Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions

2003 ◽  
Vol 5 (2) ◽  
pp. 107-126 ◽  
Author(s):  
K. Li ◽  
S. Thompson ◽  
P. A. Wieringa ◽  
J. Peng ◽  
G. R. Duan
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Power Plant ◽  
Supervisory Control ◽  
Fossil Fuel ◽  
Power Plant Emissions ◽  
Fossil Fuel Power Plant ◽  
Plant Emissions ◽  
Human Supervisory Control

scholarly journals Estimating Human Health Impacts and Costs Due to Iranian Fossil Fuel Power Plant Emissions through the Impact Pathway Approach

Energies ◽  
2017 ◽  
Vol 10 (12) ◽  
pp. 2136 ◽  
Author(s):  
Mojtaba Jorli ◽  
Steven Van Passel ◽  
Hossein Sadeghi ◽  
Alireza Nasseri ◽  
Lotfali Agheli
Keyword(s):  
Power Plant ◽  
Human Health ◽  
Fossil Fuel ◽  
Health Impacts ◽  
Power Plant Emissions ◽  
Human Health Impacts ◽  
Fossil Fuel Power Plant ◽  
Impact Pathway ◽  
Plant Emissions ◽  
The Impact

scholarly journals Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

2011 ◽  
Vol 4 (4) ◽  
pp. 5147-5182
Author(s):  
V. A. Velazco ◽  
M. Buchwitz ◽  
H. Bovensmann ◽  
M. Reuter ◽  
O. Schneising ◽  
...  
Keyword(s):  
Power Plant ◽  
Systematic Error ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Systematic Errors ◽  
Random Errors ◽  
Power Plant Emissions ◽  
Global Coverage ◽  
Plant Emissions

Abstract. Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now as the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m.) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat can verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9 % for 50 % of all the power plants. For 90 % of all the power plants, the systematic error was less than ~12.4 %. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Finally, we recommend the CarbonSat constellation configuration that achieves daily global coverage.

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

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.

scholarly journals Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

2011 ◽  
Vol 4 (12) ◽  
pp. 2809-2822 ◽  
Author(s):  
V. A. Velazco ◽  
M. Buchwitz ◽  
H. Bovensmann ◽  
M. Reuter ◽  
O. Schneising ◽  
...  
Keyword(s):  
Power Plant ◽  
Systematic Error ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Systematic Errors ◽  
Random Errors ◽  
Power Plant Emissions ◽  
Global Coverage ◽  
Plant Emissions

Abstract. Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now being the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m. LT) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat has the potential to verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9% and a random error of less than ~6.7% for 50% of all the power plants. For 90% of all the power plants, the systematic error was less than ~12.4% and the random error was less than ~13%. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Therefore, we recommend the CarbonSat constellation configuration that achieves daily global coverage.

DIURNAL VARIATION OF PARTICULATE MATTER IN THE PROXIMITY OF ROVINARI FOSSIL-FUEL POWER PLANT

2011 ◽  
Vol 10 (1) ◽  
pp. 99-105 ◽  
Author(s):  
Catalin Nisulescu ◽  
Delia Calinoiu ◽  
Adrian Timofte ◽  
Andreea Boscornea ◽  
Camelia Talianu
Keyword(s):  
Particulate Matter ◽  
Power Plant ◽  
Diurnal Variation ◽  
Fossil Fuel ◽  
Fossil Fuel Power Plant

The Benefit of Focusing on Air Pollution Instead of Climate Change: How Discussing Power Plant Emissions in the Context of Air Pollution, Rather than Climate Change, Influences Perceived Benefits, Costs, and Political Action for Policies to Limit Emissions

2020 ◽  
pp. 107554702098044
Author(s):  
P. Sol Hart ◽  
Lauren Feldman
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Power Plant ◽  
Environmental Impacts ◽  
Political Ideology ◽  
Political Action ◽  
Perceived Benefits ◽  
Power Plant Emissions ◽  
Plant Emissions ◽  
Benefits And Costs

This experiment examines how framing power plant emissions in terms of air pollution or climate change, and in terms of health or environmental impacts, influences perceived benefits and costs of policies to reduce emissions and intentions to take political action that supports such policies. A moderated-mediation model reveals that focusing on air pollution, instead of climate change, has a positive significant indirect influence on intended political action through the serial mediators of perceived benefits and costs. Political ideology moderates the association between perceived benefits and political action. No framing effects are observed in the comparison between health and environmental impacts.

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