Adaptive Fuzzy Inference Control of the Recycle Compression System
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
The industry is increasing technology setting to improve manufacturing quality products and provide greater security for people to best use materials, leaving tiring or boring tasks to the machines. Every centrifugal or axial compressor has a characteristic combination of maximum discharge pressure and minimum flow beyond which it will surge. Preventing this damaging phenomenon is one of the most important tasks of a compressor control system. The most common way to prevent surge is to recycle a portion of the flow to keep the compressor away from its surge limit. Unfortunately, such recycling extracts an economic penalty due to the cost of compressing this extra flow. So the control system must be able to determine accurately how close the compressor is to surging so that it can maintain an adequate but not excessive recycle flow rate. The integrated control and protection systems are thus extremely important to companies and industries using turbo-compressors. To insure the functioning of the compression system it is necessary to develop a theory of command and control based on physical laws. In this paper, we are going to explore the ANFIS identification method integrated to PID controller in the recycle compression system. The results of simulation show a good estimation and control of the recycle compression system.
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Mohanty, K.B., Singh, M., Performance improvement of induction motor drive using feedback linearization and fuzzy torque compensator with RTDS implementation, (2012) International Review of Electrical Engineering (IREE), 7 (3), pp. 4374-4382.
Saghafinia, A., Kahourzade, S., Mahmoudi, A., Hew, W.P., Nasir Uddin, M., Broken rotor bar fault detection of 3-phase induction motor using online adaptive continuous wavelet transform and fuzzy logic, (2012) International Review of Electrical Engineering (IREE), 7 (3), pp. 4383-4394.
C. Hansen, Dynamic Simulation of Compressor Control Systems (Final Thesis, Aalberg Univ. Esbjerg ,2008).
D.Sanadgol, Active control of surge in centrifugal compressors using magnetic tip clearance actuation (PHD, Univ. of Virginia ,2006).
J.T Gravdahl, O.Egeland, Compressor surge and rotating stall: modeling and control (Springer-Verlag ,1999).
O.B. Bjørn, J.T. Gravdahl, The recycle Compression System, (Master of Science in Engineering. NTNU, 2010).
E.Greitzer, Surge and Rotating stall in axial flow compressors: Theoretical compression system model, J. of Engineering for Power 98, 1976, pp.190-198.
L.G Jonson, Surge Testing of Natural Gas Pipeline Centrifugal Compressors (Master thesis, Department of mechanical engineering, Calgary ,1998).
A.Cruz, ANFIS Adaptive Neuro-Fuzzy Inference Systems (Mestrado NCE, IM, UFRJ, 2001).
J.S. Roger Jang, Anfis: adaptive-Network-Based Fuzzy inference System. IEEE 23, 1993, pp.665-685.
E.Guner, Adaptive neuro fuzzy inference system applications in chemical process (Master thesis, the Middle East technical univ., 2003).
B. Chetate, R. Zamoum, A. Fegriche, M. Boumdin, PID and Novel Approach of PI Fuzzy Logic Controllers for Active Surge in Centrifugal Compressor. Ajse, Springer 38,2013, pp. pp.1405-1414.
R. Zamoum Boushaki, B. Chetate, Y. Zamoum, Artificial Neural Network Control of the Recycle Compression System, Studies in Informatics and Control, vol. 23 (1) 2014,pp. 65-76.
K. Beneda, J. Rohács, Dynamic Model of Variable Inducer Shroud Bleed for Centrifugal Compressor Surge Suppression, (2013) International Review of Aerospace Engineering (IREASE), 6 (3), pp. 163-173.
Ahmed Hafaifa, Ferhat Laaouad, Kouider Laroussi, Centrifugal Compressor Surge Detection and Isolation with Fuzzy Logic Controller, (2009) International Review of Automatic Control (IREACO), 2 (1), pp. 108-114.
Ahmed, S.A., Effects of diffuser geometry on the rotating stall characteristics, (2010) International Review of Mechanical Engineering (IREME), 4 (4), pp. 417-421.
Jawad, L.H., Abdullah, S., Zulkifli, R., Mahmood, W.M.F.W., Modelling of centrifugal compressor impellers using adaptive neuro- fuzzy inference systems (ANFIS), (2012) International Review of Mechanical Engineering (IREME), 6 (5), pp. 1011-1017.
Jawad, L.H., Abdullah, S., Zulkifli, R., Mahmood, W.M.F.W., Prediction of centrifugal compressor performance by using adaptive neuro-fuzzy inference system (ANFIS), (2012) International Review on Modelling and Simulations (IREMOS), 5 (4), pp. 1580-1587.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2022 Praise Worthy Prize