Biomethane Production from the Mixture of Sugarcane Vinasse, Solid Waste and Spent Tea Waste: A Bayesian Approach for Hyperparameter Optimization for Gaussian Process Regression

In this work, sugarcane vinasse combined with organic waste (food and wasted tea) was demonstrated to be an excellent source of biomethane synthesis from carbon-rich biowaste. The discarded tea trash might successfully used generate bioenergy. uncertainties costs associated experimental testing were recommended decreased by the effective use contemporary machine learning methods such as Gaussian process regression. training hyperparameters are crucial in construction a robust ML-based model. To make autoregressive, fine-tuned employing Bayesian approach. value R2 found greater during model test phase 0.72%, assisting avoidance overtraining. mean squared error 36.243 21.145 phase. absolute percentage under 0.1%, which 0.085% throughout model’s research that combination trash, food may viable for generation. methodology approach tuning regression is efficient method prediction despite low correlation across data columns. It possible enhance sustainability paradigm direction energy security via usage agroforestry waste.