Predictive Modelling of Desiccant Drying Processes Using Multi-Feature k-Nearest Neighbours Algorithm

Desiccant dryers play a critical role in industrial sulphonation processes by ensuring that moisture is Effectively removed from the air used during SO 2 to SO 3 conversion. This is necessary to prevent the formation of sulphuric acid, which can harm machinery and lower product quality. This paper introduces a novel approach utilizing Multi-Feature k-nearest neighbours (MF-kNN) forecasting to optimise the drying and regeneration cycles of the dehumidification process units. A key advantage of the MF-kNN model is its ability to perform one-shot forecasts relatively early in the cycle, accurately predicting critical transitions without the need for recursive recalculations. The proposed approach was tested using data from a large-scale surfactant production facility. For forecasting the regeneration cycle endpoint, the model incorporates both the regeneration inlet and outlet air temperatures. Hyperparameter tuning results show that assigning 50% of the feature weight to the inlet temperature results in the lowest forecasting error. Two approaches for data window selection were investigated, namely a moving and an expanding window. The moving window approach outperforms the expanding window approach by 35% and 42% reduced errors for endpoint detection and time-series forecasting tasks respectively. Overall, the model is able to predict the endpoint within a 2 min accuracy with a 400 min lead time on the tested cycles.