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Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method
for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch
of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP),
through collaborative operation of sewer system facilities. Using a hydraulic
model, the effectiveness of ICWT is investigated in a sewer system in Drammen,
Norway. Concerning the whole system performance, we found that the S{\o}ren
Lemmich pump station plays a vital role in the ICWT framework. To enhance the
operation of this pump station, it is imperative to construct a multi-step
ahead water level prediction model. Hence, one of the most promising artificial
intelligence techniques, Long Short Term Memory (LSTM), is employed to
undertake this task. Experiments demonstrated that LSTM is superior to Gated
Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural
Network (FFNN) and Support Vector Regression (SVR)
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