Coal-fired Power Plant Emissions Research Articles

The health Loss Distribution of Coal-fired Power Plants and the relationship to Air Pollution Charges

In this study, the regional health loss due to the air pollution of a coal-fired power plant is analyzed by using the atmospheric dispersion model. AERMOD was used to simulate the increment concentration map of point source emission, and the GIS geographic information system was used to displace the distribution of air pollution concentration and regional health loss in grid receptors. The data of the continuous automatic monitoring facilities and the source of pollution sources were analyzed. Moreover, the effects of emissions, seasons, population, and distance on the simulated concentration were evaluated. The various influencing factors of air pollution concentration were evaluated.the R2 value of the empty product prediction map in summer has a better correlation with the concentration from monitor station, between 0.38 and 0.83, and the sulphur dioxide correlation has the best R2 value of 0.83. AERMOD was used to combined with pollutant emissions, weather, and terrain data, to simulate the distribution of rising concentration values due to coal-fired power plant emissions within the research area, and the study used the simulated rising concentration values to calculate the health loss. [1] The study used health loss of 25,647,291,161 NTD/year and medical loss of 319,153,198 NTD/year.

Removal and emission characteristics of hazardous trace elements in total and graded particulate matters: A case study of a typical ultra-low emission coal-fired power plant

Particulate matters (PMs) and hazardous trace elements (HTEs) emitted from coal-fired power plants (CFPPs) have raised serious environmental and human issues. Herein, total PMs and graded PMs including PM<1, PM1–2.5 and PM2.5–10 at the inlet/outlet of air pollution control devices (APCDs) were collected from a representative ultra-low emission (ULE) CFPP in China. The removal efficiencies of total PMs by selective catalytic reduction (SCR), electrostatic precipitator (ESP), wet flue gas desulfurization (WFGD) and wet electrostatic precipitator (WESP) were 0.40 %, 99.9 %, 38.1 % and 85.3 %, respectively. PM2.5–10 was robustly removed by WFGD, while PM<1 and PM1–2.5 were readily removed by WESP. The removal efficiencies of As, Cd, Cr and Pb in total PMs by APCDs followed an order: ESP > WESP > WFGD > SCR. SCR significantly decreased Se concentration by 42.8 %, contrasting to the removal of As, Cd, Cr and Pb (10.8–20.8 %). As, Cd, Cr, Pb and Se concentrations in graded PMs at the outlets of ESP, WFGD and WESP decreased with particle size increasing. All As, Cd, Cr, and Pb contents in PM<1, PM1–2.5 and PM2.5–10 at WFGD outlet increased, surpassing their analogues at ESP and WESP outlets. However, the concentration of Se declined in PM<1 at WFGD outlet. The atmospheric emission factors (EFs) of As, Cd, Cr, Pb and Se in the studied ULE CFPP were respectively 7.32, 1.27, 6.05, 122.5 and 6.42 mg/t, in line with Monte Carlo simulations. This study would not only provide a basis for emission control of PMs and HTEs in CFPPs, but also promote the improvement of respective environmental policy.

A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants

Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement.

Variation of nitrate and nitrite in condensable particulate matter from coal-fired power plants under the simulated rapid condensing conditions

In this work, condensation temperature, H2O vapor, SO2, SO3 and NH3 were studied to explore the formation mechanism of nitrate ions (NO3−) and nitrite ions (NO2−) in condensable particulate matter (CPM) discharged by ultra-low emission coal-fired power plants. Some important results were obtained: (i) The concentration of NO3− and NO2− increased with the decrease of condensation temperature, and H2O vapor could also promote the formation of NO3− and NO2−. (ii) The effects of SO2 and SO3 varied at different saturated states of flue gas, which was caused by the redox reaction of SO2 and NOX or the formation of H2SO4. (iii) NH3 could promote the nucleation of NO3− and NO2−, and the promotion effect also existed in the existence of SO2 or SO3. It is worth mentioning that SO3 and SO2 might synergistically inhibit the formation of NO3− and NO2−, regardless of the presence of NH3. The research results would enrich peoples understanding of the chemical and physical characteristics of NO3− and NO2− in CPM and provide a basic reference for the control of CPM emitted from coal-fired power plants.

