Prediction and
Evaluation of Indirect Carbon Emission
from Electrical Consumption in Multiple Full-Scale Wastewater Treatment
Plants via Automated Machine Learning-Based Analysis
posted on 2022-12-15, 18:35authored byRunze Xu, Yi Li, Yuting Luo, Fang Fang, Qian Feng, Jiashun Cao, Jingyang Luo
The indirect carbon emission from
electrical consumption
of wastewater
treatment plants (WWTPs) accounts for large proportions of their total
carbon emissions, which deserves intensive attention. This work proposed
an automated machine learning (AutoML)-based indirect carbon emission
analysis (ACIA) approach and predicted the specific indirect carbon
emission from electrical consumption (SEe; kg CO2/m3) successfully in nine full-scale WWTPs (W1–W9)
with different treatment configurations based on the historical operational
data. The stacked ensemble models generated by the AutoML accurately
predicted the SEe (mean absolute error = 0.02232–0.02352, R2 = 0.65107–0.67509). Then, the variable
importance and Shapley additive explanations (SHAP) summary plots
qualitatively revealed that the influent volume and the types of secondary
and tertiary treatment processes were the most important variables
associated with SEe prediction. The interpretation results
of partial dependence and individual conditional expectation further
verified quantitative relationships between input variables and SEe. Also, low energy efficiency with high indirect carbon emission
of WWTPs was distinguished. Compared with traditional carbon emission
analysis and prediction methods, the ACIA method could accurately
evaluate and predict SEe of WWTPs with different treatment
scales and processes with easily available variables and reveal qualitative
and quantitative relationships inside datasets simultaneously, which
is a powerful tool to benefit the “carbon neutrality”
of WWTPs.