CO2 Emission Calculation and Emission Characteristics Analysis of Typical 600MW Coal-fired Thermal Power Unit

In order to effectively reduce the total CO2 emissions of coal-fired power plants and reduce greenhouse gas emissions, the relevant data of a typical 600MW coal-fired power plant in the past five years was collected and investigated, and CO2 emissions and emission intensity were calculated. And the results were used to measure the CO2 emission level of coal-fired power plants. By comparing and analyzing the CO2 emission intensity and emission trend of 600MW coal-fired units with different unit types and different fuel types, the CO2 emission characteristics of typical 600MW coal-fired power plants are obtained.

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Exploring Potential Pathways toward Energy-Related Carbon Emission Reduction in Heavy Industrial Regions of China: An Input–Output Approach

Sustainability ◽

10.3390/su12052148 ◽

2020 ◽

Vol 12 (5) ◽

pp. 2148 ◽

Cited By ~ 1

Author(s):

Jingyao Peng ◽

Yidi Sun ◽

Junnian Song ◽

Wei Yang

Keyword(s):

Energy Consumption ◽

Co2 Emissions ◽

Emission Intensity ◽

Emission Reduction ◽

Co2 Emission ◽

Carbon Emission ◽

Input Output ◽

Carbon Emission Reduction ◽

Industrial Regions ◽

Co2 Emission Intensity

It is a very urgent issue to reduce energy-related carbon emissions in China. The three northeastern provinces (Heilongjiang (HLJ), Jilin (JL), and Liaoning (LN)) are typical heavy industrial regions in China, playing an important role in the national carbon emission reduction target. In this study, we analyzed the energy consumption, carbon dioxide (CO2) emissions, and CO2 emission intensity of each sector in the three regions, and we compared them with the national level and those of China’s most developed province Guangdong (GD). Then, based on an input–output (I–O) framework, linkage analysis of production and CO2 emission from sector–system and sector–sector dimensions was conducted. The results showed that the three regions accounted for about 1/10 of China’s energy consumption and 1/6 of China’s CO2 emissions in 2012. In addition, the level of energy structure, CO2 emission intensity, and sectoral structure lagged behind China’s average level, much lower than those for GD. According to the sectoral characteristics of each region and unified backward/forward linkages of production and CO2 emissions, we divided sectoral clusters into those whose development was to be encouraged and those whose development was to be restricted. The results of this paper could provide policy–makers with reference to exploring potential pathways toward energy-related carbon emission reduction in heavy industrial regions.

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Towards space-borne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment

10.5194/amt-2019-414 ◽

2020 ◽

Author(s):

Johan Strandgren ◽

David Krutz ◽

Jonas Wilzewski ◽

Carsten Paproth ◽

Ilse Sebastian ◽

...

Keyword(s):

Power Plant ◽

Performance Assessment ◽

Co2 Emissions ◽

Co2 Emission ◽

Power Plants ◽

Point Sources ◽

Source Strength ◽

Spectral Window ◽

Instrument Noise ◽

Noise Error

Abstract. The UNFCCC (United Nations Framework Convention on Climate Change) requires the nations of the world to report their carbon dioxide (CO2) emissions. Independent verification of these reported emissions is a corner stone for advancing towards emission accounting and reduction measures agreed upon in the Paris agreement. In this paper, we present the concept and first performance assessment of a compact space-borne imaging spectrometer that could support the task of "monitoring, verification, reporting" (MVR) of CO2 emissions worldwide. With a single spectral window in the short-wave infrared spectral region and a spatial resolution of 50 x 50 m2, the goal is to reliably estimate the CO2 emissions from localized sources down to a source strength of approx. 1 MtCO2 yr-1, hence complementing other planned CO2 monitoring missions, like the planned European Carbon Constellation (CO2M). Resolving CO2 plumes also from medium-sized power plants (1–10 MtCO2 yr-1) is of key importance for independent quantification of CO2 emissions from the coal-fired power plant sector. Through radiative transfer simulations, including a realistic instrument noise model and a global trial ensemble covering various geophysical scenarios, it is shown that an instrument noise error of 1.1 ppm (1σ) can be achieved for the retrieval of the column-averaged dry-air mole fraction of CO2 (XCO2). Despite limited amount of information from a single spectral window and a relatively coarse spectral resolution, scattering by atmospheric aerosol and cirrus can be partly accounted for in the XCO2 retrieval, with deviations of at most 4.0 ppm from the true abundance for 68 % of the scenes in the global trial ensemble. We further simulate the ability of the proposed instrument concept to observe CO2 plumes from single power plants in an urban area using high-resolution CO2 emission and surface albedo data for the city of Indianapolis. Given the preliminary instrument design and the corresponding instrument noise error, emission plumes from point sources with an emission rate down to the order of 0.3 MtCO2 yr-1 can be resolved, i.e. well below the target source strength of 1 MtCO2 yr-1. Hence, the proposed instrument concept could be able to resolve and quantify the CO2 plumes from localized point sources responsible for approx. 90 % of the power plant CO2 emission budget, assuming global coverage through a fleet of sensors and favorable conditions with respect to illumination and particle scattering.

