review · Environmental Chemistry Letters
Reliance on fossil fuels creates energy crises, environmental pollution, and health concerns, driving interest in alternative fuels such as biodiesel. Biodiesel production from diverse waste feedstocks can be advanced using computational chemistry and machine learning tools. Key feedstocks include waste cooking oil, animal fats, algae, fish waste, municipal solid waste, and sewage sludge. Waste cooking oil currently accounts for roughly ten percent of worldwide biodiesel output, while restaurant operations generate over one million cubic metres of waste vegetable oil each year. Microalgae offers oil yields up to 250 times greater per acre than soybeans and seven to thirty-one times higher than palm oil. Furthermore, transesterifying lipids from food waste can attain complete conversion yields. Computational methods assist across catalyst design, reactor and reaction optimisation, stability assessments, feedstock evaluation, process scale-up, and molecular simulations, backed by policy mechanisms encouraging waste-derived fuels.
Transitioning from fossil fuels to renewable alternatives helps mitigate environmental pollution and energy crises. Utilising abundant organic waste, from restaurant oils to sewage sludge, provides sustainable raw materials for fuel generation. Integrating computational tools and machine learning enables precise optimisation of chemical reactions and industrial scaling, making waste-derived biodiesel production significantly more efficient and environmentally viable.
The review points to applications in industrial biofuel production, catalyst manufacturing, and municipal waste management. Biofuel producers and process engineers can use computational modelling and machine learning to optimise reactors and scale up transesterification from food waste or microalgae. Because the work synthesises techniques spanning early-stage catalyst design through to industrial process scale-up, the associated tools range from early research modelling to applied engineering.
AI-generated from the published abstract. Always read the original work before citing.
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 m 3 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s10311-024-01700-y
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
