Machine learning-informed one-pot biodiesel synthesis from an optimally formulated mixed non-edible oil feedstock over magnetic sulfonated biobased catalyst.

ABSTRACT: The disposal of domestic and industrial nonedible oils is a major source of environmental concern. Similarly, the global energy-related environmental crises have exacerbated over the past decade. In this research, a ternary mixture of non-edible oils (MNEO) from castor oil (CO), waste cooking oil (WCO) and recovered-oil from palm oil mill effluent (RO-POME) was optimally formulated as a feed-stock for biodiesel production. MNEO formulation was achieved using a D-optimal mixture design-aided intelligent optimization. Poultry droppings (PD) was sequentially subjected to calcination, sulfonation and magnetization to yield a reusable heterogeneous catalyst. One-pot transesterification was modeled using explainable machine learning algorithms including support vector regression (SVR), artificia

SUBMITTER: Ohale PE 

PROVIDER: S-EPMC12712571 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature