Machine learning for high solid anaerobic digestion: Performance prediction and optimization

[Display omitted] •Supervised ML models were used to predict biogas yield and methane composition.•5 different ML algorithms were considered including SVM, DT, ET, GPR, and KNN.•Critical parameters that affect the BGP prediction were OLR and HRT, and C/N ratio.•SVM predicted the best accuracy with an R2 value of 0.91.•No significant difference was found between 2-datasets (p = 0.377). Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas predictive model was developed for high solid systems using algorithms, including SVM, ET, DT, GPR, and KNN and two different datasets (Dataset-1:10, Dataset-2:5 inputs). Support Vector Machine had the highest accuracy (R2) of all the algorithms at 91 % (Dataset-1) and 87 % (Dataset-2), respectively. The statistical analysis showed that there was no significant difference (p = 0.377) across the datasets, wherein with less inputs, accurate results could be predicted. In case of biogas yield, the critical factors which affect the model predictions include loading rate and retention time. The developed high solid machine learning model shows the possibility of integrating Artificial Intelligence to optimize and control AD process, thus contributing to a generic model for enhancing the overall performance of the biogas plant.