Analysis on Feasibility of Applying Low-Temperature Emission to Coal-fired Power Plant in Cold Weather

After the ultra-low emission(ULE) transformation, the emissions of ULE coal-fired power plants in China have been reduced greatly. However, considering the total amount of emissions, coal-fired power plants are still among the largest sources. And there may still have great potential in the removal of colored plume and some unconventional pollutants such as condensable particles matter (CPM) and sulfur trioxide (sulfuric acid mist). Though in cold days, hazes occur frequently. Besides, low-temperature condensation is conducive to the condensation and removal of vapor, CPM and sulfur trioxide in the flue gas. In this paper, a new method of low-temperature flue gas emission is proposed for coal-fired power plants in severe cold areas. The flue gas is cooled by ambient air, and discharged without reheating, and remarkable effects can be achieved in removing the pollutants. The technical feasibility and environmental compliance of applying this method to a power plant in cold weather have been analyzed. The results show that low-temperature emission of flue gas for coal-fired power plants in winter is effective in water-saving, emission reduction of pollutants and elimination of wet plume.

Research on Carbon Emissions Prediction Model of Thermal Power Plant Based on SSA-LSTM Algorithm with Boiler Feed Water Influencing Factors

China’s power industry is a major energy consumer, with the carbon dioxide (CO2) generated by coal consumption making the power industry one of the key emission sectors. Therefore, it is crucial to explore energy conservation and emissions reduction strategies suitable for China’s current situation. Taking a typical cogeneration enterprise in North China as an example, this paper aims to establish a generalized regression prediction model for carbon emissions of coal-fired power plants, which will provide a reference for China to seek strategies for carbon peaking and carbon neutralization in the future. Firstly, in terms of the selection of influencing factors, this paper uses objective index screening methods, simulation means, and the eXtreme Gradient Boosting algorithm (XG-Boost) to analyze the feature importance of various influencing factors. It is concluded that the relevant influencing factors of the boiler feed water system have a strong correlation and characteristic importance with the carbon emissions results of coal-fired power plants. Therefore, this paper proposes to introduce these factors into the regression prediction model as auxiliary variables to more scientifically reflect the carbon emissions results of coal-fired power plants. Secondly, in the aspect of regression prediction model establishment, inspired by the sparrow’s foraging behavior and anti-predation behavior, this paper selects the sparrow search algorithm (SSA) with strong optimization ability and fast convergence speed to optimize the super parameters of the long short-term memory network algorithm (LSTM). It is proposed to use the SSA-LSTM algorithm to establish the carbon emissions regression prediction model of coal-fired power plants. The advantage of the SSA-LSTM algorithm is that it can effectively simplify the super parameter selection process of the LSTM algorithm, effectively solve the global optimization problem, prevent the model from falling into overfitting and local optimization, and make the carbon emissions regression prediction model of coal-fired power plants achieve a better fitting effect. By comparing the performance indicators of the model before and after the improvement, it is found that the regression prediction effect of the SSA-LSTM coal-fired power plant carbon emissions regression prediction model, which introduces boiler feed water influencing factors, has been effectively improved. Therefore, the model proposed in this paper can be used to conduct a comprehensive impact factor analysis and regression prediction analysis on the carbon emissions intensity of China’s coal-fired power plants, formulate targeted carbon emissions reduction countermeasures, and provide a theoretical basis for energy conservation and emissions reduction of China’s coal-fired power plants.

Study on the characteristics of sulfate ion in condensable particulate matter from ultra-low emission coal-fired power plants

The sulfate ion (SO42−) in condensable particulate matters (CPM) discharged by ultra-low emission coal-fired power plants has caused a series of environmental problems. In this work, condensation temperature, H2O, NOx and NH3 were investigated to elucidate the characteristics of SO42− under fast condensation. The results demonstrated that SO3 was a crucial component for the formation of SO42− in CPM, and the SO42− concentration increased with the increase of proportion of deposited H2SO4. The SO42− produced by heterogeneous reaction of SO2 could be enhanced in low temperature, and SO2 and SO3 could jointly promote the formation of SO42−. Furthermore, H2O vapor could also strengthen the formation of SO42−, which was caused by the Stefan flow. NH3 acted as the core, and the formation of SO42− was promoted. NOx could provide an acidic environment, which would interfere with the state of SO42− and even suppress the aqueous-phase oxidation of SO2. Finally, the formation of fine particles would aggravate the capture difficulty of CPM, which might help to explain the decrease of SO42− concentration in real flue gas. The results could provide basic guidance for improving the control of SO42− in CPM emitted from coal-fired power plants.

