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Prediction of CO2 Emissions Based on Multiple Linear Regression Analysis
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 105 (2017) 4222 – 4228
The 8th International Conference on Applied Energy – ICAE2016
Prediction of CO2 emissions based on multiple linear regression analysis Yin Libaoa, Yao Tingtingb, Zhou Jieliana, Liu Guicaib,*, Liao Yanfenb, Ma Xiaoqianb a
Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China b School of Electric Power,South China University of Technology, Guangzhou 510640, China
Abstract In order to realize low-carbon development, and strive to complete the carbon emissions reduction targets of "13th Five-Year", this paper takes the national first batch of low-carbon pilot provinces-Guangdong Province as an example, to explore the precise algorithm of carbon emissions from coal-fired power plants. In this algorithm, the calculation formula of fuel characteristic coefficient β is fitted out by the proximate analysis data of fuel, and the content of CO2 in flue gas is calculated by using the fuel characteristic coefficient. Then the carbon emissions is calculated according to the total amount of smoke. Finally, the algorithm is verified by taking a thermal power plant in Guangzhou as an example, and compared with the IPCC algorithm and the method of coal consumption rate of power supply respectively, which verifies the accuracy of this algorithm. Keywords: CO2 emissions, multiple linear regression analysis, fuel characteristic coefficient
1. Introduction In October 2010, the central government issued the "12th Five-Year" Plan, which has been clearly put forward to achieve low carbon development. One of the core content is to adjust and optimize the energy structure with low carbon economy as the core. The electric power industry is the key sector of CO2 emissions reduction, and its emissions of CO2 in the energy conversion process accounts for about 50% of total national emissions. By the end of 2014, CO2 emissions from China's national unit of GDP declined 6.1% year by year, and the requirements of "12th Five-Year" planning fell by 17% of the target has been completed[1].In the "13th Five-Year" plan, National Energy Administration clearly put forward to complete international commitments to low carbon targets, which is the carbon emissions per unit of GDP dropped 40%~45% than in 2005. And for the completion of the Sino US joint statement on climate change is proposed in China 2030 to reach the peak of carbon emissions in the long-term low carbon development goals lay the foundation and achieved remarkable results in the prevention and control of
* Corresponding author. Tel.: +86-159-1434-4774. E-mail address: [email protected].
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.906
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atmospheric pollution and other environmental indicators [2]. Therefore, in order to better achieve the goal of carbon emission reduction, it is necessary to achieve a precise calculation of carbon emissions from coal-fired power plants in the power industry. In China, the main method of calculating carbon emissions in coal-fired power plants is provided by 2006 IPCC Guidelines[3]. It’s based on the fuel characteristics to obtain the default emission factor of carbon. Then the actual coal consumption of the power plant is multiplied by the emission factors of various kinds of coal, which can estimate the amount of CO2 emissions. In fact, a wide range of coal emission factors for CO2 prediction has a larger error. At the same times, the main influence factors of CO2 emission factors are the unit installed capacity, the combustion mode, and the service life of the unit and the quality of maintenance in addition to the fuel type. Different types of units due to the thermal efficiency of power generation is different, so CO2 emissions are also different. Due to the difference of service life and quality of maintenance, the CO2 emission factor of the same capacity unit can also be different. Besides, because of the different combustion mode, the carbon content of the incomplete combustion in the furnace slag is different, and the CO2 emission factor will also have some differences. So, in order to obtain more accurate CO2 emissions, the most researchers use some accurate mathematical models based on C balance to predict CO2 emissions through the coal quality analysis. As calculated by the flue gas side is also a kind of way, taking into account that the power plants generally have the detection of O2 and SO2 concentration of exhaust gas, amount of coal consumed and basic datas of coal such as proximate analysis and calorific value of coal, this paper uses the method based on the composition of flue gas emission to predict the CO2 emissions more accurately under the condition of making full use of the existing datas of the power plant and in the case of no increase in detection. Nomenclature RO2
