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Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception

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Abstract

Thermoelectric generators (TEGs) convert heat into electrical energy. These energy-conversion systems do not involve any moving parts and are made of thermoelectric (TE) elements connected electrically in a series and thermally in parallel; however, they are currently not suitable for use in regular operations due to their low efficiency levels. In order to produce high-efficiency TEGs, there is a need for highly heat-resistant thermoelectric materials (TEMs) with an improved figure of merit (ZT). Production and test methods used for TEMs today are highly expensive. This study attempts to estimate the Seebeck coefficient of TEMs by using the values of existing materials in the literature. The estimation is made within an artificial neural network (ANN) based on the amount of doping and production methods. Results of the estimations show that the Seebeck coefficient can approximate the real values with an average accuracy of 94.4%. In addition, ANN has detected that any change in production methods is followed by a change in the Seebeck coefficient.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey

    Fatih Uysal, Enes Kilinc, Huseyin Kurt & Selami Sagiroglu

  2. Center for Production and Application of Electronic Materials (EMUM), Dokuz Eylul University, Izmir, Turkey

    Erdal Celik

  3. Department of Metallurgical and Materials Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Turkey

    Erdal Celik

  4. Department of Nanoscience and Nanoengineering, The Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Turkey

    Erdal Celik

  5. Department of Industrial Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey

    Muharrem Dugenci

Authors
  1. Fatih Uysal
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  2. Enes Kilinc
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  3. Huseyin Kurt
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  4. Erdal Celik
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  5. Muharrem Dugenci
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  6. Selami Sagiroglu
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Correspondence to Fatih Uysal.

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Uysal, F., Kilinc, E., Kurt, H. et al. Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using Bee Algorithm Multi-layer Perception. J. Electron. Mater. 46, 4931–4938 (2017). https://doi.org/10.1007/s11664-017-5497-6

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  • DOI: https://doi.org/10.1007/s11664-017-5497-6

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