Biodiesel production from sewage sludge catalyzed by polyoxometalate Cs10[H1.8Ge3.2Nb11O39]: Experiments, mechanism and machine learning modeling with radial basis function interpolation based data augmentation

Abundant lipids-containing sewage sludge are potential inexpensive alternative substrate for biodiesel production. Herein, a novel polyoxometalate, Cs 10 [H 1.8 Ge 3.2 Nb 11 O 39 ], with desirable acid-base bifunctionality, was synthesized. It demonstrated high efficiency in facilitating biodiesel production from sewage sludge, through in-situ esterification/transesterification, resulted in maximum biodiesel yields of 94.4 %. The catalyst exhibited good resistance to free fatty acids and demonstrated high water tolerance, retaining over 60 % yield even at 99 wt% moisture. To predict and optimize biodiesel yield from sewage sludge, this study presents a novel machine learning framework that integrates Radial Basis Function (RBF) interpolation-based data augmentation with Artificial Neural Network (ANN) modeling, achieving a high coefficient of determination (R 2  = 0.96) and reduced prediction errors. The incorporation of RBF-enhanced data augmentation effectively mitigates model overfitting while improving cost-effectiveness. The influence of each parameter and relative importance of key reaction parameters on biodiesel production was elucidated by SHapley Additive exPlanations and partial dependence analysis. The physicochemical characteristics of the produced biodiesel met the specifications of international standards, indicating its potential to replace diesel fuel. This study not only offers a new approach to advancing sustainable biodiesel production processes with cheap substrates but also provides practical guidance for sewage sludge treatment and upcycling.