Environmental and Operational Performance Evaluation of Taiwan Thermal Power Plants

An integrated environmental and operational efficiency model for evaluation of seven thermal power plants in Taiwan was constructed by data envelopment analysis (DEA). Inputs and desirable outputs along with 3 undesirable outputs, including CO2, SOx, and NOx emissions were simulated. From results we found that the integrated efficiency and the production scale of most plants were inefficient during 2001- 2008. Reductions in fuel consumption and CO2 emission are the major solutions for efficiency improvement. Other improvements include enhancing pollution control measures and optimizing power plant scales. Also, clean-coal technology and lower-carbon fuels should be enhanced to reduce CO2 emissions from thermal power plants in Taiwan. In addition, flue gas desulfurization (FGD) and high efficient selective catalytic reduction (SCR) and advanced low-NOx burners can be installed to remove extra SOx and NOx emissions. Findings of this study can be of value for improving environmental and operational performance of thermal power plants in Taiwan as well as countries with similar concerns.

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The Suitability of Fly Ash for Use in Concrete According to EN 450 Based on their Particle Size

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.276.110 ◽

2018 ◽

Vol 276 ◽

pp. 110-115

Author(s):

Martin Ťažký ◽

Martin Labaj ◽

Rudolf Hela

Keyword(s):

Fly Ash ◽

Power Plants ◽

Catalytic Reduction ◽

Raw Materials ◽

Thermal Power ◽

Combustion Process ◽

Thermal Power Plants ◽

Mechanical Parameters ◽

Fly Ashes ◽

The Impact

The by-products of energy industry are nowadays often affected by new limits governing the production of harmful gases discharged into the air. These stricter and stricter criteria are often met by electricity producers by changing the combustion process in thermal power plants itself. Nowadays, the SNCR (selective non-catalytic reduction) application is quite common in the combustion process in order to help reduce the nitrogen oxide emission. This article deals with the primary measures of thermal power plants, which in particular consist of a modified treatment of raw materials (coal) entering the combustion process. These primary measures then often cause the formation of fly ash with unsuitable fineness for the use in concrete according to EN 450. The paper presents the comparison of the physico-mechanical parameters of several fly ashes with a different fineness values. The primary task is to assess the impact of non-suitable granulometry in terms of EN 450 on the other physico-mechanical parameters of fly ashes sampled within the same thermal power plant. Several fly ashes produced in the Czech Republic and surrounding countries were evaluated in this way.

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Scenario Analysis of Denitration for Chinese Coal-Fired Power Generation

Materials Science Forum ◽

10.4028/www.scientific.net/msf.814.425 ◽

2015 ◽

Vol 814 ◽

pp. 425-429 ◽

Cited By ~ 3

Author(s):

Xian Ce Meng ◽

Chen Li ◽

Su Ping Cui ◽

Li Li Zhao ◽

Xian Zheng Gong ◽

...

Keyword(s):

Power Generation ◽

Scenario Analysis ◽

Power Plants ◽

Fossil Fuels ◽

Catalytic Reduction ◽

Thermal Power ◽

Power Industry ◽

Nox Emissions ◽

Technology Applications ◽

Chinese Coal

The environmental loads are made due to the natural resources and fossil fuels use and pollutants emissions by Chinese thermal power industry. To explore the realistic coal-fired power generation and its denitration strategies, the input and output of coal-fired power generation in China were identified and quantified. The scope of this paper is defined in the boundary of coal-fired electricity generation system all over China. The methodology follows the principal of ISO 14040 and ISO 14044. The functional unit is “1 kWh of electricity generated”. The inventory data of Chinese coal-fired power generation in 2009 without denitration technology applications were measured. The output data include the CO, N2O, CH4, CO2, NOx, PM and SO2 emissions. NOx emissions are the major contributor of acidification and photochemical in China. To avoid catastrophic environmental damages, the air pollution especially NOx emissions from coal-fired power plants are advised to be cut. For scenario analysis, in the assumption of 100%of selective non-catalytic reduction (SNCR) technology applications, China still has denitration potential. In the coming several decades, the SNCR technology will be decisive for the Chinese coal-fired power industry to reach deeper NOx emission reductions. However, the reduction agents of ammonia and urea usage bring ammonia slip, and extra natural resource and fossils consumption. The urea use also brings extra CO2 emissions. This limits the applications of SNCR technology to reduce NOx emissions.

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Estimation of NOx Emissions in Thermal Power Plants Using Neural Networks

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.1367339 ◽

2001 ◽

Vol 123 (2) ◽

pp. 465-471 ◽

Cited By ~ 26

Author(s):

G. Ferretti ◽

L. Piroddi

Keyword(s):

Neural Network ◽

Power Plants ◽

Thermal Power ◽

Three Dimensional ◽

Estimation Procedure ◽

Thermal Power Plants ◽

Nox Emissions ◽

Generation Process ◽

Trained Neural Network ◽

Local Nature

In this paper a neural network-based strategy is proposed for the estimation of the NOx emissions in thermal power plants, fed with both oil and methane fuel. A detailed analysis based on a three-dimensional simulator of the combustion chamber has pointed out the local nature of the NOx generation process, which takes place mainly in the burners’ zones. This fact has been suitably exploited in developing a compound estimation procedure, which makes use of the trained neural network together with a classical one-dimensional model of the chamber. Two different learning procedures have been investigated, both based on the external inputs to the burners and a suitable mean cell temperature, while using local and global NOx flow rates as learning signals, respectively. The approach has been assessed with respect to both simulated and experimental data.

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