Controlled Inline Fluid Separation Based on Smart Process Tomography Sensors

Journal article (2020)

Authors

Benjamin Sahovic Helmholtz Zentrum Dresden Rossendorf
Hanane Atmani Institut National Polytechnique de Toulouse (INP)
Muhammad Awais Sattar Lodz University of Technology
M. Martinez Garcia ChemE/Transport Phenomena - AS
Eckhart Schleicher Helmholtz Zentrum Dresden Rossendorf
Dominique Legendre Institut National Polytechnique de Toulouse (INP)
Eric Climent Institut National Polytechnique de Toulouse (INP)
Annaig Pedrono Institut National Polytechnique de Toulouse (INP)
L. Portela ChemE/Transport Phenomena - AS
More Authors More Authors External organisation
Research Group
ChemE/Transport Phenomena (AS) (TU Delft)
Copyright: © 2020 Benjamin Sahovic, Hanane Atmani, Muhammad Awais A. Sattar, M. Martinez Garcia, Eckhart Schleicher, Dominique Legendre, Eric Climent, Annaig Pedrono, L. Portela, More Authors
DOI
help_outline
https://doi.org/10.1002/cite.201900172
CFD simulation Control systems Electrical tomography Inline fluid separation Wire-mesh sensor
More Info
expand_more

Published Date

2020

Language

English

Faculty

Applied Sciences

Department

ChemE/Chemical Engineering

Research Group

ChemE/Transport Phenomena

Abstract

Today's mechanical fluid separators in industry are mostly operated without any control to maintain efficient separation for varying inlet conditions. Controlling inline fluid separators, on the other hand, is challenging since the process is very fast and measurements in the multiphase stream are difficult as conventional sensors typically fail here. With recent improvement of process tomography sensors and increased processing power of smart computers, such sensors can now be potentially used in inline fluid separation. Concepts for tomography-controlled inline fluid separation were developed, comprising electrical tomography and wire-mesh sensors, fast and massive data processing and appropriate process control strategy. Solutions and ideas presented in this paper base on process models derived from theoretical investigation, numerical simulations and analysis of experimental data.