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Global and regional drivers of power plant CO2 emissions over the last three decades

10.31223/x5xc90 ◽

2022 ◽

Author(s):

Xinying Qin ◽

Dan Tong ◽

Fei Liu ◽

Ruili Wu ◽

Bo Zheng ◽

...

Keyword(s):

United States ◽

Power Plant ◽

Co2 Emissions ◽

Power Plants ◽

Fossil Fuel ◽

Driving Forces ◽

The United States ◽

The Past ◽

Power Plant Emissions ◽

Plant Emissions

The past three decades have witnessed the dramatic expansion of global biomass- and fossil fuel-fired power plants, but the tremendously diverse power infrastructure shapes different spatial and temporal CO2 emission characteristics. Here, by combining Global Power plant Emissions Database (GPED v1.1) constructed in this study and the previously developed China coal-fired power Plant Emissions Database (CPED), we analyzed global and regional changes in generating capacities, age structure, and CO2 emissions by fuel type and unit size, and further identified the major driving forces of these global and regional structure and emission trends over the past 30 years. Accompanying the growth of fossil fuel- and biomass-burning installed capacity from 1,774 GW in 1990 to 4,139 GW in 2019 (a 133.3% increase), global CO2 emissions from the power sector relatively increased from 7.5 Gt to 13.9 Gt (an 85.3% increase) during the same period. However, diverse developments and transformations of regional power units in fuel types and structure characterized various regional trends of CO2 emissions. For example, in the United States and Europe, CO2 emissions from power plants peaked before 2005, driven by the utilization of advanced electricity technologies and the switches from coal to gas fuel at the early stage. It is estimated the share of identified low-efficiency coal power capacity decreased to 4.3% in the United States and 0.6% in Europe with respectively 2.1% and 13.2% thermal efficiency improvements from 1990-2019. In contrast, CO2 emissions in China, India, and the rest of world are still steadily increasing because the growing demand for electricity is mainly met by developing carbon-intensive but less effective coal power capacity. The index decomposition analysis (IDA) to identify the multi-stage driving forces on the trends of CO2 emissions further suggests different global and regional characteristics. Globally, the growth of demand mainly drives the increase of CO2 emissions for all stages (i.e. 1990-2000, 2000-2010 and 2010-2019). Regional results support the critical roles of thermal efficiency improvement (accounting for 20% of the decrease in CO2 emissions) and fossil fuel mix (61%) in preventing CO2 emission increases in the developed regions (e.g., the United States and Europe). The decrease of fossil fuel share gradually demonstrates its importance in carrying the positive effects on curbing emissions in the most of regions, including the developing economics (i.e. China and India) after 2010 (accounting for 46% of the decrease in CO2 emissions). Our results highlight the contributions of different driving forces to emissions have significantly changed over the past 30 years, and this comprehensive analysis indicates that the structure optimization and transformations of power plants is paramount importance to curb or further reduce CO2 emissions from the power sector in the future.

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A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-3-55-2010 ◽

2010 ◽

Vol 3 (1) ◽

pp. 55-110 ◽

Cited By ~ 5

Author(s):

H. Bovensmann ◽

M. Buchwitz ◽

J. P. Burrows ◽

M. Reuter ◽

T. Krings ◽

...