High contribution of vehicular exhaust and coal combustion to PM2.5-bound Pb pollution in an industrial city in North China: An insight from isotope

Although industrial emissions are the major pollution source of PM2.5-bound Pb, which affects air quality in cities, their influence has gradually decreased with the enhancement of industrial control in recent years. Thus, at present, the source of PM2.5-bound Pb is unclear, especially in an industrial city in North China. In this study, by incorporating the stable Pb isotopic compositions into a Bayesian isotope mixing model (MixSIAR), source apportionment of PM2.5-bound Pb was conducted during autumn and winter in 2017, and summer in 2018 in Changzhi. The daily average concentrations of PM2.5 and PM2.5-bound Pb during the study period were lower than the second-grade limit value of China (75 μg m−3) and the Chinese National Ambient Air Quality Standard (1 μg m−3), respectively. The pollution sources of PM2.5-bound Pb were fully distinguishable using Pb isotopes. In Changzhi, the 206Pb/207Pb ratio in soil (average: 1.0891) and exhaust dust from gasoline (average: 1.0832) and diesel (average: 1.0814) showed low levels of Pb isotope signature in Changzhi, whereas the rest of the source exhibited Pb isotope signature levels. The source apportionment results revealed that in an industrial city with many steel and coking plants, the primary source of PM2.5-bound Pb were motor vehicle emission (29.7%) and coal combustion (26.9%), rather than industrial emission (26.5%). Moreover, seasonal variations in PM2.5 of Pb sub-sources were observed: soil dust (21.7% and 18.4%) and diesel motor vehicles emission (16.4% and 15.6%) in summer and autumn; domestic coal combustion (17.1%) and coal-fired power plant emission (15.5%) in winter. The carcinogenic risk from motor vehicle emission in autumn was higher than that from industrial and coal sources in winter, and higher than that from motor vehicle sources in summer. This finding is a critical evidence of an anomalistic enrichment of PM2.5-bound Pb in an industrial city. These results obtained through Pb isotopic composition and MixSIAR model also improved the accuracy of source apportionment of atmospheric PM2.5-bound Pb, representing a high scientific significance and reference value for Pb pollution control in North China.

Speciation, bioaccessibility and human health risk assessment of chromium in solid wastes from an ultra-low emission coal-fired power plant, China

Chromium (Cr) in solid wastes from ultra-low emission (ULE) coal-fired power plants (CFPPs) could engender adverse effects on environment and human health. Hence, solid waste samples containing bottom ash, fly ash, gypsum and sludge were collected from a typical ULE CFPP in China to study the distribution, speciation, bioaccessibility and human health risk of Cr. The results showed that Cr was depleted in gypsum, whereas significantly enriched in bottom ash, fly ash and sludge comparing with feed coal. The ratios of Cr(VI) to total Cr in solid wastes were relatively low, but the increase of flow fractions in Cr chemical binding forms implied the deterioration of environmental stability. Based on the in vitro simulated digestion methods of solubility bioavailability research consortium (SBRC) and physiologically based extraction test (PBET), the bioaccessibility of Cr in the gastric and intestinal phases reached the highest values in either gypsum or sludge. After incorporating bioaccessibility in human health risk assessment, the carcinogenic risk (CR) within acceptable limits of Cr in solid wastes to adults and children was concluded, with the non-carcinogenic hazard quotient (HQ) was all within the safety threshold. The Monte Carlo model was applied to evaluate the uncertainty analysis of human health risk assessment at 5% and 95% confidence interval, and the fitting results were consistent with the calculation results of the carcinogenic and non-carcinogenic risk for adults and children. This study is expected to provide insights for the integration of bioaccessibility into the health risk assessment of Cr in solid wastes from ULE CFPPs, thus is conducive to the disposal of solid wastes and human health protection.

Synergetic removal characteristics of mercury for ultra-low emission coal-fired power plant

The synergetic mercury removal by flue gas treatment process in coal-fired power plants is widely accepted, but there is a lack of a corresponding model to describe mercury transformations. In this work, a model for mercury transformations is established in a flue gas treatment process model using Aspen Plus. The concentrations, mass flow rates, and normalized emission factors of mercury, NOX, particulate matters (PM), and SO2 are investigated in a 660 MW coal-fired power plant. The mercury oxidation efficiency of the selective catalytic reduction (SCR) unit is 56.9 %. The mercury removal efficiency of the electrostatic precipitator (ESP) and wet flue gas desulphurization (WFGD) units are 53.5 % and 34.4 %, respectively. The ultra-low emission system achieves 63.3 % removal of mercury. Approximately 53.5 % of mercury is retained in the fly ash. The emission factor of mercury is 0.0131 mg/kWh and increases to 0.0265 mg/kWh when the load decreases from 100 % to 50 %. The emission factors of PM, SO2 and NOX are 7.1, 97.1, and 142.9 mg/kWh at full load, and 10.9, 97.4, 150.0 mg/kWh at 50 % load, respectively. The proposed flue gas treatment process model is considered a feasible approach for quantitative evaluation of multi-pollutants emissions at plant level.

