Applying machine learning in intelligent sewage treatment: A case study of chemical plant in sustainable cities

Abstract Nowadays, sewage treatment in sustainable cities attracts more researchers both from academic and industrial communities. Especially, since industrial sewage is normally highly toxic, which could cause serious pollution in a city and lead to health problems of residents, it is critical to monitor and predictably maintain sewage treatment facilities in cities. This paper presents an intelligent sewage treatment system based on machine learning and Internet of Things sensors to assist to manage the sewage treatment in a fine chemical plant. The implemented system has operated for twenty months, acquired multi-dimension data such as temperatures in different treatment processes, operation parameters of devices, and real-time Chemical Oxygen Demand (COD). Since the change trend of outflow COD is highly related to operation status, this paper innovatively uses different types of temperature and water inflow data as model inputs and applies three algorithms to make prediction, which are Support Vector Regression (SVR), Long Short-Term Memory (LSTM) neural network, and Gated Recurrent Unit (GRU) neural network. The experimental results show that GRU model performs better (MAPE = 10.18%, RMSE = 35.67, MAE = 31.16) than LSTM and SVR. This study can be extended to various sewage treatment scenarios in sustainable cities.