Abstract
In this work, we have implemented a method which uses structural information from 1H NMR spectra of fatty esters and biodiesels to infer the corresponding Cetane Number (CN). The method consists of the successive application of Principal Component Analysis (PCA), Fuzzy Clustering and a feed-forward Artificial Neural Network (ANN) to the data set. PCA recognized redundant information, and determined the number of clusters for subsequent Fuzzy Clustering classification. At the final stage ANN used membership values from the Fuzzy Clustering process as inputs to predict the cetane number of different types of biodiesel (complex mixtures) from data of pure substances (esters). Root-mean-square deviations were in the range of 0.2–2.4.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