the volume fraction of three atomic gases in flue gas, mainly sulfur dioxide and carbon dioxide
O2
the volume fraction of oxygen in flue gas
CO
the volume fraction of carbon monoxide in flue gas
£
the characteristic coefficient of fuel
Qnet,ar
the low calorific value of fuel
¢
the excess coefficient of air
V0
the theoretical amount of air required to burn 1 ton fuel
2. CO2 Emissions Prediction Model Based on Gas Emissions 2.1. The Principle of Model The amount of CO2 in the flue gas can be calculated according to the formula:
VCO2 = Vy CO2
(1)
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Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
2.2. Volume Fraction of CO2 According to the combustion equation, the relationship between the fuel characteristic coefficient [4] and the flue gas composition can be obtained as follows:
21= RO2 O2 0.605CO E RO2 CO
(2)
From formula (2), we can get the calculation formula for the volume fraction of CO2 as follows:
CO2 =
21 ª¬O2 0.605 E CO º¼ 1 E
SO2
(3) Because of carbon monoxide concentration in flue gas is very low, it can be ignored[5], that is, the formula for calculating the volume fraction of CO2 is changed to:
CO2 =
21 O2 SO2 1 E
(4) So, under the condition of known O2 and sulfur dioxide, the volume fraction of CO2 in flue gas can be obtained only by getting the fuel characteristic coefficient β. Among them, the concentration of O2, and sulfur dioxide can be directly obtained from the measurement of flue gas analysis. 2.3. Multiple Linear Regression of Fuel Characteristic Coefficien If the fuel characteristic coefficient is known, it can be calculated directly by formula (4). In fact, it is not economical to test the fuel characteristic coefficient directly. So we can get the fuel characteristic coefficient by the proximate analysis data. This paper uses multiple linear regression method to establish the relationship between the proximate analysis datas and the fuel characteristic coefficient, that is, by fitting the proximate analysis datas (including ash, volatiles, fixed carbon and high calorific value, and repectively expressed by letter A, V, FC and Q) of fuel, we can solve the required fuel characteristic coefficient. Then the volume fraction of CO2 can be obtained by formula (4). The analysis sample used in this paper mainly comes from the coal quality analysis datas of The nature, classification and utilization of coal in China[6]. In this paper, we use the stepwise command in MATLAB to achieve multiple linear regression, the command format is stepwise(X,Y). Among then, X and Y can be respectively expressed as:
§ A1 V1 ¨ A V2 X= ¨ 2 ¨ ¨ © An Vn
FC1 FC2 FCn
Q1 · ¸ Q2 ¸ , ¸ ¸ Qn ¹
§ E1 · ¨ ¸ E % ¨ 2¸ ¨ ¸ ¨ ¸ © En ¹
(5) As shown in Figure 1, it is the results of the regression analysis of the sample datas by MATLAB. As we can seen, the R-square and the R-sq Adj value of the fuel characteristic coefficient are relatively close to 1. And the value of P is very low, almost close to 0. So, we can get a satisfactory result of regression fitting, and the prediction accuracy is quite high. Therefore, the linear regression equation between the fuel characteristic coefficient and industrial analysis data can be obtained:
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Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
E
0.109471 0.00337903 Aad 0.0012095Vad 0.00034338FCad 0.00564078Qgr ,ad
(6) This paper mainly focuses on the fitting analysis of lignite. Its error range is between -2.5% and 2.5%. Only when the fuel is lignite, the equation has a higher prediction accuracy. If the use of bituminous coal, anthracite and others, we should make appropriate amendments to the equation to properly use it.
Fig. 1. Results of MATLAB regression analysis
2.4. Flue Gas Volume Vy According to the amount of coal combustion and the excess air coefficient[7], we can get the amount of flue gas. And the formula is as follows:
Vy
º M ª1.04Qnet ,ar u« 0.77 1.0161 D 1 V0 » 1000 ¬ 4.187 ¼
(7)
Among then, the α and V0 can be calculated by the following formulas:
D V0
21 21 O2 1.05 u Qnet ,ar 0.278 u1000
(8) (9)
3. Verification 3.1. Instance Verification In this paper, the actual data from a thermal power plant in Guangzhou is used to verify the method of multiple linear regression of fuel characteristic coefficient. The CO2 emissions of the plant was calculated by the model, and the results were compared and verified with the detection results. The quality analysis datas of coal and flue gas measurement results of the power plant are shown in Table 1 and 2 respectively.
