scholarly journals LOCALIZED SUSTAINABLE AND ECOFRIENDLY ENERGY GENERATION AND DISTRIBUTION USING BLOCKCHAIN NETWORK: BANGLADESH PERSPECTIVE

2021 ◽  
Author(s):  
Md. Abdullah Mia
Keyword(s):  
Power Plants ◽  
Fossil Fuel ◽  
Banking System ◽  
Energy Generation ◽  
Load Shedding ◽  
Economic Perspective ◽  
Lower Efficiency ◽  
Transmission And Distribution ◽  
Core Problem ◽  
Fossil Fuel Energy

Thepower industry of Bangladesh is not enough sustainable and eco friendly which is governed by anumber of centralized authorities The energy is received from various power plants and then route theenergy to substations and eventually to the consumers which is palpably inadequate to meet the farreaching demand This leads to the load shedding in many areas which is still a burning question from thesocio economic perspective of Bangladesh A significant percentage of people are still deprived ofuninterrupted energy supply and a many more are still out of the energy distribution network due to lackof resources The energy currently available in the country is mainly fossil fuel energy which is neithersustainable nor eco friendly and the centralized distribution of energy requires the transmission of energyat high voltages which has a lower efficiency and adds to the wastage of energy Another core problem isunauthorized connection of energy and intentional faulty meter reading submission by operators whicheventually leads to a momentous loss of revenue, and this creates a sense of skepticism among the massusers on the local authorities responsible for proper energy management From the economic perspective,it is imperative to create a sustainable sector of investment for mass people due to the current fragile stateof stock market and banking system Addressing these issues is obligatory to maintain the economicgrowth of Bangladesh as well as to elevate the lifestyle of mass people The aim of our project is to attainthese milestones by creating a localized energy generation, storage, transmission and distribution systemusing blockchain which exhibits some genuine concern on pertinent areas and solution of the issues.

scholarly journals PYROLYSIS OF BIOMASS AS A SUITABLE ALTERNATIVE TO FOSSIL FUEL ENERGY IN NIGERIA: AN OVERVIEW

2020 ◽  
Vol 3 (1) ◽  
pp. 27-30
Author(s):  
Adeoye A.O ◽  
Quadri R.O. ◽  
Lawal O. S.
Keyword(s):  
Physical Environment ◽  
Alternative Energy ◽  
Fossil Fuel ◽  
Low Cost ◽  
Agricultural Waste ◽  
Energy Generation ◽  
Good Source ◽  
Suitable Alternative ◽  
Pyrolysis Of Biomass ◽  
Fossil Fuel Energy

Environmental problems associated with fossil fuel were highlighted to see the need for alternative energy in Nigeria. This review identified the various types of pyrolysis and their major products that make them fit as a suitable alternative energy source. It described pyrolysis as a means of converting waste to wealth and as a good source of energy generation thereby reducing reliance on fossil fuel. It proffers low-cost solutions for energy generation. The study as a whole contributed to the sustainability of the environment and removal of agricultural waste that constitute nuisance to Nigeria physical environment.

scholarly journals Energy production efficiency assessment using network data envelopment analysis

2021 ◽  
Author(s):  
Ramin Gharizadeh Beiragh ◽  
Reza Alizadeh ◽  
Mina Gharizadeh Beiragh ◽  
Dragan Pamucar
Keyword(s):  
Data Envelopment Analysis ◽  
Power Plants ◽  
Production Efficiency ◽  
Energy Generation ◽  
Network Data ◽  
Data Envelopment ◽  
Generation System ◽  
Network Efficiency ◽  
Network Data Envelopment Analysis ◽  
Transmission And Distribution

Abstract The performance of an energy generation system depends on three sectors of generation, transmission, and distribution. Especially when the power generation system has a decentralized structure in which different companies have different types of power plants, transmission, and distribution architectures. In this study, a network data envelopment analysis (NDEA) model is extended to assess the energy generation system's performance considering all three sectors of the regional electricity companies. There is a gap in the published DEA literature on energy network efficiency in studying the function of individual processes of these subsystems. After identifying the companies' structure, inputs, and outputs for all sectors, the extended NDEA model is implemented to estimate the efficiency of the whole energy generation network including production, transfer, and circulation sectors of the firms. The proposed model's network-based features allow computing the efficiency of individual subsystems and the whole system simultaneously. Iran’s energy generation system, including all three sectors during 2015–2019, is used to verify the model. The results indicate that while the annual average efficiency score of electricity companies has been increasing by 2018, it was decreased during 2018–2019. The model can be easily applied to energy generation systems in other countries.

scholarly journals Limitations of Greenhouse Gas Mitigation Technologies Set by Rapid Growth and Energy Cannibalism

2017 ◽  
Author(s):  
Joshua M. Pearce
Keyword(s):  
Growth Rate ◽  
Energy Efficiency ◽  
Greenhouse Gas ◽  
Rapid Growth ◽  
Power Plants ◽  
Fossil Fuel ◽  
Ghg Emissions ◽  
Net Energy ◽  
Technical Limitation ◽  
Fossil Fuel Energy

As the unacceptable results of continued fossil fuel combustion on climate change become ever clearer, a need to dramatically reduce greenhouse gas (GHG) emissions by aggressive energy conservation and immediate transitioning global civilization to alternative energy sources has become evident. Many energy technologies are capable of displacing significant volumes of fossil fuels. Unfortunately, neither the enormous scale of the current fossil fuel energy system nor the necessary growth rate of these technologies is well understood within the limits imposed by the net energy produced for a growing industry. This technical limitation is known as energy cannibalism and refers to an effect where rapid growth of an entire energy producing or energy efficiency industry creates a need for energy that uses (or cannibalizes) the energy of existing power plants or production plants. Thus during rapid growth, the industry as a whole produces no net energy because new energy (or conserved energy) is used to fuel the embodied energy of future power plants or production facilities. Such life cycle analysis is also valid for GHG emissions. All current technologies are dependent to some degree on fossil fuel energy and thus also contribute to emissions. This paper expands earlier work to generalize the GHG emission neutral growth rate limitation imposed by energy cannibalism to any renewable energy technology or any energy efficiency technology. Conclusions and recommendations are made from the analysis to assist decision makers in optimizing deployment of technologies on large scales to reduce GHG emissions to safe levels without overshoot.

