Bridging waste-derived carbon catalyst innovation and machine learning for sustainable biodiesel production: a comprehensive review.
- 1Chemical Engineering Department, Faculty of Engineering, Cairo University Giza 12613 Egypt [email protected].
- 2Public Works Department, Faculty of Engineering, Cairo University Giza 12613 Egypt.
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.
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BibTeX
@article{ebrahim2026bridging,
title = {{Bridging waste-derived carbon catalyst innovation and machine learning for sustainable biodiesel production: a comprehensive review.}},
author = {Menna Ebrahim and Mostafa Mahmoud and Fatma H Ashour and Osama A Ibrahim and Nourhan H Khashaba},
journal = {RSC advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra05871j},
url = {https://europepmc.org/article/MED/42898746},
note = {CC BY},
}
RIS
TY - JOUR
TI - Bridging waste-derived carbon catalyst innovation and machine learning for sustainable biodiesel production: a comprehensive review.
AU - Menna Ebrahim
AU - Mostafa Mahmoud
AU - Fatma H Ashour
AU - Osama A Ibrahim
AU - Nourhan H Khashaba
PY - 2026
DA - 2026/10/19
JO - RSC advances
DO - 10.1039/d6ra05871j
UR - https://europepmc.org/article/MED/42898746
AB - 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.
SN - 2046-2069
ER -