In this study, a hybridized neuro-genetic optimization methodology realized by embedding finite element analysis (FEA) trained artificial neural networks (ANN) into genetic algorithms (GA), is used to optimize temperature control in a ceramic based continuous flow polymerase chain reaction (CPCR) device. The CPCR device requires three thermally isolated reaction zones of , , and for the denaturing, annealing, and extension processes, respectively, to complete a cycle of polymerase chain reaction. The most important aspect of temperature control in the CPCR is to maintain temperature distribution at each reaction zone with a precision of or better, irrespective of changing ambient conditions. Results obtained from the FEA simulation shows good comparison with published experimental work for the temperature control in each reaction zone of the microfluidic channels. The simulation data are then used to train the ANN to predict the temperature distribution of the microfluidic channel for various heater input power and fluid flow rate. Once trained, the ANN analysis is able to predict the temperature distribution in the microchannel in less than , whereas the FEA simulation takes approximately to do so. The final optimization of temperature control in the CPCR device is achieved by embedding the trained ANN results as a fitness function into GA. Finally, the GA optimized results are used to build a new FEA model for numerical simulation analysis. The simulation results for the neuro-genetic optimized CPCR model and the initial CPCR model are then compared. The neuro-genetic optimized model shows a significant improvement from the initial model, establishing the optimization method’s superiority.
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e-mail: hingwah.lee@mimos.my
e-mail: parthi.arunasalam@binghamton.edu
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August 2007
Technical Papers
Neuro-Genetic Optimization of Temperature Control for a Continuous Flow Polymerase Chain Reaction Microdevice
Hing Wah Lee,
e-mail: hingwah.lee@mimos.my
Hing Wah Lee
The Malaysian Institute of Microelectronic Systems (MIMOS) Berhad
, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia
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Parthiban Arunasalam,
Parthiban Arunasalam
Department of Mechanical Engineering, T. J. Watson School of Engineering and Applied Science,
e-mail: parthi.arunasalam@binghamton.edu
State University of New York at Binghamton
, P.O. Box 6000, Binghamton, NY 13902
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William P. Laratta,
William P. Laratta
Agave BioSystems
, Ithaca, NY 14850
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Kankanhalli N. Seetharamu,
Kankanhalli N. Seetharamu
Sri Bhagawan Mahaveer Jain College of Engineering
, Jakkasandra Post, Kanakapura Taluk, Bangalore Rural District 562112, India
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Ishak A. Azid
Ishak A. Azid
School of Mechanical Engineering,
University of Science Malaysia
, 14300 Nibong Tebal, Penang, Malaysia
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Hing Wah Lee
The Malaysian Institute of Microelectronic Systems (MIMOS) Berhad
, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysiae-mail: hingwah.lee@mimos.my
Parthiban Arunasalam
Department of Mechanical Engineering, T. J. Watson School of Engineering and Applied Science,
State University of New York at Binghamton
, P.O. Box 6000, Binghamton, NY 13902e-mail: parthi.arunasalam@binghamton.edu
William P. Laratta
Agave BioSystems
, Ithaca, NY 14850
Kankanhalli N. Seetharamu
Sri Bhagawan Mahaveer Jain College of Engineering
, Jakkasandra Post, Kanakapura Taluk, Bangalore Rural District 562112, India
Ishak A. Azid
School of Mechanical Engineering,
University of Science Malaysia
, 14300 Nibong Tebal, Penang, MalaysiaJ Biomech Eng. Aug 2007, 129(4): 540-547 (8 pages)
Published Online: December 11, 2006
Article history
Received:
November 10, 2005
Revised:
December 11, 2006
Citation
Lee, H. W., Arunasalam, P., Laratta, W. P., Seetharamu, K. N., and Azid, I. A. (December 11, 2006). "Neuro-Genetic Optimization of Temperature Control for a Continuous Flow Polymerase Chain Reaction Microdevice." ASME. J Biomech Eng. August 2007; 129(4): 540–547. https://doi.org/10.1115/1.2746376
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