Deep learning-based optimization of a microfluidic membraneless fuel cell for maximum power density via data-driven three-dimensional multiphysics simulation.

Journal: Bioresource technology
Published Date: Feb 8, 2022

Abstract

A deep learning-based method for optimizing a membraneless microfluidic fuel cell (MMFC)performance by combining the artificial neural network (ANN) and genetic algorithm (GA) was for the first time introduced. A three-dimensional multiphysics model that had an accuracy equivalent to experimental results (R = 0.976) was employed to generate the ANN's training data. The constructed ANN is equivalent to the simulation (R = 0.999) but with far better computation resource efficiency as the ANN's execution time is only 0.041 s. The ANN model is then used by the GA to determine the inputs (microchannel length = 10.040 mm, width = 0.501 mm, height = 0.635 mm; temperature = 288.210 K, cell voltage = 0.309 V) that lead to the maximum power density of 0.263 mWcm (current density of 0.852 mAcm) of the MMFC. The ANN-GA and numerically calculated maximum power densities differed only by 0.766%.

Authors

  • Dang Dinh Nguyen
    School of Mechanical Engineering, Kyungpook National University, Daegu 41566, South Korea; National Research Institute of Mechanical Engineering, No.4 Pham Van Dong street, Cau Giay district, Ha Noi, Viet Nam.
  • Thinh Quy Duc Pham
    Institute of Strategies Development, Thu Dau Mot University, 06 Tran Van On, Phu Hoa, Binh Duong, Viet Nam.
  • Muhammad Tanveer
    School of Mechanical Engineering, Kyungpook National University, Daegu 41566, South Korea.
  • Haroon Khan
    Neuroscience and Neuroengineering Research Center, Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Ji Won Park
    School of Mechanical Engineering, Kyungpook National University, Daegu 41566, South Korea.
  • Cheol Woo Park
    School of Mechanical Engineering, Kyungpook National University, Daegu 41566, South Korea.
  • Gyu Man Kim
    School of Mechanical Engineering, Kyungpook National University, Daegu 41566, South Korea. Electronic address: gyuman.kim@knu.ac.kr.

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