Authors
Menna Ebrahim, Mostafa Mahmoud, Fatma H Ashour, Osama A Ibrahim, Nourhan H Khashaba
Published in
RSC advances. Oct 19, 2026. Epub Oct 19, 2026.
Abstract
Biodiesel is a promising sustainable alternative to fossil diesel, but its deployment is limited by challenges in catalyst efficiency, stability, reusability, and process optimization. Metal-doped activated carbon (AC) and biochar-based catalysts have attracted considerable attention owing to their high surface area, tunable porosity, and low-cost production from waste biomass. In parallel, machine learning (ML) has emerged as a powerful tool for biodiesel yield prediction, process modeling, and optimization. However, these research areas have largely progressed separately. This review combines scientometric analysis with a systematic review to examine advances in waste-derived AC and biochar-based catalysts and ML applications in biodiesel production. Catalyst synthesis, activation, metal doping, physicochemical properties, catalytic performance, stability, and reusability are critically evaluated alongside ML-based prediction and process optimization approaches. An integrated catalyst-process-performance framework is further discussed to highlight opportunities for incorporating catalyst descriptors into data-driven biodiesel modeling. The review identifies key research trends, knowledge gaps, and future opportunities for advancing sustainable and scalable biodiesel production.
PMID:
42898746
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 9
- Comments 0