A typical 300 MW ultralow emission coal-fired power plant: source, distribution, emission, and control of polycyclic aromatic hydrocarbons

• The LLT-ESP presents the best effect on the control of PAHs. • The APCDs have selectivity for the removal of PAHs with different phases and ring numbers. • Blending coal species guides the source reduction technology of PAHs. • The concentration of PAHs is effectively reduced by adsorbent injection coupled with an LLT-ESP. Polycyclic aromatic hydrocarbons (PAHs) are one of the dominant contributors to toxic organic pollutants emitted from coal combustion. In this study, the PAHs and condensable particulate matter (CPM) were sampled from a 300 MW ultralow emission coal-fired power plant. The effect of air pollution control devices (APCDs) on PAHs migration regularity and the influence of coal species changes on the emission of PAHs were studied. Note that the APCDs have definite selectivity for the removal of PAHs with different phases and ring numbers. The low-low temperature electrostatic precipitator (LLT-ESP) presented the best removal effect on PAHs (PAHs in flue gas, 29.92%; PAHs in CPM, 94.76%). The concentrations of PAHs were reduced at the outlet of the furnace (PAHs in flue gas, decreased from 3.318 to 2.850 μg/Nm 3 ) and at the stack (PAHs in flue gas, decreased from 4.737 to 3.008 μg/Nm 3 ; PAHs in CPM, decreased from 0.554 to 0.429 μg/Nm 3 ) by replacing bituminous coal with blended coal (lower volatiles) as fuel. The adsorbent injection coupled with LLT-ESP had a preferable removal effect on the PAHs (decreased from 4.949 to 1.451 μg/Nm 3 ), which was nearly 40% higher than the efficiency without the spray adsorbent.

Soft sensing of SO2 emission for ultra-low emission coal-fired power plant with dynamic model and segmentation model

Static models, segmentation models and dynamic models for soft sensing of SO2 emission were developed based on the Ordinary Least Squares, Support Vector Regression, eXtreme Gradient Boosting, Random Forest and Neural Network algorithms with real operational data from a 1000 MW coal-fired power plant with ultra-low emission systems. The prediction results of test set show that static models are not suitable for modeling the desulfurization systems with complex condition and time-delay process. Thus, segmentation models and dynamic models were used to optimize the prediction accuracy. All the prediction performance of the nonlinear models was improved significantly with dynamic model, while the prediction performance of the linear model was improved with segmentation model. The dynamic Neural Network model with one hidden layer achieves the most accurate regression result (R2 = 70.4 %, RMSE = 1.95 mg/m3, MAE = 1.53 mg/m3). The results show the superiority of the dynamic Neural Network model over the other and the dynamic Support Vector Regression model perform second-best.

Study on the control of condensable particulate matter by spraying activated carbon combined with electrostatic precipitator

Condensable particulate matter (CPM) is a primary particulate matter that exists in the form of gas or vapor phase before discharge, but after discharge, it rapidly turns into a particulate substance due to cooling and dilution. The composition of CPM is very complex, and it poses a threat to the environment and human health. However, there is a lack of research on the control of CPM. Therefore, this study investigated the effects of activated carbon (AC) injection coupled with low-low temperature electrostatic precipitator on the adsorption of CPM in the flue gas of a 300 MW ultra-low emission coal-fired power plant. The sampling system was conducted according to ISO 23210–2009 and U.S. EPA method 202. Results shows that the adsorption removal efficiency of CPM by ACs ranged from 14% to 33%, and the adsorption removal effect of AC on inorganic components in CPM was better than that of organic components. AC had significant adsorption effects on Cl− and NH4+ but had weak adsorption effect on NO3− in CPM. AC with larger specific surface area, more abundant pore structure, and richer surface chemical functional groups has better adsorption effect on metal elements such as Na, Ca, Mg, K, and Al in CPM is better than that of Fe. As for the organic components in CPM, the adsorption effect of AC for esters was considerable, but its adsorption on organosilicon was limited.

Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion.

Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO2, PM2.5, NO x , and SO2, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.

