logo SBA

ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-02272023-155230


Thesis type
Tesi di dottorato di ricerca
Author
LO GRASSO, ANNA
URN
etd-02272023-155230
Thesis title
Development and validation of mathematical physical/chemical models for sensory systems.
Academic discipline
ING-INF/07
Course of study
SMART INDUSTRY
Supervisors
tutor Prof. Mugnaini, Marco
relatore Fort, Ada
Keywords
  • Conductive gas sensors
  • differential equation
  • fitting algorithm.
  • mathematical physical/chemical models
  • viscosity measurement
Graduation session start date
03/03/2023
Availability
Full
Summary
Nowadays industrial processes are more and more advanced and therefore the effectiveness of detection systems has become fundamental to monitor and control processes. Hence, it is therefore essential to develop new control and monitoring technologies for sensor-based systems to ensure greater reliability and long-term competitiveness. There are several sensors types available for monitoring aspects of processing environments, in particular, my research activity has focused on the study of the behavior of three types of sensors systems noticeably important in industrial monitoring applications: chemoresistive gas sensors for the detection of low concentrations of polluting and greenhouse gases, such as NOX, Quartz Crystal Microbalance (QCM) for fluid analysis such as for the test of lubricating oils and a measurement system for evaluating the e ciency of enzyme accelerated CO2 capture.
In detail, in studying the behavior of these sensor systems I worked on the development of mathematical models that could describe the sensing mechanisms of the devices with the aim of understanding their behavior in a more precise and complete way in order to speed up the design phase and contribute to the tuning of more efficient devices. In fact, the development of a dynamic mathematical model allows us to examine and control the phenomenon, and make predictions on its evolution. In particular, computational modeling allows us to obtain information on the behavior of complex systems which are difficult or impossible to obtain by direct measurements and observations.
File