scholarly journals Bioelectricity Production Using Microbial Fuel Cell–a Review

2020 ◽  
Vol 11 (2) ◽  
pp. 9420-9431

This paper summarised different methods used for the electrical power generation using microorganisms in MFC. In the past decade, Microbial Fuel Cells (MFC) attracted many researchers due to their ability to convert organic waste into electric currents by the usage of microorganisms. It has been developing as a great source of renewable energy. This device makes use of simple cathode and anode compartments and a separating membrane. This can be efficiently used for power generations and wastewater treatments. Microbial electrolysis cell (MEC), a type of MFC is also used in generating Hydrogen energy from various biological matters. The performance of MFC totally depends upon the nature of microorganisms, electrodes selected, and the separating membarane used. MFCs serve as a sustainable and alternate energy source to reduce the pollution caused by industrialization. In this review, a detailed explanation about MFC and different ways of generating bioelectricity and hydrogen from wastewater treatment are explained.

2020 ◽  
Vol 2 (1) ◽  
pp. 85

This paper summarises different methods used for the Electrical power generation using microorganisms in Microbial Fuel Cell (MFC), where power generation is done in a microbial environment. Microorganisms are used as catalysts to degrade the supplied source effectively. This bioelectricity production is carried out in an enhanced way in a pollution-free environment. This paper addresses different aspects of electricity generation with the help of microorganisms. Various types of Microbial fuel cells have been described based on their constructional details. One of the different power generation methods is wastewater treatment. Also, hydrogen is generated in this environment, which can be used in fuel cells. Different factors and catalysts used to produce bioelectricity are identified and analyzed. Finally, the power produced in those methods had been compared, and the best method is cited.


IJIREEICE ◽  
2017 ◽  
Vol 5 (6) ◽  
pp. 110-114
Author(s):  
Miss Niharika Verma ◽  
Miss Prachi Laddha ◽  
Miss Sushmita Priya ◽  
Mrs. Soman A. R.

2019 ◽  
Vol 5 (2) ◽  
pp. 39-47
Author(s):  
Sergey O, Starkov ◽  
Yury N, Lavrenkov

Hydrogen energy is able to solve the problem of the dependence of modern industries on fossil fuels and significantly reduce the amount of harmful emissions. One of the ways to produce hydrogen is high-temperature water-steam electrolysis. Increasing the temperature of the steam involved in electrolysis makes the process more efficient. The key problem is the use of a reliable heat energy source capable of reaching high temperatures. High-temperature gas-cooled reactors with a gaseous coolant and a graphite moderator provide a solution to the problem of heating the electrolyte. Part of the heat energy is used for producing electrical energy required for electrolysis. Modern electrolyzers built as arrays of tubular or planar electrolytic cells with a nuclear energy source make it possible to produce hydrogen by decomposing water molecules, and the working temperature control leads to a decrease in the Nernst potential. The operation of such facilities is complicated by the need to determine the optimal parameters of the electrolysis cell, the steam flow rate, and the operating current density. To reduce the costs associated with the process optimization, it is proposed to use a low-temperature electrolysis system controlled by a spiking neural network. The results confirm the effectiveness of intelligent technologies that implement adaptive control of hybrid modeling processes in order to organize the most feasible hydrogen production in a specific process, the parameters of which can be modified depending on the specific use of the reactor thermal energy. In addition, the results of the study confirm the feasibility of using a combined functional structure made on the basis of spiking neurons to correct the parameters of the developed electrolytic system. The proposed simulation strategy can significantly reduce the consumption of computational resources in comparison with models based only on neural network prediction methods.


Author(s):  
Peter Rez

Solar and wind power have low power densities. Large areas will be required to generate the electrical energy that we are using right now. These energy sources are intermittent, although sunshine is reasonably predictable in desert climates. Even in these ideal locations, fixed rooftop PV can only be used to meet a relatively small proportion of total electrical demand. Solar thermal with molten salt storage has a higher efficiency, and can better match electrical demands in these places. For wind turbines to generate their advertised or rated power, winds have to be blowing at about 12 m/sec (20 kt or 24 mph). In the United States, except in mountain passes and the Texas panhandle, this does not appear to happen very often. A simple test of whether a given renewable energy source is practical is to check whether it can meet the electrical demands of a single house.


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