Machine Learning Model for Predicting the Performance of Activated Carbon Column for the Removal of Volatile Organic Compounds (VOCs)

Abstract

Objectives In this study, a performance prediction model for a pilot-scale VOC adsorption column was developed using ANN algorithm. We compared the prediction accuracy of the mathematical models (Thomas model and Yan model) and the multiple linear regression model with that of ANN. This study showed the applicability of the ANN model for predicting the performance of activated carbon columns. Methods The adsorption module contained 79.8 kg/module of wood-based activated carbon. The gas with 800 ppm-THC of toluene flowed downward from the top at about 5,700 m3/h. The breakthrough point was taken as 200 ppm-THC, the same as VOC emission regulation. The desorption was carried out using 130 m3/h of hot gas flowing upwards with reduced pressure (-150 to -200 mbar) and high heat (170℃). Adsorption and desorption cycles were conducted 6 times using 3 batches of activated carbon modules. Thomas model, Yan model, multiple linear regression model, and ANN model were developed to predict the breakthrough of Cout/Cin . Results and Discussion The Thomas model and the Yan model provided the R2 values of 0.25 and 0.28, respectively, for predicting the Cout/Cin of all adsorption module batches and cycles, and the prediction accuracies were low. This could be because these two models do not consider temperature and pressure change operating conditions in the models. Also, the prediction accuracy of Cout/Cin was low when the initial inlet concentration and flow rate conditions were different for each batch. The multiple linear regression model considers all operating factors in the model, but the prediction accuracy of Cout/Cin was low as R2 of 0.45. On the other hand, the ANN model predicted the Cout/Cin with R2 higher than 0.97 for all adsorption module batches. In particular, even with the non-ideal data, the ANN model derived a breakthrough of Cout/Cin close to the experimental value. Conclusion The ANN model provided high prediction performance for the breakthrough of Cout/Cin even under non-ideal operation conditions and was expected to be helpful for actual THC adsorption column operation. The accuracy of the ANN model will be further improved if data are accumulated under various conditions.

Keywords

• volatile organic compounds
• activated carbon
• adsorption
• model
• machine learning