Analysis of Measured Current Densities and Power Densities Obtained From a Microbial Fuel Cell Using Saltbridge as Membrane

Uddin, Sheikh Shehab; Uddin, Sheikh Nayeem; Roni, Kazi Shoffiuddin; Shatil, Abu Hena; Kabir, Fahmid

doi:10.1109/cepe.2019.8726789

Cited by 1 publication

(1 citation statement)

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“…Further changes to the cell's design are made following various studies and budgets. Because the membrane between the anodic and cathodic chambers is usually quite expensive due to its material, various studies have attempted to eliminate or use less expensive materials for the membrane in a microbial fuel cell [10,11]. Jang et al (2004) experimented with constructing a microbial fuel cell with no membrane and no mediator.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Applications of MFC and Recent Progress of Artificial Intelligence and Machine Learning Techniques and Applications, with competing fuel cells

et al. 2022

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Tomorrow is a technology for Microbial fuel cells (MFC). It has attracted numerous studies for the continuous development of cell efficiency since the problem of the coming era can be resolved. Implementing artificial learning and machine learning is a change that can effectively achieve the goals. A microbial fuel cell constitutes a complex non-linear procedure that preferably needs a strategy that is not a linear control strategy for the most optimum result. Rather than making a computationally tedious and heavy non-linear control strategy, a superior single linear model or gain scheduling, or multiple models-based control techniques are the practical and feasible ways to tackle the non-linearity existing in the Microbial Fuel Cell. Machine learning and Artificial Intelligence help reduce computation and model costs. It saves time and is more efficient than previously used manual methods, which are now obsolete. In order to find the most accurate results, the study would compare all currently available research efforts and focus on implementing Artificial Intelligence and Machine learning concepts within the Microbial Fuel Cell and comparison with other fuel cells.

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Section: Introductionmentioning

confidence: 99%