Data-driven self-learning forecast model for membrane fouling in filtration processes

This article focuses on filtration processes and AI models for forecasting membrane fouling, measured by the pressure drop (TMP) at the membrane. While conventional AI models can achieve accurate TMP forecasts, they require large amounts of training data and time-consuming training procedures. However, the amount of filtration data to train AI models is very sparse, leading to an industrial need for self-learning forecasting models. With those self-learning abilities, built-in guarantees for adequate forecasts are mandatory. The main contribution is a self-learning ARIMA model with an adaptive training data selection and a built-in guard mechanism that guarantees process-technically logical TMP forecasts by ensuring their adherence to expert-defined rules on the TMP curve’s dynamic. Evaluation results confirm that the guard improves the forecasting performance.