Generation of experimental data for model training to optimize fouling prediction

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.

Automated summary

Successfully dealing with complex fouling problems requires a good understanding based on a wide range of additional data. Process data can be utilized to better understand and predict fouling in an apparatus or a production plant. We propose a systematic approach to generate training data in a pipe fitting, which can be used before utilizing the entire data set of the production plant for fouling prediction. The temperature-based detection of heat transfer resistance and particle deposition in the pipe fitting provides reproducible results for model training.