The
low-carbon and sustainable operation challenges the wastewater
treatment plant (WWTP), notably as influent temperatures vary seasonally.
Optimizing operational parameters is a feasible approach, but current
methods generally face a large computational cost and imprecise optimization.
In response, this study developed a novel seasonal multiobjective
optimization method based on deep learning to trade-off effluent quality
index (EQI), operational cost index (OCI), and greenhouse gas (GHG)
emissions. The crucial control and operation variables were identified
for both winter and summer using Sobol’s sensitivity analysis,
serving as optimization candidates and inputs for the data-driven
models. Then, deep neural network models were constructed on a seasonal
basis to approximate EQI, OCI, GHG emissions, and crucial effluent
quality limitations. Furthermore, multiobjective optimization for
winter and summer was performed based on the preference-inspired coevolutionary
algorithm. The results show that the optimized scenarios reduce the
level of the OCI by 23.92% and 40.94% and the level of GHG emissions
by up to 7.72% and 13.91% in winter and summer, compared to the base
cases. The novel optimization approach simplifies and improves performance
trade-off for low-carbon WWTPs. It also facilitates online fine-tuning
of WWTP operating parameters for different seasons, particularly in
regions with significant temperature changes.