Assessing the impact of coal-fired thermal power plant emissions on surrounding vegetation health using geoinformatics: a case study

Assessing the impact of coal-fired thermal power plant emissions on surrounding vegetation health using geoinformatics: a case study

Technology development and cost analysis of multiple pollutant abatement for ultra-low emission coal-fired power plants in China

The implementation of ultra-low emission (ULE) limits (SO2: 35 mg/m3, NOx: 50 mg/m3, PM: 10 mg/m3) promoted the development of flue gas treatment technologies in China. Pollutant control technology development for Chinese coal-fired power plants was summarized and an analysis of the applicability and cost of pollutant control technologies was conducted. Detailed data were collected from 30 ultra-low emission coal-fired units across China. Based on a cost analysis model, the average unit power generation incremental costs were 0.0144 and 0.0095 CNY/(kW·hr) for SO2 and NOx control technologies, respectively. The unit power generation incremental cost of twin spray tower technology was 7.2% higher than that of dual-loop spray tower technology. The effect of key parameters on operating cost was analyzed. The unit power generation incremental cost increased because of increments in the electricity price for SO2 control technology and the price of the reductant in NOx control technology. With high sulfur content or NOx concentration, the unit power generation incremental cost caused by pollutant control increased, whereas the unit pollutant abatement cost decreased. However, the annual operating hours or load increased, thereby leading to a decline in unit power generation incremental cost and unit pollutant abatement cost.

Assessing the dry impinger method for condensable particulate matter from ultra-low emission coal-fired power plant measurement

The dry impinger method is commonly used for the determination of condensable particulate matter (CPM) emissions. The coil and chamber condenser is used to build different dry impinger methods for CPM sampling. The comparative analysis of coil and chamber condenser is performed in a laboratory experiment to evaluate the deviation caused by SO2. Results showed that the positive deviation caused by SO2 in the chamber condenser is lower than that in the coil condenser under the same sampling conditions, especially under high humidity flue gas. The CPM emission characteristics from Hanchuan coal-fired power plant (CFPP) determined by both dry impinger methods are also investigated as well. The CPM and its most water-soluble ions (e.g., F−, Cl−, NO3−, SO42−, Na+, Ca2+ and NH4+) measured by method #2 (chamber condenser) are higher than that of method #1 (coil condenser). In addition, the esters in the CPM also increased with the CPM concentrations. Based on above evidences, it can be inferred that the dry impinger method with chamber condenser, will be recommended as the appropriate method for measuring CPM emitted from stationary sources, especially under the high humidity flue gas conditions.

Effect of WESP on Emission Characteristics of Condensable Particulate Matter from Ultra-low Emission Coal-fired Power Plants

In order to study the effect of wet electrostatic precipitators(WESP) on emission characteristics of condensable particulate matter (CPM) from ultra-low emission coal-fired power plants that are under different capacity conditions, a set of CPM sampling devices was built based on US EPA Method 202, and an ultra-low emission coal-fired power plant was detected. This study evaluated the emission level of the CPM from the flue gas of coal-fired power plants, the effects of different unit capacity conditions on the CPM emission concentrations, and the removal efficiency of WESP for different components of the CPM. The results suggested that the emission concentrations of the CPM from ultra-low emission power plants were 27.27 mg·m-3 and 28.71 mg·m-3under the conditions of 75% and 100% capacity, respectively. The removal efficiencies of WESP for the CPM were 35.59% and 27.59%, respectively. SO42- was the main component of water-soluble ions of the CPM. The proportion of SO42- in inorganic components of the CPM reached more than 65% under different capacity conditions. In addition, the removal efficiency of WESP for Cl-, K+, Ca2+, Mg2+, Na+, and other inorganic ions reached 30%-50%, but the mass concentrations of SO42- and NO3- increased.

Distribution and emission characteristics of filterable and condensable particulate matter in the flue gas emitted from an ultra-low emission coal-fired power plant

Total particulate matter emitted from coal-fired power plants can be classified into filterable particulate matter (FPM) and condensable particulate matter (CPM). In this study, the FPM and CPM in the flue gas were sampled from a 300 MW ultra-low emission coal-fired power plant by the simultaneous sampling system, which was conducted according to ISO 23210-2009 and U.S. EPA method 202. The results show that the emission concentration of CPM rose from 5.15 mg/Nm3 to 7.19 mg/Nm3 at the stack when coal mixed with sludge. Almost all of the air pollutant control devices have a positive effect on the removal of CPM and FPM in the flue gas, except the influence of the selective catalytic reduction (SCR) denitration device on CPM. And the SCR denitration equipment increased the concentration of inorganic components of CPM in flue gas. The low-low temperature electrostatic precipitator had the most obvious removal effect on CPM and FPM, and the removal efficiency for FPM and CPM was more than 90% and 75% respectively. The organic fraction in CPM was mainly composed of hydrocarbons, esters, organosilicon, and other organics. In particular, the proportion of hydrocarbons and organosilicon was relatively high. In the case of co-combustion of sludge and coal, the concentration of CPM and FPM in flue gas increased as a whole, but the distribution trend of CPM and FPM was consistent with that of the non-combustion sludge and the distribution of organic components in CPM was almost the same.

Life cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions

Life cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions