Modeling Indirect
Greenhouse Gas Emissions Sources
from Urban Wastewater Treatment Plants: Integrating Machine Learning
Models to Compensate for Sparse Parameters with Abundant Observations
Electricity consumption and sludge yield (SY) are important
indirect
greenhouse gas (GHG) emission sources in wastewater treatment plants
(WWTPs). Predicting these byproducts is crucial for tailoring technology-related
policy decisions. However, it challenges balancing mass balance models
and mechanistic models that respectively have limited intervariable
nexus representation and excessive requirements on operational parameters.
Herein, we propose integrating two machine learning models, namely,
gradient boosting tree (GBT) and deep learning (DL), to precisely
pointwise model electricity consumption intensity (ECI) and SY for
WWTPs in China. Results indicate that GBT and DL are capable of mining
massive data to compensate for the lack of available parameters, providing
a comprehensive modeling focusing on operation conditions and designed
parameters, respectively. The proposed model reveals that lower ECI
and SY were associated with higher treated wastewater volumes, more
lenient effluent standards, and newer equipment. Moreover, ECI and
SY showed different patterns when influent biochemical oxygen demand
is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore,
managing ECI and SY requires quantifying the coupling relationships
between biochemical reactions instead of isolating each variable.
Furthermore, the proposed models demonstrate potential economic-related
inequalities resulting from synergizing water pollution and GHG emissions
management.