Analysis of Carbon Emissions and Emission Reduction from Coal-Fired Power Plants Based on Dual Carbon Targets

Hou, Haitao; Xie, Bo; Cheng, Yingying

doi:10.3390/su15097369

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…Let the proportion of SO 2 in coal-fired flue gas be denoted as W SO 2 , and the volume of coal-fired flue gas be denoted as G (m 3 /h) [31]. The amount of crystalline water in gypsum can be calculated using Equation (8), and the amount of adhering water in gypsum can be calculated using Equations ( 9) and (10).…”

Section: Simulation Of Primary Water Usagesmentioning

confidence: 99%

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

et al. 2023

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Climate change and water scarcity are significant global issues both currently and in the future. Investigating the changes in industrial water usage under this backdrop holds crucial guidance for enabling adaptive changes within industries. It offers methods and case support for assessing the impact of climate on the water usage processes of industrial enterprises as well. This study employed a physical model to examine the impact of climate factors on the primary water consumption processes of a selected case of a coal-fired power plant. Additionally, by utilizing the ScenarioMIP experimental outcomes from CMIP6 models, the future water consumption processes were predicted under four different SSPs-RCPs scenarios. A relevant LSTM neural network was constructed based on the primary water consumption calculation to establish connections between the power generation process, meteorological process, water consumption process, and water intake process. Findings from the study reveal that the annual average primary water consumption showed a decreasing trend in different SSPs-RCPs scenarios. Specifically, there were reductions of 1600, 5300, 9000, and 11,400 t/year in each respective scenario from SSP1–2.6 to SSP5–8.5. Conversely, the water intake exhibited a gradual increase, with increments of 2000, 5600, 9200, and 10,000 t/year, respectively. Moreover, the impact of climate change on evaporation showed an annual decrease of less than 0.056%, while the increase in water intake was below 0.044%. Under the SSPs-RCPs scenarios, both water intake and consumption exhibited a gradually increasing trend with fluctuations ranging from 1–2.6 to 5–8.5. However, the annual trends remained relatively stable. It is crucial to acknowledge that climate change has amplified the uncertainty surrounding water intake and consumption. Industrial enterprises should proactively ensure the stability of their production processes in response to climate change.

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Section: Simulation Of Primary Water Usagesmentioning

confidence: 99%

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

et al. 2023

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“…In recent years, many scholars studied the impact between the power industry and carbon emissions. For example, Hou [6] constructed a carbon emission model and a carbon emission data accounting model for coal-fired power plants, which is of great significance for quantifying carbon emissions from coal-fired power plants and achieving the dual carbon goals. Karmellos [7] studied the drivers of CO 2 emissions from the power sector in EU countries.…”

Section: Introductionmentioning

confidence: 99%

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

Dong

Zheng

2023

Sustainability

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The existing research on critical peak price (CPP) decision-making ignores the difference in risk appetite between industries within the consumer population, resulting in a serious lag in the enthusiasm of some users to respond to CPP, and unsatisfactory improvement of power systems and carbon emission reduction on the supply and demand side. Firstly, the problem of consumer risk appetite was comprehensively analyzed, and the industrial consumer population was secondarily stratified according to the influencing factors and the enthusiasm of responding to CPP, namely: stubborn, active and conformist, and quantitatively verified by cluster analysis. Secondly, by combing the relevant paths of CPP decision-making, the critical-peak window determination model and CPP multi-objective optimization model were constructed, and the calculation of relevant indicators was introduced. Finally, taking 10 industrial enterprises in a city in Sichuan Province as an example, the clustering method was used to verify the stratification results, and the index analysis method was used to measure the load and carbon emission improvement of two typical enterprises after CPP optimization. The results showed that the stubborn users insist on using electricity, the improvement effect of load and carbon emission reduction was poor, and additional production costs will be caused. The conformist and active users had high sensitivity to electricity price fluctuations, good load and carbon emission reduction improvement effects, and significantly reduced electricity costs.

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“…There are many factors affecting coal combustion such as coal type, combustion temperature, air intake, coal grinding, and so on [7]. A proper controlling of operation parameters (amount of feeding coal, nozzle angle, boiler feed water, intake air) of the boiler helps to burn coal completely.…”

Section: Introductionmentioning

confidence: 99%

Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network

So,

Kibet,

Woo

et al. 2023

Machines

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Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.

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Analysis of Carbon Emissions and Emission Reduction from Coal-Fired Power Plants Based on Dual Carbon Targets

Cited by 7 publications

References 41 publications

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network

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