Keyword(s):

Remote Sensing ◽

Power Plant ◽

Greenhouse Gas ◽

Co2 Emissions ◽

Kyoto Protocol ◽

Inverse Modeling ◽

Co2 Emission ◽

Power Plants ◽

Independent Verification ◽

Anthropogenic Co2

Abstract. Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas causing global warming. The atmospheric CO2 concentration increased by more than 30% since pre-industrial times – primarily due to burning of fossil fuels – and still continues to increase. Reporting of CO2 emissions is required by the Kyoto protocol. Independent verification of reported emissions, which are typially not directly measured, by methods such as inverse modeling of measured atmospheric CO2 concentrations is currently not possible globally due to lack of appropriate observations. Existing greenhouse gas observing satellites such as SCIAMACHY and GOSAT focus on advancing our understanding of natural CO2 sources and sinks. The obvious next step for future generation satellites is to also measure anthropogenic CO2 emissions. Here we present a promising satellite remote sensing technology based on spectroscopic measurements of reflected solar radiation in the short-wave infrared (SWIR) and near-infrared (NIR) spectral regions and show, using power plants as an example, that strong localized CO2 point sources can be detected and their emissions quantified. This requires mapping the CO2 column distribution at a spatial resolution of 2×2 km2 or better with a precision of about 0.5% (2 ppm) or better of the background column. We indicate that this can be achieved with existing technology. For a single satellite in sun-synchronous orbit with an across-track swath width of 500 km each power plant is overflown every 6 days or faster. Based on clear sky statistics we conservatively estimate that about one useful measurement per 1–2 months for a given power plant can typically be achieved. We found that the uncertainty of the retrieved power plant CO2 emission during a single satellite overpass is in the range 0.5–5 MtCO2/year – depending on observation conditions – which is about 2–20% of the CO2 emission of large power plants (25 Mt CO2/year). The investigated instrument aims at fulfilling all requirements for global regional-scale CO2 and CH4 surface flux inverse modeling. Using a significantly less demanding instrument concept based on a single SWIR channel we indicate that this also enables the monitoring of power plant CO2 emissions in addition to high-quality methane retrievals. The latter has already been demonstrated by SCIAMACHY. The discussed technology has the potential to significantly contribute to an independent verification of reported anthropogenic CO2 emissions and therefore could be an important component of a future global anthropogenic CO2 emission monitoring system. This is of relevance in the context of Kyoto protocol follow-on agreements but also allows to detect and monitor strong natural CO2 and CH4 emitters such as (mud) volcanoes.

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Decomposition Analysis of the Carbon Emissions of the Manufacturing and Industrial Sector in Thailand

Energies ◽

10.3390/en13040798 ◽

2020 ◽

Vol 13 (4) ◽

pp. 798

Author(s):

Jaruwan Chontanawat ◽

Paitoon Wiboonchutikula ◽

Atinat Buddhivanich

Keyword(s):

Co2 Emissions ◽

Emission Intensity ◽

Co2 Emission ◽

Energy Intensity ◽

Manufacturing Sector ◽

Decomposition Analysis ◽

Intensity Change ◽

Positive Contribution ◽

Intensity Effect ◽

Co2 Emission Intensity

Since the 1990s, CO2 emissions have increased steadily in line with the growth of production and the use of energy in the manufacturing sector in Thailand. The Logarithmic Mean Divisia Index Method is used for analysing the sources of changes in CO2 emissions as well as the CO2 emission intensity of the sector in 2000–2018. On average throughout the period, both the amount of CO2 emissions and the CO2 emission intensity increased each year relative to the baseline. The structural change effect (effect of changes of manufacturing production composition) reduced, but the intensity effect (effect of changes of CO2 emissions of individual industries) increased the amount of CO2 emissions and the CO2 emission intensity. The unfavourable CO2 emission intensity change came from the increased energy intensity of individual industries. The increased use of coal and electricity raised the CO2 emissions, whereas the insignificant change in emission factors showed little impact. Therefore, the study calls for policies that decrease the energy intensity of each industry by limiting the use of coal and reducing the electricity used by the manufacturing sector so that Thailand can make a positive contribution to the international community’s effort to achieve the goal of CO2 emissions reduction.

