Temporal Convolutional Network-based Approach for Forecasting Fluctuations Differential Pressure in Reverse Osmosis Systems

Providing forecasts of pressure fluctuations and changes will aid in selecting appropriate maintenance strategies to optimize efficiency and costs. This paper presents a deep-learning-based model to forecast the degradation evolution of membrane biological fouling in RO (Reverse Osmosis) systems. Although applying deep learning in forecasting still faces many challenges, applying convolutional operations in convolution 1D has yielded promising results for sequential data, particularly time series data. Thus, in this paper we study and develop the 1D convolution operation-based Temporal Convolutional Network (TCN) model to predict pressure dynamics at both ends of the RO vessel. In addition, since the deep learning technique has yet to be widely explored in this field, thus we also need to pre-process the data collected from the Carlsbad Desalination Plant in California, such as the proposed model can identify complex relationships between timestamps and pressure features. The experiment results were evaluated and compared with other existing models, such as LSTM, CNN & LSTM, and GRU. The obtain results show that the TCN-based prediction model had the slightest error in the test dataset.