Prediction of flocculant dosage in water plant based on LSTM network

Flocculation and sedimentation is a crucial step in the water treatment process. Currently, most water plants still use a fixed-value proportional dosing method for flocculant dosing, which has low accuracy. Flocculant dosing prediction is a time series problem, and the complexity of the problem that can be expressed using traditional time-series modeling is limited, and machine learning requires a more complex manual feature engineering component. In this paper, we propose an LSTM neural network prediction model incorporating the Attention mechanism to correlate current sensor acquisition data with historical moment data, extract multidimensional features, and focus on key information and ignore redundant information. It can be a better solution for this problem with nonlinearity, multiple input factors, uncertainty, and time-varying characteristics. Through experiments, comparing the common models such as BP, RNN, LSTM, etc. to predict the flocculant dosing of half-yearly in water plants, the model has a high accuracy.