Forecasting the Quantity and Concentration of Flocculant in Clarification Process for Sugarcane Industry

The clarification process is an important part of sugarcane production. This process is used for separating
sediment and sugarcane juice by adding flocculant. The addition of quantity and concentration of flocculant directly affects the settling rate and turbidity of sugarcane juice. This paper proposes a model for forecasting quantity and concentration of flocculant by using Long Short-Term Memory (LSTM) Neural Network.
Input data consists of green cane, burn cane, turbidity, and rainfall. Output data includes quantity and concentration of flocculant. Raw data was collected from top sugarcane factory and meteorological department in Thailand. The results are the forecast of the quantity and concentration of flocculant for one day in advance. The performance of LSTM is compared to the autoregressive integrated moving average (ARIMA), recurrent neural network (RNN), and gated recurrent unit (GRU) using root mean square error and mean absolute percent error. The result indicates that LSTM has the best performance. The forecast helps the operator in clarification process to prepare the flocculant.