Abstract

Wastewater sludge from a poultry slaughterhouse treatment plant was recovered through conversion into biodiesel by ultrasound-assisted in situ transesterification. The main effects of the process parameters were investigated at three levels, and their empirical relationship was modeled using artificial neural network (ANN) and response surface methodology (RSM). The developed models predicted the process behavior with excellent accuracy. Although both models had similar prediction performances, ANN marginally outperformed RSM. The capability of the genetic algorithm (GA) combined with the RSM (RSM-GA) and ANN (ANN-GA) models was evaluated for optimizing the process variables. The maximum biodiesel yield (21.45 w/w) was obtained using the ANN-GA model under optimized conditions, i.e., at the reaction time of 39.69 min, H2SO4 concentration of 3.34 (v/v), methanol-to-sludge relative content of 14.91:1 (mL/g), and ultrasound power of 104.87 W. Consequently, a combination of ANN and GA was proposed to model and optimize the transesterification process. The biodiesel yield obtained in this study was higher than the previously reported values from tannery (10.98), dairy (13.46), and municipal (18.58) sewage sludge. This study specified biodiesel with a fatty acid methyl esters content of 96.86 using gas chromatography-mass spectrometry, Fourier transform infrared, and nuclear magnetic resonance spectroscopy. © 2021 Elsevier Ltd