Abstract
Abstract Accurate and responsive non-invasive temperature measurements are enablers for process monitoring and plant optimization use cases in the context of Industry 4.0. If their performance is proven for large classes of applications, such measurement principles can replace traditional invasive measurements. In this paper we describe a two-step model to estimate the process temperature from a pipe surface temperature measurement. This static case model is compared to and enhanced by computational fluid dynamic (CFD) calculations to predict transient situations. The predictions of the approach are validated by means of controlled experiments in a laboratory environment. The experimental results demonstrate the efficacy of the model, the responsiveness of the pipe surface temperature, and that state of the art industrial non-invasive sensors can achieve the performance of invasive thermowells. The non-invasive sensors are then used to demonstrate the performance of the model in industrial applications for cooling fluids and steam.
Highlights
Accurate and responsive non-invasive temperature measurements are enablers for process monitoring and plant optimization use cases in the context of Industry 4.0
In this paper we describe a two-step model to estimate the process temperature from a pipe surface temperature measurement
The experimental results demonstrate the efficacy of the model, the responsiveness of the pipe surface temperature, and that state of the art industrial non-invasive sensors can achieve the performance of invasive thermowells
Summary
A process medium is conveyed in a pipe that is protected against ambient conditions by one or several layers of insulating material. We will call this the process model. Finding a good estimate of the pipe surface temperature Ts is important and a challenge. A specific type of model is needed to infer a good estimate of the surface temperature Ts, e. G., as indicated, from sensor readings Tprimary and Treference We will call this the device model.
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