Solar Thermal Energy

2018 ◽  
Author(s):  
E. L. Wolf
Keyword(s):  
Solar Cells ◽  
Thermal Energy ◽  
Fossil Fuel ◽  
Rankine Cycle ◽  
Carnot Cycle ◽  
The Sun ◽  
Solar Thermal Energy ◽  
Heat Engines ◽  
Direct Use ◽  
Fossil Fuel Energy

The Sun’s spectrum on Earth is modified by the atmosphere, and is harvested either by generating heat for direct use or for running heat engines, or by quantum absorption in solar cells, to be discussed later. Focusing of sunlight requires tracking of the Sun and is defeated on cloudy days. Heat engines have efficiency limits similar to the Carnot cycle limit. The steam turbine follows the Rankine cycle and is well developed in technology, optimally using a re-heat cycle of higher efficiency. Having learned quite a bit about how the Sun’s energy is created, and how that process might be reproduced on Earth, we turn now to methods for harvesting the energy from the Sun as a sustainable replacement for fossil fuel energy.

Capturing CO2 in flue gas from fossil fuel-fired power plants using dry regenerable alkali metal-based sorbent

2013 ◽  
Vol 39 (6) ◽  
pp. 515-534 ◽  
Author(s):  
Chuanwen Zhao ◽  
Xiaoping Chen ◽  
Edward J. Anthony ◽  
Xi Jiang ◽  
Lunbo Duan ◽  
...  
Keyword(s):  
Alkali Metal ◽  
Flue Gas ◽  
Power Plants ◽  
Fossil Fuel

Boiler Implosion Control in Fossil Fuel Power Plants

1985 ◽  
Vol 107 (4) ◽  
pp. 267-269 ◽  
Author(s):  
S. Z. Wu ◽  
D. N. Wormley ◽  
D. Rowell ◽  
P. Griffith
Keyword(s):  
Power Plants ◽  
Fossil Fuel ◽  
Companion Paper ◽  
Simulation Techniques ◽  
Power Plant Boiler ◽  
Induced Draft Fan ◽  
Fossil Fuel Power Plants ◽  
Computer Simulation Techniques ◽  
Plant Boiler ◽  
Furnace Pressure

An evaluation of systems for control of fossil fuel power plant boiler and stack implosions has been performed using computer simulation techniques described in a companion paper. The simulations have shown that forced and induced draft fan control systems and induced draft fan bypass systems reduce the furnace pressure excursions significantly following a main fuel trip. The limitations of these systems are associated with actuator range and response time and stack pressure excursions during control actions. Preliminary study suggests that an alternative control solution may be achieved by discharging steam into the furnace after a fuel trip.

Thermo-Economic Analysis of Microalgae Co-Firing Process for Fossil Fuel-Fired Power Plants

2010 ◽  
Author(s):  
Jian Ma ◽  
Oliver Hemmers
Keyword(s):  
Natural Gas ◽  
Flue Gas ◽  
Power Plants ◽  
Fossil Fuel ◽  
Construction Materials ◽  
Ghg Emissions ◽  
Combustion Efficiency ◽  
Pure Oxygen ◽  
Firing Process ◽  
Microalgal Biomass

A thermoeconomic analysis of microalgae co-firing process for fossil fuel-fired power plants is studied. A process with closed photobioreactor and artificial illumination is evaluated for microalgae cultivation, due to its simplicity with less influence from climate variations. The results from this process would contribute to further estimation of process performance and investment. The concept of co-firing (coal-microalgae or natural gas-microalgae) includes the utilization of CO2 from power plant for microalgal biomass culture and oxy-combustion of using oxygen generated by biomass to enhance the combustion efficiency. As it reduces CO2 emission by recycling it and uses less fossil fuel, there are concomitant benefits of reduced GHG emissions. The by-products (oxygen) of microalgal biomass can be mixed with air or recycled flue gas prior to combustion, which will have the benefits of lower nitrogen oxide concentration in flue gas, higher efficiency of combustion, and not too high temperature (avoided by available construction materials) resulting from coal combustion in pure oxygen. Two case studies show that there are average savings about $0.386 million/MW/yr and $0.323 million/MW/yr for coal-fired and natural gas-fired power plants, respectively. These costs saving are economically attractive and demonstrate the promise of microalgae technology for reducing greenhouse gas (GHG) emission.

scholarly journals Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

2019 ◽  
Vol 11 (9) ◽  
pp. 1117 ◽  
Author(s):  
Haopeng Zhang ◽  
Qin Deng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Power Plants ◽  
North China ◽  
Fossil Fuel ◽  
Environment Monitoring ◽  
Remote Sensing Images ◽  
Plant Monitoring ◽  
Working Status ◽  
Fossil Fuel Power Plants

The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring.

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