Machine learning-enhanced mass flow measurements using a Coriolis mass flow sensor

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Abstract

We present machine learning-enhanced mass flow measurements based on microfabricated Coriolis mass flow sensors with integrated pressure and temperature sensors. Two machine learning techniques have been applied: linear regression (LR) and support vector regres- sion (SVR) on four features extracted from the raw sensor data to improve mass flow estimation. In contrast to conventional mass flow detection, LR and SVR use information from all integrated sensors to estimate the mass flow, which results in a full-scale accuracy improvement of a factor 4 for trained fluids.
Original languageEnglish
Title of host publicationThe 5th Conference On MicroFluidic Handling Systems (MFHS 2024)
Pages65-68
Publication statusPublished - 21 Feb 2024
Event5th Conference on MicroFluidic Handling Systems, MFHS 2024 - Munich, Germany
Duration: 21 Feb 202423 Feb 2024
Conference number: 5

Conference

Conference5th Conference on MicroFluidic Handling Systems, MFHS 2024
Abbreviated titleMFHS 2024
Country/TerritoryGermany
CityMunich
Period21/02/2423/02/24

Keywords

  • Coriolis
  • Mass flow
  • Machine learning
  • Linear regression
  • Support vector regression
  • Sensors
  • Microfluidics

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