Abstract
We report on statistical machine learning methods applied to a Coriolis-based sensor chip to accurately estimate liquid density and dynamic viscosity for trained liquids. This estimation is challenging with conventional methods due to non-ideal sensor effects. The chip (1.2 cm 2 ) has been exposed to different combinations of temperatures, flows, and pressures for three different liquids: ethanol, water and isopropanol. The statistical machine learning methods have been applied to the raw sampled signals of the sensing structures. The results have been obtained for different temperatures and shown to be less dependent on the liquid state (i.e., pressure and flow). The best-performing method was the Gaussian Process Regression (GPR) method, which results in a mean absolute percentage error of < 0.01 % and < 1 % for density and viscosity respectively, which is a factor of > 4 better compared to conventional methods.
Original language | English |
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Title of host publication | 2024 IEEE 37th International Conference on Micro Electro Mechanical Systems (MEMS) |
Place of Publication | Austin, Texas, USA |
Publisher | IEEE |
Pages | 82-85 |
Number of pages | 4 |
ISBN (Electronic) | 979-8-3503-5792-9 |
ISBN (Print) | 979-8-3503-5793-6 |
DOIs | |
Publication status | Published - 22 Jan 2024 |
Event | 37th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2024 - Austin, United States Duration: 21 Jan 2024 → 25 Jan 2024 Conference number: 37 |
Conference
Conference | 37th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2024 |
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Abbreviated title | MEMS 2024 |
Country/Territory | United States |
City | Austin |
Period | 21/01/24 → 25/01/24 |
Keywords
- Microfluidics
- Coriolis
- Mass flow sensors
- Machine learning
- Regression
- Viscosity
- Density
- Estimation
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