Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

Abstract

Anaerobic digestion (AD) of sludge is a key approach to recover useful bioenergy from wastewater treatment and its stable operation is important to a wastewater treatment plant (WWTP). Because of various biochemical processes that are not fully understood, AD operation can be affected by many parameters and thus modeling AD processes becomes a useful tool for monitoring and controlling their operation. In this case study, a robust AD model for predicting biogas production was developed using ensembled machine learning (ML) model based on the data from a full-scale WWTP. Eight ML models were examined for predicting biogas production and three of them were selected as metamodels to create a voting model. This voting model had a coefficient of determination (R 2 ) at 0.778 and a root mean square error (RMSE) of 0.306, outperformed individual ML models. The Shapley additive explanation (SHAP) analysis revealed that returning activated sludge and temperature of wastewater influent were important features, although they affected biogas production in different ways. The results of this study have demonstrated the feasibility of using ML models for predicting biogas production in the absence of high-quality data input and improving model prediction through assembling a voting model. Practitioner Points• Machine learning is applied to model biogas production from anaerobic digesters at a full-scale wastewater treatment plant.• A voting model is created from selected individual models and exhibits better performance of predication.• In the absence of high quality data, indirect features are identified to be important to predicting biogas production. K E Y W O R D Sanaerobic digestion, ensembled model, sensitivity analysis, wastewater treatment plant Both Jianpeng Zhou and Zhen He are WEF members.

Influence of Thermal Pretreatment on Lignin Destabilization in Harvest Residues: An Ensemble Machine Learning Approach

“…Previous studies provided very limited knowledge on the effectiveness of ensemble machine learning in optimizing individual components of biogas production. The available research indicated its ability to improve the prediction of individual methods [45,46], which contradicts the results of this study. However, while several broader studies generally agreed on the effectiveness of ensemble machine learning, they also provided mixed observations regarding its robustness relative to individual methods.…”