Dynamic analysis and multiple model control of continuous microbial fuel cell (CMFC)

Yewale, Ashish; Methekar, Ravi; Agrawal, Shailesh

doi:10.1016/j.cherd.2019.06.007

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…Thus, one can choose one of these as a manipulated input variable. It can be chosen based on the type of control strategy and application of MFC [75]. Yan and Fan proposed a fuzzy proportional integral derivative (PID) controller for dual-chamber MFC to maintain a constant output voltage [76].…”

Section: Developments In Control Strategies Of Mfcsmentioning

confidence: 99%

A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells

2020

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A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this field needs interdisciplinary knowledge and involves diverse areas such as biological, chemical, electrical, etc. In recent decades, rapid strides have taken place in fuel cell research and this technology has become more efficient. For effective usage, such devices need advanced control techniques for maintaining a balance between substrate supply, mass, charge, and external load. Most of the research work in this area focuses on experimental work and have been described from the design perspective. Recently, the development in mathematical modeling of such cells has taken place which has provided a few mathematical models. Mathematical modeling provides a better understanding of the operations and the dynamics of MFCs, which will help to develop control and optimization strategies. Control-oriented bio-electrochemical models with mass and charge balance of MFCs facilitate the development of advanced nonlinear controllers. This work reviews the different mathematical models of such cells available in the literature and then presents suitable parametrization to develop control-oriented bio-electrochemical models of three different types of cells with their uncertain parameters.

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Section: Developments In Control Strategies Of Mfcsmentioning

confidence: 99%

A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells

2020

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Machine Learning in Bioelectrocatalysis

Huang,

Gao,

Chang

et al. 2023

Advanced Science

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At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high‐value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

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Dynamic analysis and split range control for maximization of operating range of continuous microbial fuel cell

Yewale

Methekar

Agrawal

2020

Chinese Journal of Chemical Engineering

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Dynamic analysis and multiple model control of continuous microbial fuel cell (CMFC)

Cited by 6 publications

References 44 publications

A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells

A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells

Machine Learning in Bioelectrocatalysis

Dynamic analysis and split range control for maximization of operating range of continuous microbial fuel cell

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