single layer outperforms popular models, and is comparable with a recent advanced models in predicting the diesel fuel properties using near Infrared spectrum without data reduction.