The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

In-line monitoring and control of rheological properties through data-driven ultrasound soft-sensors

Tronci S.
Primo
Methodology
;
Mei R.
Resources
;
Grosso M.
Ultimo
Conceptualization
2019-01-01

Abstract

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.
2019
Data-driven; Decision support; Hybrid approach; Industry 4.0; Neural network; Non-Newtonian fluid; Ultrasound sensor; Viscosity curve
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Descrizione: Articolo principale
Tipologia: versione editoriale
Dimensione 1.85 MB
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1.85 MB Adobe PDF Visualizza/Apri

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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/283271
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