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Designing and Testing a Chemical Demulsifier Dosage Controller in a Crude Oil Desalting Plant: An Artificial Intelligence‐Based Network Approach
Author(s) -
Alshehri A. K.,
RicardezSandoval L. A.,
Elkamel A.
Publication year - 2010
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.200900615
Subject(s) - demulsifier , artificial neural network , controller (irrigation) , process engineering , engineering , chemical plant , salt (chemistry) , process (computing) , crude oil , control engineering , petroleum engineering , computer science , chemistry , chemical engineering , artificial intelligence , agronomy , biology , operating system
The aim of this paper is to present an artificial neural network (ANN) controller trained on a historical data set that covers a wide operating range of the fundamental parameters that affect the demulsifier dosage in a crude oil desalting process. The designed controller was tested and implemented on‐line in a gas‐oil separation plant. The results indicate that the current control strategy overinjects chemical demulsifier into the desalting process whereas the proposed ANN controller predicts a lower demulsifier dosage while keeping the salt content within its specification targets. Since an on‐line salt analyzer is not available in the desalting plant, an ANN based on historical measurements of the salt content in the desalting process was also developed. The results show that the predictions made by this ANN controller can be used as an on‐line strategy to predict and control the salt concentration in the treated oil.