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Start-Up Process Modelling of Sediment Microbial Fuel Cells Based on Data Driven

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  • Fengying Ma
  • Yankai Yin
  • Min Li

Abstract

Sediment microbial fuel cells (SMFCs) are a typical microbial fuel cell without membranes. They are a device developed on the basis of electrochemistry and use microbes as catalysts to convert chemical energy stored in organic matter into electrical energy. This study selected a single-chamber SMFC as a research object, using online monitoring technology to accurately measure the temperature, pH, and voltage of the microbial fuel cell during the start-up process. In the process of microbial fuel cell start-up, the relationship between temperature, pH, and voltage was analysed in detail, and the correlation between them was calculated using SPSS software. The experimental results show that, at the initial stage of SMFC, the purpose of rapid growth of power production can be achieved by a large increase in temperature, but once the temperature is reduced, the power production of SMFC will soon recover to the state before the temperature change. At the beginning of SMFC, when the temperature changes drastically, pH will change the same first, and then there will be a certain degree of rebound. In the middle stage of SMFC start-up, even if the temperature will return to normal after the change, a continuous temperature drop in a short time will lead to a continuous decrease in pH value. The RBF neural network and ELM neural network were used to perform nonlinear system regression in the later stage of SMFC start-up and using the regression network to forecast part of the data. The experimental results show that the ELM neural network is more excellent in forecasting SMFC system. This article will provide important guidance for shortening start-up time and increasing power output.

Suggested Citation

  • Fengying Ma & Yankai Yin & Min Li, 2019. "Start-Up Process Modelling of Sediment Microbial Fuel Cells Based on Data Driven," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, January.
  • Handle: RePEc:hin:jnlmpe:7403732
    DOI: 10.1155/2019/7403732
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    Cited by:

    1. Xiao Zhang & Feng Ding & Ling Xu & Ahmed Alsaedi & Tasawar Hayat, 2019. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise," Mathematics, MDPI, vol. 7(4), pages 1-17, April.
    2. Lijuan Wan & Ximei Liu & Feng Ding & Chunping Chen, 2019. "Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems," Mathematics, MDPI, vol. 7(7), pages 1-20, July.
    3. Feng Ding & Jian Pan & Ahmed Alsaedi & Tasawar Hayat, 2019. "Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data," Mathematics, MDPI, vol. 7(5), pages 1-15, May.
    4. Jianping Yuan & Jin Li & Zhihui Dong & Qinglong Chen & Hanbing Sun, 2022. "A Method of Reducing Invalid Steering for AUVs Based on a Wave Peak Frequency Tracker," Sustainability, MDPI, vol. 14(22), pages 1-14, November.

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