Hydrate-based technology for CO2 capture from fossil fuel power plants

2014 ◽  
Vol 116 ◽  
pp. 26-40 ◽  
Author(s):  
Mingjun Yang ◽  
Yongchen Song ◽  
Lanlan Jiang ◽  
Yuechao Zhao ◽  
Xuke Ruan ◽  
...  
2021 ◽  
Author(s):  
Paolo Cotone ◽  
Matteo Mensi ◽  
VINCENZO TOTA ◽  
Keith Burnard

2014 ◽  
Vol 63 ◽  
pp. 18-26 ◽  
Author(s):  
Dumitru Cebrucean ◽  
Viorica Cebrucean ◽  
Ioana Ionel

Author(s):  
Jesu´s M. Escosa ◽  
Cristo´bal Cortes ◽  
Luis M. Romeo

Fossil fuel power plants account for about a third of global carbon dioxide emissions. Coal is the major power-generation fuel, being used twice as extensively as natural gas (IEA, 2003). Moreover, on a global scale, coal demand is expected to double over the period to 2030; IEA estimates that 4500 GWe of new installed power will be required. Coal is expected to provide 40% of this figure. It is thus obvious that coal power plants must be operative to provide such amount of energy in the short term, at the same time reducing their CO2 emissions in a feasible manner and increasing their efficiency and capacity. However, the main technologies currently considered to effect CO2 capture, both post-and pre-combustion, introduce a great economic penalty and largely reduce the capacity and efficiency. One of these technologies involves the separation of CO2 from high temperature flue gases using the reversible carbonation reaction of CaO and the calcination of CaCO3. The process is able to simultaneously capture sulfur dioxide. The major disadvantage of this well-known concept is the great amount of energy consumption in the calcinator and auxiliary equipment. This paper proposes a new, feasible approach to supply this energy which leads to an optimal integration of the process within a conventional coal power plant. Calcination is accomplished in a kiln fired by natural gas, whereas a gas turbine is used to supply all the auxiliary power. Flue gases from the kiln and the gas turbine can substitute a significant part of the heat duty of the steam cycle heaters, thus accomplishing feed water repowering of the steam turbine. This novel CO2-capture cycle is proposed to be integrated with aging coal-fired power plants. The paper shows that an optimal integration of both elements represents one of the best methods to simultaneously achieve: a) an increase of specific generating capacity in a very short period of time, b) a significant abatement of CO2 emissions, and c) an increase of plant efficiency in a cost-effective way.


Author(s):  
Ribooga Chang ◽  
Xianyue Wu ◽  
Ocean Cheung ◽  
Wen Liu

Carbon capture is an important and effective approach to control the emission of CO2 from point sources such as fossil fuel power plants, industrial furnaces and cement plants into the...


1985 ◽  
Vol 107 (4) ◽  
pp. 267-269 ◽  
Author(s):  
S. Z. Wu ◽  
D. N. Wormley ◽  
D. Rowell ◽  
P. Griffith

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.


2019 ◽  
Vol 11 (9) ◽  
pp. 1117 ◽  
Author(s):  
Haopeng Zhang ◽  
Qin Deng

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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