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Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
Table 1. Coal quality analysis report the amount of fuel used for combustion
M=44t/h
Mar(%)
14.72
Mad(%)
2.10
Aar(%)
15.88
Var(%)
25.76
FCar(%)
43.64
Qnet,ar(MJ/kg)
21.05
Qgr,ad(MJ/kg)
22.10
Car(%)
51.64
Table 2. Flue gas measurement results Flue gas flow rate : 169.24t/h Side A
Point 1
Point 3
Point 5
Side B
Point 2
Point 4
Point 6
CO2 (%)
15.7
15.7
13.8
CO2 (%)
16.0
15.6
15.4
O2 (%)
3.10
3.15
3.11
O2 (%)
2.57
3.15
3.11
SO2 (%)
0.03210
0.0316
0.0344
SO2 (%)
0.0280
0.0342
0.0330
The second measurement results: Flue gas flow rate is 252.57t/h Side A
1
3
5
Side B
2
4
6
CO2 (%)
14.7
14.6
14.5
CO2 (%)
14.8
14.7
14.7
O2 (%)
4.40
4.45
4.41
O2 (%)
4.10
4.00
4.00
SO2 (%)
0.0326
0.0337
0.0348
SO2 (%)
0.0339
0.0347
0.0346
According to the measurement results, the average value of the six monitoring points on both side A and B to get the actual CO2 emissions is 552319.28 tons per year. Based on the linear regression of fuel characteristics, we can get the fuel characteristic coefficient β and its value is 0.129759894. Then bring it into the calculation formula (4) to calculate CO2 emissions. The calculated results are shown in Table 3. Table 3. The calculation results Side A
Point 1
Point 3
Point 5
Side B
Point 2
Point 4
Point 6
CO2 (%)
15.8119746
15.76822
15.80082313
CO2 (%)
15.8160746
15.76562
15.80222313
Vy(t/h)
173.551572
176.8398
174.2077399
Vy (t/h)
139.79365
176.8398
174.2077399
VCO2(t/h)
27.4419305
27.88448
27.52625686
VCO2(t/h)
22.1098679
27.87988
27.52869577
The second calculation results Side A
1
3
5
Side B
2
4
6
CO2 (%)
14.6607876
14.61543
14.64973615
CO2 (%)
14.9250307
15.01275
15.01284512
Vy(t/h)
265.482245
269.3065
266.2452441
Vy(t/h)
243.012163
235.6984
235.6983712
VCO2(t/h)
38.9217881
39.3603
39.00422577
VCO2(t/h)
36.26964
35.3848
35.38503143
If in accordance with side A and B of the six measurement points, the total amount of CO2 emissions for one year is 561809.59 tons. Compared with the actual results, the difference between them is 1.72%. 3.2. Compared with the Method of "2006 IPCC Guidelines" and coal consumption rate of power supply The IPCC method is mainly based on the overall activity level and default Emission-Factor. That is, by the amount of fuel involved in the combustion of fuel directly multiplied by the average emission-factor to get CO2 emissions. The coal used in the power plant is lignite, and looking for 2006 IPCC Guidelines
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to get the default factor value is 101000kg/TJ. The amount of CO2 emissions from the coal-fired power plant in the unit time period is:
E CO2 , IPCC
M u Qnet ,ar u Carbon Emission Factor 44 t h u 21050 kJ kg u 109 TJ kJ u 101000 kg TJ
93.55 t h
So, the CO2 emissions in one year is 93.55×24×365=819498.00 tons. Compared with the actual results, the difference between them is 48.37%. In fact, many power plants also use the method to calculate carbon emissions of units, which is based on the actual coal consumption rate of power supply and the carbon content as received basis, that is:
V CO2
44 u Car u M 12
44 u 51.64% u 44t / h 83.02t / h 12
So, the CO2 emissions in one year is 83.02×24×365=727255.20 tons. In accordance with the actual results, the difference between them is 31.67%. 4. Conclusions Based on the analysis of combustion process, and by the proximate analysis data to predict fuel characteristic factor, the calculation method of predicting CO 2 emission of coal-fired power plant is established, which provides guidance for the realization of the on-line monitoring. In this paper, by the calculation of CO2 emissions of Wanglong thermal power plant unit, and respectively compared with the results which is calculated by the methods provided in the 2006 IPCC Guidelines and the method of the coal consumption rate of power supply, we can draw the following conclusions: Compared with the linear regression method, both the results of IPCC and the results obtained by the coal consumption rate of power supply are quite different from the actual results. For the former, the main reason is that there is a big difference of the coal quality between China and the world. And for the letter, the