Optimizing biodiesel production from waste with computational chemistry, machine learning and policy insights: a review

Ahmed I. Osman *, Mahmoud Nasr*, Mohamed Farghali, Ahmed K. Rashwan, Adel Abdelkader, Ala’a H. Al-Muhtaseb, Ikko Ihara, David W. Rooney

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
14 Downloads (Pure)

Abstract

The excessive reliance on fossil fuels has resulted in an energy crisis, environmental pollution, and health problems, calling for alternative fuels such as biodiesel. Here, we review computational chemistry and machine learning for optimizing biodiesel production from waste. This article presents computational and machine learning techniques, biodiesel characteristics, transesterification, waste materials, and policies encouraging biodiesel production from waste. Computational techniques are applied to catalyst design and deactivation, reaction and reactor optimization, stability assessment, waste feedstock analysis, process scale-up, reaction mechanims, and molecular dynamics simulation. Waste feedstock comprise cooking oil, animal fat, vegetable oil, algae, fish waste, municipal solid waste and sewage sludge. Waste cooking oil represents about 10% of global biodiesel production, and restaurants alone produce over 1,000,000 m3 of waste vegetable oil annual. Microalgae produces 250 times more oil per acre than soybeans and 7–31 times more oil than palm oil. Transesterification of food waste lipids can produce biodiesel with a 100% yield. Sewage sludge represents a significant biomass waste that can contribute to renewable energy production.

Original languageEnglish
Number of pages67
JournalEnvironmental Chemistry Letters
Early online date13 Feb 2024
DOIs
Publication statusEarly online date - 13 Feb 2024

Fingerprint

Dive into the research topics of 'Optimizing biodiesel production from waste with computational chemistry, machine learning and policy insights: a review'. Together they form a unique fingerprint.

Cite this