Application of Genetic Algorithm for Increasing the Utilization of Poor-quality Biodiesel through Blending

Biodiesel is a viable alternative to petroleum diesel. The properties of the biodiesel depend on the feedstock used to produce it. A significant difference in properties exists between different biodiesels. Therefore, standards for biodiesel fuel had been developed considering many factors such as s...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Oleo Science 2021, Vol.70(8), pp.1039-1050
Main Authors: Adhikesavan, Chellachamy, Balamurugan, Chinnasamy, Ganesh, Duraisamy
Format: Article
Language:English
Subjects:
biodiesel blending
Biodiesel fuels
biodiesel quality
biodiesel standards
biodiesel utilization
Cold flow
Diesel fuels
Flow stability
genetic algorithm
Genetic algorithms
Raw materials
Sorting algorithms
Specifications
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Biodiesel is a viable alternative to petroleum diesel. The properties of the biodiesel depend on the feedstock used to produce it. A significant difference in properties exists between different biodiesels. Therefore, standards for biodiesel fuel had been developed considering many factors such as safe handling, corrosion, ignition quality, stability, cold flow property, and performance. For using biodiesel as fuel, the properties of the biodiesel should be within the limits specified in the standard. Unfortunately, biodiesel produced from many feedstocks does not comply with the specifications for all the properties. To utilize biodiesel with poor quality, biodiesels can be blended so that the properties of the blend comply with the specifications. Determining the optimal biodiesel blend ratio experimentally requires a lot of effort particularly when the number of parameters to be optimized is more and when the number of constituent biodiesels in the blend is more. In this work, the application of non-dominated sorting genetic algorithm to predict optimal blends for different scenarios is demonstrated.
ISSN:1345-8957
1347-3352
DOI:10.5650/jos.ess20296