main reason is that part of carbon is not burned and discharged outside of the furnace as ash, fly ash and other froms. However, this paper use Matlab software for multiple linear regression to obtain the correlation between proximate analysis data and the fuel characteristic coefficient, which is more consistent with the actual situation. Therefore, the calculation results of this paper is more accurate. References [1] National Development and Reform Commission. China's response to climate change policy and action 2015 Annual Report. Beijing: National Development and Reform Commission; 2015. [2] The eighteenth Central Committee of the Communist Party of China. The Central Committee of the Communist Party of China on the formulation of national economic and social development of the 13th Five-year plan. Beijing: The eighteenth session of the Fifth Plenary Session of Communist Party of China; 2015. [3] IPCC,OECD,IEA.Revised 2006 IPCC Guidelines for National Greenhouse Gas Inventories. PCC,Bracknell,Volumes 2,1996. [4] Jiang Xilun, Qu Weidong. Boiler equipment and operation. Beijing: China Electric Power Press; 2006. [5] Fang Jinghua, Zhao Yulan, Zeng Taofang.CO2 emission calculation and discussion for coal-fired bollers. Coal Conversion, 1999, 22(1):63-6. [6] Chen Peng. The nature, classification and utilization of coal in China. Beijing: Chemical Industry Press, 2001. [7] Zhou Taiqiang. Boiler Principle (the Second Edition). Beijing: China Electric Power Press; 2009.
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Biography Ph.D candidate, main research interests of Dr. Liu is high efficiency and low pollution combustion..
ScienceDirect Energy Procedia 105 (2017) 4222 – 4228
The 8th International Conference on Applied Energy – ICAE2016
Prediction of CO2 emissions based on multiple linear regression analysis Yin Libaoa, Yao Tingtingb, Zhou Jieliana, Liu Guicaib,*, Liao Yanfenb, Ma Xiaoqianb a
Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China b School of Electric Power,South China University of Technology, Guangzhou 510640, China
Abstract In order to realize low-carbon development, and strive to complete the carbon emissions reduction targets of "13th Five-Year", this paper takes the national first batch of low-carbon pilot provinces-Guangdong Province as an example, to explore the precise algorithm of carbon emissions from coal-fired power plants. In this algorithm, the calculation formula of fuel characteristic coefficient β is fitted out by the proximate analysis data of fuel, and the content of CO2 in flue gas is calculated by using the fuel characteristic coefficient. Then the carbon emissions is calculated according to the total amount of smoke. Finally, the algorithm is verified by taking a thermal power plant in Guangzhou as an example, and compared with the IPCC algorithm and the method of coal consumption rate of power supply respectively, which verifies the accuracy of this algorithm. Keywords: CO2 emissions, multiple linear regression analysis, fuel characteristic coefficient
1. Introduction In October 2010, the central government issued the "12th Five-Year" Plan, which has been clearly put forward to achieve low carbon development. One of the core content is to adjust and optimize the energy structure with low carbon economy as the core. The electric power industry is the key sector of CO2 emissions reduction, and its emissions of CO2 in the energy conversion process accounts for about 50% of total national emissions. By the end of 2014, CO2 emissions from China's national unit of GDP declined 6.1% year by year, and the requirements of "12th Five-Year" planning fell by 17% of the target has been completed[1].In the "13th Five-Year" plan, National Energy Administration clearly put forward to complete international commitments to low carbon targets, which is the carbon emissions per unit of GDP dropped 40%~45% than in 2005. And for the completion of the Sino US joint statement on climate change is proposed in China 2030 to reach the peak of carbon emissions in the long-term low carbon development goals lay the foundation and achieved remarkable results in the prevention and control of
* Corresponding author. Tel.: +86-159-1434-4774. E-mail address: [email protected].
