LAPSE:2023.13256
Published Article

LAPSE:2023.13256
Comprehensive Optimisation of Biodiesel Production Conditions via Supercritical Methanolysis of Waste Cooking Oil
March 1, 2023
Abstract
Biodiesel has been established as a promising alternative fuel to petroleum diesel. This study offers a promising energy conversion platform to valorise high acidity waste cooking oil (WCO) into biodiesel in a single-step reaction via supercritical methanol. Carbon dioxide (CO2) has been used as a co-solvent in the reaction with a catalytic effect to enhance the production of biodiesel. This work provides an in-depth assessment of the yield of four fatty acids methyl esters (FAME) from their correspondent triglycerides and fatty acids. The effects of four independent process variables, i.e., methanol to oil (M:O) molar ratio, temperature, pressure, and time, have been investigated using Response Surface Methodology (RSM). Four quadratic models have been developed between process variables and the yield of FAMEs. The statistical validation of the predicted models has been performed using analysis of variance (ANOVA). Numerical optimisation has been employed to predict the optimal conditions for biodiesel production. The predicted optimal conditions are at 25:1 M:O molar ratio, 254.7 °C, 110 bar within 17 min resulting in 99.2%, 99.3%, 99.13%, and 99.05% of methyl-oleate, methyl-palmitate, methyl-linoleate, and methyl-stearate yields, respectively. The predicted optimum conditions have been validated experimentally.
Biodiesel has been established as a promising alternative fuel to petroleum diesel. This study offers a promising energy conversion platform to valorise high acidity waste cooking oil (WCO) into biodiesel in a single-step reaction via supercritical methanol. Carbon dioxide (CO2) has been used as a co-solvent in the reaction with a catalytic effect to enhance the production of biodiesel. This work provides an in-depth assessment of the yield of four fatty acids methyl esters (FAME) from their correspondent triglycerides and fatty acids. The effects of four independent process variables, i.e., methanol to oil (M:O) molar ratio, temperature, pressure, and time, have been investigated using Response Surface Methodology (RSM). Four quadratic models have been developed between process variables and the yield of FAMEs. The statistical validation of the predicted models has been performed using analysis of variance (ANOVA). Numerical optimisation has been employed to predict the optimal conditions for biodiesel production. The predicted optimal conditions are at 25:1 M:O molar ratio, 254.7 °C, 110 bar within 17 min resulting in 99.2%, 99.3%, 99.13%, and 99.05% of methyl-oleate, methyl-palmitate, methyl-linoleate, and methyl-stearate yields, respectively. The predicted optimum conditions have been validated experimentally.
Record ID
Keywords
biodiesel, optimisation, response surface methodology, supercritical methanolysis, waste cooking oil
Subject
Suggested Citation
Aboelazayem O, Gadalla M, Saha B. Comprehensive Optimisation of Biodiesel Production Conditions via Supercritical Methanolysis of Waste Cooking Oil. (2023). LAPSE:2023.13256
Author Affiliations
Aboelazayem O: School of Engineering, London South Bank University, London SE1 0AA, UK; Department of Chemical Engineering, The British University in Egypt, Misr-Ismalia Road, El-Shorouk City 11837, Egypt; School of Chemical and Process Engineering, University of Leeds, [ORCID]
Gadalla M: Department of Chemical Engineering, The British University in Egypt, Misr-Ismalia Road, El-Shorouk City 11837, Egypt; Department of Chemical Engineering, Port Said University, Port Said 42511, Egypt
Saha B: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK [ORCID]
Gadalla M: Department of Chemical Engineering, The British University in Egypt, Misr-Ismalia Road, El-Shorouk City 11837, Egypt; Department of Chemical Engineering, Port Said University, Port Said 42511, Egypt
Saha B: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK [ORCID]
Journal Name
Energies
Volume
15
Issue
10
First Page
3766
Year
2022
Publication Date
2022-05-20
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15103766, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.13256
This Record
External Link

https://doi.org/10.3390/en15103766
Publisher Version
Download
Meta
Record Statistics
Record Views
263
Version History
[v1] (Original Submission)
Mar 1, 2023
Verified by curator on
Mar 1, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.13256
Record Owner
Auto Uploader for LAPSE
Links to Related Works