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MITIGATION OF CO2 EMISSIONS USING LOW AND NEAR ZERO CO2 EMISSION POWER PLANTS

Clean Air ◽

10.1615/interjenercleanenv.v4.i1.20 ◽

2003 ◽

Vol 4 (1) ◽

pp. 21-36 ◽

Cited By ~ 2

Author(s):

P. Mathieu

Keyword(s):

Co2 Emissions ◽

Co2 Emission ◽

Power Plants ◽

Emission Power

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Nonlinear Trend Analysis of Mill Fan System Vibrations for Predictive Maintenance and Diagnostics

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-012-0048-9 ◽

2012 ◽

Vol 58 (4) ◽

pp. 351-356

Author(s):

Mincho B. Hadjiski ◽

Lyubka A. Doukovska ◽

Stefan L. Kojnov

Keyword(s):

Power Plant ◽

Trend Analysis ◽

Thermal Power Plant ◽

Power Plants ◽

Thermal Power ◽

Balkan Peninsula ◽

Predictive Maintenance ◽

Nonlinear Trend ◽

The Subject ◽

The Given

Abstract Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.

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Features of antibodies against benzo[a]pyrene formation in workers of coal mines and thermal power plants

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-1-9-14 ◽

2019 ◽

pp. 9-14

Author(s):

Ye. G. Polenok ◽

S. A. Mun ◽

L. A. Gordeeva ◽

A. A. Glushkov ◽

M. V. Kostyanko ◽

...

Keyword(s):

Power Plant ◽

Thermal Power Plant ◽

Power Plants ◽

Reference Group ◽

Coal Dust ◽

Thermal Power ◽

Coal Mines ◽

Serum Levels ◽

Thermal Power Plants ◽

Immune Reactions

Introduction.Coal dust and coal fi ring products contain large amounts of carcinogenic chemicals (specifically benz[a]pyrene) that are different in influence on workers of coal mines and thermal power plants. Specific immune reactions to benz[a]pyrene therefore in these categories of workers can have specific features.Objective.To reveal features of antibodies specifi c to benz[a]pyrene formation in workers of coal mines and thermal power plants.Materials and methods.The study covered A and G class antibodies against benz[a]pyrene (IgA-Bp and IgG-Bp) in serum of 705 males: 213 donors of Kemerovo blood transfusion center (group 1, reference); 293 miners(group 2) and 199 thermal power plant workers (group 3). Benz[a]pyrene conjugate with bovine serum albumin as an adsorbed antigen was subjected to immune-enzyme assay.Results.IgA-Bp levels in the miners (Me = 2.7) did not differ from those in the reference group (Me = 2.9), but in the thermal power plant workers (Me = 3.7) were reliably higher than those in healthy men and in the miners (p<0.0001). Levels of IgG-Bp in the miners (Me = 5.0) appeared to be lower than those in the reference group (Me = 6.4; (p = 0.05). IgG-Bb level in the thermal power plantworkers (Me = 7.4) exceeded the parameters in the healthy donors and the miners (p<0.0001). Non-industrial factors (age and smoking) appeared tohave no influence on specific immune reactions against benz[a]pyrene in the miners and the thermal power plant workers.Conclusions.Specific immune reactions against benz[a]pyrene in the miners and the thermal power plant workers are characterized by peculiarities: the miners demonstrate lower levels of class A serum antibodies to benz[a]pyrene; the thermal power plant workers present increased serum levels of class G antibodies to benz[a]pyrene. These peculiarities result from only the occupational features, but do not depend on such factors as age, smoking and length of service at hazardous production. It is expedient to study specific immune reactions to benz[a]pyrene in workers of coal mines and thermal power plants, to evaluate individual oncologic risk and if malignancies occur.

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Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽

10.3390/atmos12070811 ◽

2021 ◽

Vol 12 (7) ◽

pp. 811

Author(s):

Yaqin Hu ◽

Yusheng Shi

Keyword(s):

Power Plant ◽

Co2 Emissions ◽

Atmospheric Carbon Dioxide ◽

Power Plants ◽

Large Scale ◽

Global Climate ◽

Gaussian Model ◽

Point Sources ◽

Emission Inventories ◽

Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

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