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.906
4223
Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
atmospheric pollution and other environmental indicators [2]. Therefore, in order to better achieve the goal of carbon emission reduction, it is necessary to achieve a precise calculation of carbon emissions from coal-fired power plants in the power industry. In China, the main method of calculating carbon emissions in coal-fired power plants is provided by 2006 IPCC Guidelines[3]. It’s based on the fuel characteristics to obtain the default emission factor of carbon. Then the actual coal consumption of the power plant is multiplied by the emission factors of various kinds of coal, which can estimate the amount of CO2 emissions. In fact, a wide range of coal emission factors for CO2 prediction has a larger error. At the same times, the main influence factors of CO2 emission factors are the unit installed capacity, the combustion mode, and the service life of the unit and the quality of maintenance in addition to the fuel type. Different types of units due to the thermal efficiency of power generation is different, so CO2 emissions are also different. Due to the difference of service life and quality of maintenance, the CO2 emission factor of the same capacity unit can also be different. Besides, because of the different combustion mode, the carbon content of the incomplete combustion in the furnace slag is different, and the CO2 emission factor will also have some differences. So, in order to obtain more accurate CO2 emissions, the most researchers use some accurate mathematical models based on C balance to predict CO2 emissions through the coal quality analysis. As calculated by the flue gas side is also a kind of way, taking into account that the power plants generally have the detection of O2 and SO2 concentration of exhaust gas, amount of coal consumed and basic datas of coal such as proximate analysis and calorific value of coal, this paper uses the method based on the composition of flue gas emission to predict the CO2 emissions more accurately under the condition of making full use of the existing datas of the power plant and in the case of no increase in detection. Nomenclature RO2
the volume fraction of three atomic gases in flue gas, mainly sulfur dioxide and carbon dioxide
O2
the volume fraction of oxygen in flue gas
CO
the volume fraction of carbon monoxide in flue gas
£
the characteristic coefficient of fuel
Qnet,ar
the low calorific value of fuel
¢
the excess coefficient of air
V0
the theoretical amount of air required to burn 1 ton fuel
2. CO2 Emissions Prediction Model Based on Gas Emissions 2.1. The Principle of Model The amount of CO2 in the flue gas can be calculated according to the formula:
VCO2 = Vy CO2
(1)
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Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
2.2. Volume Fraction of CO2 According to the combustion equation, the relationship between the fuel characteristic coefficient [4] and the flue gas composition can be obtained as follows:
21= RO2 O2 0.605CO E RO2 CO
(2)
From formula (2), we can get the calculation formula for the volume fraction of CO2 as follows:
CO2 =
21 ª¬O2 0.605 E CO º¼ 1 E
SO2
(3) Because of carbon monoxide concentration in flue gas is very low, it can be ignored[5], that is, the formula for calculating the volume fraction of CO2 is changed to:
CO2 =
21 O2 SO2 1 E
(4) So, under the condition of known O2 and sulfur dioxide, the volume fraction of CO2 in flue gas can be obtained only by getting the fuel characteristic coefficient β. Among them, the concentration of O2, and sulfur dioxide can be directly obtained from the measurement of flue gas analysis. 2.3. Multiple Linear Regression of Fuel Characteristic Coefficien If the fuel characteristic coefficient is known, it can be calculated directly by formula (4). In fact, it is not economical to test the fuel characteristic coefficient directly. So we can get the fuel characteristic coefficient by the proximate analysis data. This paper uses multiple linear regression method to establish the relationship between the proximate analysis datas and the fuel characteristic coefficient, that is, by fitting the proximate analysis datas (including ash, volatiles, fixed carbon and high calorific value, and repectively expressed by letter A, V, FC and Q) of fuel, we can solve the required fuel characteristic coefficient. Then the volume fraction of CO2 can be obtained by formula (4). The analysis sample used in this paper mainly comes from the coal quality analysis datas of The nature, classification and utilization of coal in China[6]. In this paper, we use the stepwise command in MATLAB to achieve multiple linear regression, the command format is stepwise(X,Y). Among then, X and Y can be respectively expressed as:
§ A1 V1 ¨ A V2 X= ¨ 2 ¨ ¨ © An Vn
FC1 FC2 FCn
Q1 · ¸ Q2 ¸ , ¸ ¸ Qn ¹
§ E1 · ¨ ¸ E % ¨ 2¸ ¨ ¸ ¨ ¸ © En ¹
(5) As shown in Figure 1, it is the results of the regression analysis of the sample datas by MATLAB. As we can seen, the R-square and the R-sq Adj value of the fuel characteristic coefficient are relatively close to 1. And the value of P is very low, almost close to 0. So, we can get a satisfactory result of regression fitting, and the prediction accuracy is quite high. Therefore, the linear regression equation between the fuel characteristic coefficient and industrial analysis data can be obtained:
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Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
E
0.109471 0.00337903 Aad 0.0012095Vad 0.00034338FCad 0.00564078Qgr ,ad
(6) This paper mainly focuses on the fitting analysis of lignite. Its error range is between -2.5% and 2.5%. Only when the fuel is lignite, the equation has a higher prediction accuracy. If the use of bituminous coal, anthracite and others, we should make appropriate amendments to the equation to properly use it.
