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Controlled Inline Fluid Separation Based on Smart Process Tomography Sensors
Author(s) -
Sahovic Benjamin,
Atmani Hanane,
Sattar Muhammad Awais,
Garcia Matheus Martinez,
Schleicher Eckhart,
Legendre Dominique,
Climent Eric,
Zamansky Remi,
Pedrono Annaig,
Babout Laurent,
Banasiak Robert,
Portela Luis M.,
Hampel Uwe
Publication year - 2020
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.201900172
Subject(s) - tomography , process (computing) , separation (statistics) , separation process , computer science , process engineering , mechanical engineering , control engineering , engineering , physics , machine learning , chemical engineering , optics , operating system
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.