In control processes, sometimes it would be necessary to know variables at locations that are barely accessible. In such cases, soft sensors (also known as 'virtual sensors') could really help. In fact, they are powerful instruments for the indirect measure of quantities that would not be measurable, if not with the installation of physical sensors that could perturbate the normal working conditions of the system under test. In this paper, the authors describe an approach to indirectly measure temperature values in a brew group of a professional coffee machine. A finite element (FE) model simulating both the fluid dynamics and the thermal distribution on the group was developed and validated by dedicated experimental tests. The FE model was then exploited to feed an autoregressive exogenous model [autoregressive with eXogenous input (ARX) model] linking the temperature in the boiler (i.e., a quantity ordinarily assessed in the coffee machine) and the one near the water output, where otherwise a hardware sensor would compromise the correct coffee brewing process and the safety/quality of the brewed coffee. The obtained data-driven soft sensor can help to improve the control unit architecture of the coffee machine.

Development of a Soft Sensor for Indirect Temperature Measurement in a Coffee Machine

Cosoli G.
;
Chiariotti P.;Martarelli M.;Tomasini E. P.
2020-01-01

Abstract

In control processes, sometimes it would be necessary to know variables at locations that are barely accessible. In such cases, soft sensors (also known as 'virtual sensors') could really help. In fact, they are powerful instruments for the indirect measure of quantities that would not be measurable, if not with the installation of physical sensors that could perturbate the normal working conditions of the system under test. In this paper, the authors describe an approach to indirectly measure temperature values in a brew group of a professional coffee machine. A finite element (FE) model simulating both the fluid dynamics and the thermal distribution on the group was developed and validated by dedicated experimental tests. The FE model was then exploited to feed an autoregressive exogenous model [autoregressive with eXogenous input (ARX) model] linking the temperature in the boiler (i.e., a quantity ordinarily assessed in the coffee machine) and the one near the water output, where otherwise a hardware sensor would compromise the correct coffee brewing process and the safety/quality of the brewed coffee. The obtained data-driven soft sensor can help to improve the control unit architecture of the coffee machine.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/278025
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