Fig. 1. Results of MATLAB regression analysis
2.4. Flue Gas Volume Vy According to the amount of coal combustion and the excess air coefficient[7], we can get the amount of flue gas. And the formula is as follows:
Vy
º M ª1.04Qnet ,ar u« 0.77 1.0161 D 1 V0 » 1000 ¬ 4.187 ¼
(7)
Among then, the α and V0 can be calculated by the following formulas:
D V0
21 21 O2 1.05 u Qnet ,ar 0.278 u1000
(8) (9)
3. Verification 3.1. Instance Verification In this paper, the actual data from a thermal power plant in Guangzhou is used to verify the method of multiple linear regression of fuel characteristic coefficient. The CO2 emissions of the plant was calculated by the model, and the results were compared and verified with the detection results. The quality analysis datas of coal and flue gas measurement results of the power plant are shown in Table 1 and 2 respectively.
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Table 1. Coal quality analysis report the amount of fuel used for combustion
M=44t/h
Mar(%)
14.72
Mad(%)
2.10
Aar(%)
15.88
Var(%)
25.76
FCar(%)
43.64
Qnet,ar(MJ/kg)
21.05
Qgr,ad(MJ/kg)
22.10
Car(%)
51.64
Table 2. Flue gas measurement results Flue gas flow rate : 169.24t/h Side A
Point 1
Point 3
Point 5
Side B
Point 2
Point 4
Point 6
CO2 (%)
15.7
15.7
13.8
CO2 (%)
16.0
15.6
15.4
O2 (%)
3.10
3.15
3.11
O2 (%)
2.57
3.15
3.11
SO2 (%)
0.03210
0.0316
0.0344
SO2 (%)
0.0280
0.0342
0.0330
The second measurement results: Flue gas flow rate is 252.57t/h Side A
1
3
5
Side B
2
4
6
CO2 (%)
14.7
14.6
14.5
CO2 (%)
14.8
14.7
14.7
O2 (%)
4.40
4.45
4.41
O2 (%)
4.10
4.00
4.00
SO2 (%)
0.0326
0.0337
0.0348
SO2 (%)
0.0339
0.0347
0.0346
According to the measurement results, the average value of the six monitoring points on both side A and B to get the actual CO2 emissions is 552319.28 tons per year. Based on the linear regression of fuel characteristics, we can get the fuel characteristic coefficient β and its value is 0.129759894. Then bring it into the calculation formula (4) to calculate CO2 emissions. The calculated results are shown in Table 3. Table 3. The calculation results Side A
Point 1
Point 3
Point 5
Side B
Point 2
Point 4
Point 6
CO2 (%)
15.8119746
15.76822
15.80082313
CO2 (%)
15.8160746
15.76562
15.80222313
Vy(t/h)
173.551572
176.8398
174.2077399
Vy (t/h)
139.79365
176.8398
174.2077399
VCO2(t/h)
27.4419305
27.88448
27.52625686
VCO2(t/h)
22.1098679
27.87988
27.52869577
The second calculation results Side A
1
3
5
Side B
2
4
6
CO2 (%)
14.6607876
14.61543
14.64973615
CO2 (%)
14.9250307
15.01275
15.01284512
Vy(t/h)
265.482245
269.3065
266.2452441
Vy(t/h)
243.012163
235.6984
235.6983712
VCO2(t/h)
38.9217881
39.3603
39.00422577
VCO2(t/h)
36.26964
35.3848
35.38503143
If in accordance with side A and B of the six measurement points, the total amount of CO2 emissions for one year is 561809.59 tons. Compared with the actual results, the difference between them is 1.72%. 3.2. Compared with the Method of "2006 IPCC Guidelines" and coal consumption rate of power supply The IPCC method is mainly based on the overall activity level and default Emission-Factor. That is, by the amount of fuel involved in the combustion of fuel directly multiplied by the average emission-factor to get CO2 emissions. The coal used in the power plant is lignite, and looking for 2006 IPCC Guidelines
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Yin Libao et al. / Energy Procedia 105 (2017) 4222 – 4228
