1/2008 - 2 |
Temperature Optimization of a Naphtha Splitter UnitNEVADO, A. , ONATE, L. M. , SANZ, J. , ALCALDE, R. , SLAVEN, K. |
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Author keywords
adaptive predictive expert, control, optimization, splitter unit
References keywords
control(26), sanchez(13), martin(11), predictive(8), adaptive(7), process(6), optimization(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2008-04-02
Volume 8, Issue 1, Year 2008, On page(s): 7 - 16
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.01002
Web of Science Accession Number: 000259903500002
SCOPUS ID: 77955651689
Abstract
A fully reliable and efficient adaptive control methodology has been long awaited in industry due to the time-varying nature of industrial plants. This paper demonstrates that this kind of adaptive solution is now available and simple to apply by presenting the first application of a methodology called Adaptive Predictive Expert (ADEX) Control in a petrochemical production unit. A description of the plant and the ADEX solution is followed by a comparative analysis of the results obtained with those of the existing conventional PID control. The objectives of the application involving the naphtha splitter at the Puertollano Refinery of Repsol were to establish the viability of ADEX in this environment, increase the quality of the naphtha products by achieving closer compliance with desired specification and maximizing the economic yield. To achieve these objectives it was necessary to enhance the stability of the naphtha splitter by improving control of the principal variables and eliminating a degree of interaction between them which was causing a resonance problem in the column. The application of the ADEX system confirm its viability, demonstrated a marked increase in column stability, significant improvements in levels of control and the elimination of the resonance problems. |
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[1] Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques, Hussein Humod Al Jlibawi, Ali, Othman, Mohammad Lutfi, Ishak, Aris, Moh Noor, Bahari S., Sajitt, Al Huseiny M. Sattar, IEEE Access, ISSN 2169-3536, Issue , 2022.
Digital Object Identifier: 10.1109/ACCESS.2021.3134931 [CrossRef]
[2] Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model, Piñón, Alejandro, Favela-Contreras, Antonio, Beltran-Carbajal, Francisco, Lozoya, Camilo, Dieck-Assad, Graciano, Actuators, ISSN 2076-0825, Issue 1, Volume 11, 2022.
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[3] A new control strategy for IKN-type coolers, Nevado, Antonio, Requena, Ricardo, Viudez, Daniel, Plano, Emilio, Control Engineering Practice, ISSN 0967-0661, Issue 9, Volume 19, 2011.
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[4] Adaptive predictive control of the sulfur recovery process at Pemex Cadereyta refinery, Raimondi, Angelo, Favela, Antonio, Estrada, Raul, Nevado, Antonio, Gracia, Edgar, International Journal of Adaptive Control and Signal Processing, ISSN 0890-6327, Issue 10, Volume 26, 2012.
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[5] Stable predictive control horizons, Estrada, Raúl, Favela, Antonio, Raimondi, Angelo, Nevado, Antonio, Requena, Ricardo, Beltrán-Carbajal, Francisco, International Journal of Control, ISSN 0020-7179, Issue 4, Volume 85, 2012.
Digital Object Identifier: 10.1080/00207179.2011.652363 [CrossRef]
[6] Survey of industrial optimized adaptive control, Martín‐Sánchez, Juan M., Lemos, João M., Rodellar, José, International Journal of Adaptive Control and Signal Processing, ISSN 0890-6327, Issue 10, Volume 26, 2012.
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Stefan cel Mare University of Suceava, Romania
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