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Neural-Genetic Optimization of Temperature Control in a Continuous Flow Polymerase Chain Reaction Microdevice

[+] Author Affiliations
Hing Wah Lee, Ishak A. Azid, Kankanhally N. Seetharamu

University of Science Malaysia (USM), Penang, Malaysia

Parthiban Arunasalam

State University of New York at Binghamton, Binghamton, NY

Paper No. IPACK2005-73280, pp. 733-740; 8 pages
doi:10.1115/IPACK2005-73280
From:
  • ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems collocated with the ASME 2005 Heat Transfer Summer Conference
  • Advances in Electronic Packaging, Parts A, B, and C
  • San Francisco, California, USA, July 17–22, 2005
  • Conference Sponsors: Heat Transfer Division and Electronic and Photonic Packaging Division
  • ISBN: 0-7918-4200-2 | eISBN: 0-7918-3762-9
  • Copyright © 2005 by ASME

abstract

In this study, a hybridized neural-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 zones of 94°C, 65°C and 72°C for the corresponding process of denaturing, annealing and extension to complete a cycle of polymerase chain reaction. Three separately addressable heaters provide heat input to each zone, microfluidic channels allow for the transport of fluid between zones and thermal isolation between the zones is maintained by machining air-gaps into the device. 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 are compared with published experimental work. Simulation results show good comparison with experimental work for the temperature control in each reaction zone of the microfluidic channels. The data is then used to train the ANN to predict the temperature distribution of the microfluidic channel for new heater input power and fluid flow rate. Using these data, optimization of temperature control in the CPCR device is achieved by embedding the trained ANN results as a fitness function into GA. The objective of the optimization is to minimize the temperature difference in each reaction zone of the microfluidic channel while satisfying the residence time requirement. Finally, the optimized results for the CPCR device are used to build a new FEA model for numerical simulation analysis. The simulation results for the neural-genetic optimized CPCR model and the initial CPCR model are then compared. The neural-genetic optimized model shows a significant improvement from the initial model establishing the optimization methods superiority.

Copyright © 2005 by ASME

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