Rheality is the outcome of the current Ph.D. thesis (Towards Advanced in situ Measurements of Complex Fluid Rheological Properties in Manufacturing Processes: Identification of a “Finger Print” of a Fluid) of Daniel Hefft, supervised by Dr Alberini. The patent pending technology (GB1909291.5), named Rheality, is designed as a plug and play system and works for any single and multiphase fluid passing through a pipe. The technology works on a combination of transient energy release measurements and machine learning. The fluid passes through a specifically designed pipe segment, which causes it to release transient energy. This is detected by a piezoelectric sensor on the outer wall of the pipe segment. With the help of self-developed algorithms and statistical analysis, the incoming data is reduced by over 99% and selected features are given to machine learning. The machine learning algorithms are trained to predict the following features on a live basis:
1) Relative rheology,
2) Blockages and leakages in the pipe system,
3) Solid/gas content for multiphase systems,
4) Flow and flow rate.
More info about the research developments:
Article on the journal of the institut of food science & technology
Article on Mess‐Steuer‐Automatisierungs‐Technik