Integrating online learning technology with computational fluid dynamics to control combustion process

Liu, X. and Bansal, R. C. (2011). Integrating online learning technology with computational fluid dynamics to control combustion process. In: IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2011, Trivandarum, Kerala, India, (303-306). 22-24 September 2011. doi:10.1109/RAICS.2011.6069323

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Author Liu, X.
Bansal, R. C.
Title of paper Integrating online learning technology with computational fluid dynamics to control combustion process
Conference name IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2011
Conference location Trivandarum, Kerala, India
Conference dates 22-24 September 2011
Journal name 2011 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2011
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/RAICS.2011.6069323
ISBN 9781424494781
Start page 303
End page 306
Total pages 4
Collection year 2012
Language eng
Abstract/Summary The work presented in this paper has been developed aiming at how to integrate an online learning controller with an online simulation module to control a complex combustion process, in which some critical process variables which are not easy to be measured using industry instruments. First, it is intended to design a neural network based adaptive controller which owns the ability of learning a real time process. This work consists of designing an online indirect adaptive controller based on radial basis function (RBF) and combining the controller with a numerical combustion process simulated using computational fluid dynamics (CFD). Secondly, the integrated system is simulated in Simulink. Finally, another proportional-integralderivation (PID) controller is built which substitutes the proposed online learning controller combined with CFD based simulation module to test the proposed control system. The performance of the two different controllers is compared and the results show that the online learning controller is more efficient than PID controller. Moreover, all the work show encouraging results that integrating online learning controller with CFD based online simulation module can provide a new strategy to control a complex combustion process in which instrument reading data is difficult to obtain.
Keyword Online Learning
Computational Fluid Dynamics
Radial Basis Function
Gradient Descent
Indirect Adaptive Control
CFD based simulation
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Mon, 19 Dec 2011, 15:14:36 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering