Machine learning based optimization of ultrasonic pretreatment and biogas prediction in livestock wastewater anaerobic digestion.

Anaerobic digestion of livestock wastewater is frequently hindered by severe hydrolytic bottlenecks. This study integrates Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to optimize ultrasonic pretreatment and predict biogas production dynamics. A Central Composite Design (CCD) optimized critical ultrasonic parameters (power, exposure time, and total solids concentration) to maximize cell disintegration and biogas yields. The CCD analysis identified exposure time as the most critical parameter, accounting for 49% of the variance (R2 = 0.9879). Under RSM-optimized conditions (93.6% power, 13 min exposure, 2.29% Total Solids), cell disintegration reached 12.4%. Concurrently, cumulative biogas production increased by 41% versus the untreated control, peaking at 568 ± 36 mL/g total chemical oxygen demand. To delineate the non-linear kinetics of biogas generation, a 9-neuron Multilayer Perceptron trained via the Broyden-Fletcher-Goldfarb-Shanno algorithm was constructed. Evaluated against an independent blind test set, the ANN yielded acceptable predictive accuracy (R2 = 0.9643; MSE = 9520), effectively bypassing the limitations of the Modified Gompertz equation in capturing multi-parametric dynamics. Sensitivity analysis revealed biogas measurement duration as the most influential factor, representing 48% of the model's relative importance. Ultimately, while RSM elucidated variable interactions and optimal conditions, the ANN reliably predicted complex digestion trajectories, yielding a comprehensive optimization-prediction framework for wastewater valorization.