Synthesis of Bio-lubricant Base Stocks From Waste Oil

Simple item page
dc.contributor.authorPaul, Atanu Kumar
dc.date.accessioned2023-09-06T08:00:51Z
dc.date.accessioned2023-10-19T10:34:49Z
dc.date.available2023-09-06T08:00:51Z
dc.date.available2023-10-19T10:34:49Z
dc.date.issued2022
dc.descriptionSupervisor: Goud, Vaibhav Ven_US
dc.description.abstractLubricants are oils that are often used in machines to reduce friction. Most lubricants and functional fluids in the present day are made entirely from petrochemical or mineral sources. Rising concerns about the environmental effects of mineral-based lubricants have prompted research into biodegradable lubricants. Vegetable oils have excellent biodegradability and rheological properties at higher operating temperatures, but poor cold flow characteristics Several methods have been attempted to solve these technological challenges, including altering fatty acid structure and genetic modification. Bio-lubricant base stocks derived from waste soybean cooking oil and its methyl esters are ideal for hydraulic and transmission applications as an alternative to traditional lubricants. Three modelling methods, namely, Response Surface Methodology (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) have been applied to optimise the process parameters to maximise the product yield. Additionally, thermal degradation kinetics of the prepared product have also been attempted in this study.en_US
dc.identifier.otherROLL NO.136107004
dc.identifier.urihttps://gyan.iitg.ac.in/handle/123456789/2446
dc.language.isoenen_US
dc.relation.ispartofseriesTH-2811;
dc.subjectBio-oilen_US
dc.subjectBio-dieselen_US
dc.subjectbio-lubricanten_US
dc.subjectEpoxidesen_US
dc.subjectRheologyen_US
dc.subjectKineticsen_US
dc.subjectArtificial Neural Networken_US
dc.subjectResponse Surface Methodologyen_US
dc.subjectGenetic Algorithmen_US
dc.titleSynthesis of Bio-lubricant Base Stocks From Waste Oilen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
Abstract-TH-2811_136107004.pdf
Size:
567.59 KB
Format:
Adobe Portable Document Format
Description:
ABSTRACT
Download
No Thumbnail Available
Name:
TH-2811_136107004.pdf
Size:
5.82 MB
Format:
Adobe Portable Document Format
Description:
THESIS
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Plain Text
Description:
Download
Collections
PhD Theses (Chemical Engineering)