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TECHNICAL PAPERS: Fluids/Heat/Transport

Neuro-Genetic Optimization of Temperature Control for a Continuous Flow Polymerase Chain Reaction Microdevice

[+] Author and Article Information
Hing Wah Lee

 The Malaysian Institute of Microelectronic Systems (MIMOS) Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysiahingwah.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 13902parthi.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, Malaysia

J Biomech Eng 129(4), 540-547 (Dec 11, 2006) (8 pages) doi:10.1115/1.2746376 History: Received November 10, 2005; Revised December 11, 2006

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 94°C, 65°C, and 72°C 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 ±1°C 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 20min, whereas the FEA simulation takes approximately 7h 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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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 2

Finite element analysis (a) schemetic drawing of the CPCR device; and (b) CPCR device model for FEA analysis that consist of a single straight ceramic microfluidic channel measuring 65mm in total length

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Figure 3

CPCR microdevice unit cell for finite element modeling

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Figure 4

FEA temperature distribution results along the microchannel. Tolerance bands of ±1°C are shown about the set temperatures for denaturation (94°C) and extension (72°C), whereas for annealing, the tolerance band is shown for 55–65°C: (a) variation in fluid velocities; (b) variation in heater power 1; (c) variation in heater power 2; and (d) variation in heater power 3. Note: For any process parameter variation in the analysis, the remaining process parameters will be retained as the benchmarked value; i.e., heater power 1=580mW, heater power 2=80mW, heater power 3=300mW, and fluid velocity=1.3mm∕s.

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Figure 5

Comparison of simulation results between ANSYS and ANN. The conditions for this simulation are 1.3mm∕s for fluid velocity, 580mW for heater power 1,80mW for heater power 2, and 300mW for heater power 3.

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