Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model

TL;DR

This paper introduces an innovative ICWT method for sewer overflow mitigation, utilizing hydraulic modeling and LSTM-based water level prediction to optimize pump station operation in a Norwegian sewer system.

Contribution

It combines hydraulic modeling with deep learning for predictive control, specifically applying LSTM to improve pump station management in wastewater transfer.

Findings

LSTM outperforms GRU, RNN, FFNN, and SVR in water level prediction.

The ICWT method effectively balances sewer flow and treatment capacity.

The pump station's operation is significantly enhanced through predictive modeling.

Abstract

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).