Generation of experimental data for model training to optimize fouling prediction

Abstract

To successfully deal with a complex fouling problem usually entails a good understanding based on a broad spectrum of additional data. Meanwhile, a huge amount of process data is recorded and may be utilized to create a better understanding and prediction of the fouling status of an apparatus or the entire production plant. We propose a systematic approach to generate training data in a pipe fitting as a pre-step before the potential use of the entire data set of the production plant, irrespective of the relevance for the fouling prediction. Therefore, a temperature-based detection of the heat transfer resistance of plastic discs (representing 'artificial’ fouling) and a particulate material deposition (representing ‘real’ fouling) was applied in a pipe fitting obtaining reproducible results. The parameter variation experiments exhibit linear fouling curves and are therefore very suitable for model training. The temperature measurements confirm a correlation between the obtained temperature drop and the layer thickness of the plastic discs as well as the deposited particle fouling mass.