Foaming Prediction for CO2 Removal Absorber Column

In industrial processes, foaming detection is typically reactive, relying on fixed thresholds applied to individual process variables. This study presents a methodology for the early prediction of foaming events in the CO 2 removal section of an ammonia production plant. The proposed approach first identifies key variables and then uses Long Short-Term Memory (LSTM) Autoencoder, which are well-suited for learning complex temporal dependencies in multivariate time series data, to achieve early prediction of foaming events. The method is evaluated using data from real foaming events. The results demonstrate its effectiveness in providing early warnings, offering an advantage over traditional reactive detection methods.