to get the default factor value is 101000kg/TJ. The amount of CO2 emissions from the coal-fired power plant in the unit time period is:
E CO2 , IPCC
M u Qnet ,ar u Carbon Emission Factor 44 t h u 21050 kJ kg u 109 TJ kJ u 101000 kg TJ
93.55 t h
So, the CO2 emissions in one year is 93.55×24×365=819498.00 tons. Compared with the actual results, the difference between them is 48.37%. In fact, many power plants also use the method to calculate carbon emissions of units, which is based on the actual coal consumption rate of power supply and the carbon content as received basis, that is:
V CO2
44 u Car u M 12
44 u 51.64% u 44t / h 83.02t / h 12
So, the CO2 emissions in one year is 83.02×24×365=727255.20 tons. In accordance with the actual results, the difference between them is 31.67%. 4. Conclusions Based on the analysis of combustion process, and by the proximate analysis data to predict fuel characteristic factor, the calculation method of predicting CO 2 emission of coal-fired power plant is established, which provides guidance for the realization of the on-line monitoring. In this paper, by the calculation of CO2 emissions of Wanglong thermal power plant unit, and respectively compared with the results which is calculated by the methods provided in the 2006 IPCC Guidelines and the method of the coal consumption rate of power supply, we can draw the following conclusions: Compared with the linear regression method, both the results of IPCC and the results obtained by the coal consumption rate of power supply are quite different from the actual results. For the former, the main reason is that there is a big difference of the coal quality between China and the world. And for the letter, the main reason is that part of carbon is not burned and discharged outside of the furnace as ash, fly ash and other froms. However, this paper use Matlab software for multiple linear regression to obtain the correlation between proximate analysis data and the fuel characteristic coefficient, which is more consistent with the actual situation. Therefore, the calculation results of this paper is more accurate. References [1] National Development and Reform Commission. China's response to climate change policy and action 2015 Annual Report. Beijing: National Development and Reform Commission; 2015. [2] The eighteenth Central Committee of the Communist Party of China. The Central Committee of the Communist Party of China on the formulation of national economic and social development of the 13th Five-year plan. Beijing: The eighteenth session of the Fifth Plenary Session of Communist Party of China; 2015. [3] IPCC,OECD,IEA.Revised 2006 IPCC Guidelines for National Greenhouse Gas Inventories. PCC,Bracknell,Volumes 2,1996. [4] Jiang Xilun, Qu Weidong. Boiler equipment and operation. Beijing: China Electric Power Press; 2006. [5] Fang Jinghua, Zhao Yulan, Zeng Taofang.CO2 emission calculation and discussion for coal-fired bollers. Coal Conversion, 1999, 22(1):63-6. [6] Chen Peng. The nature, classification and utilization of coal in China. Beijing: Chemical Industry Press, 2001. [7] Zhou Taiqiang. Boiler Principle (the Second Edition). Beijing: China Electric Power Press; 2009.
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Biography Ph.D candidate, main research interests of Dr. Liu is high efficiency and low pollution combustion..