Article pubs.acs.org/EF
Evaluation of the Integrated Characteristics on Combustion and Drying Using Element Analysis Qiulin Wang,*,† Jialing Zhu,† Tailu Li,‡ and Guowei Zhang† †
Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin 300072, China ‡ School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China ABSTRACT: Cleaning combustion and drying of coal can be contributed to improve fuel applied efficiency and reduce greenhouse gas emissions and particulates to the atmosphere. Parameters of drying, theoretical/actual air requirement and volume of flue gas of coal combustion, ignition characteristic coefficient, and burning rate were introduced in the paper. Relationships between moisture and calorific value, capacity of theoretical power generation, generator coal consumption, theoretical air volume, and flue gas emission were described in curves. The calorific value is mainly affected on elements of C and H by sensitivity analysis of drying experimental data. Equations 41 and 42 were recommended to evaluate the integrated characteristics on combustion and drying. Figure 1 shows physical property of drying coal matching with combustion and drying characteristics reflecting demands of cleaning combustion. The coupling applied technology of combustion and drying-based element analysis will benefit cleaning coal, reducing greenhouse gas emissions.
1. INTRODUCTION The abundance and versatility of coals are applied as an important source of energy. Drying upgrading and gasification of coal are applied in integrated coal gasification combined cycle (IGCC) to improve applied efficiency. Coal is mainly composed of carbon, hydrogen, oxygen, nitrogen, sulfur, ash, and moisture. The relationship between the calorific value of various regions of coal and composition-existing significant statistical laws have also been reported in the literature.1−4 Three set of inputs: (a) volatile matter, ash, and moisture, (b) C, H, N, O, S, and ash, and (c) C, Hexclusive of moisture, N, Oexclusive of moisture, S, moisture, and ash were used for the prediction of the gross calorific value (GCV) by regression and artificial neural networks (ANNs). The multivariable regression studies have shown that the model c is the most suitable estimator of GCV. Running of the best arranged ANN structures for the models a−c and assessment of errors have shown that the ANNs are not better or much different from regression, as a common and understood technique, in the prediction of uncomplicated relationships between proximate and ultimate analyses and GCV.5 GCV, which is indicated by the useful energy content, is an important parameter defining the energy content and, thereby, efficiency of coals. There exist some methods of estimating GCV of a coal sample based on its proximate or ultimate analyses. The content of various elements are measured in the ultimate analysis. An important specific correlation of coal was proposed that correlated the calorific value with the volatiles and fixed carbon as given.6−8 Channiwala and Parikh9 have reviewed a large number literature of type correlations for estimating GCV and also proposed a unified correlation from the elemental analysis of fuels as given by
where CC, CH, CO, CN, CS, and CA represent the mass percentages of carbon, hydrogen, oxygen, nitrogen, sulfur, and ash contents of coal dry basis, respectively. Equation 1 could predict the GCV of various types of fuels with an average absolute error of 1.45%. Parikh et al. developed a proximate analysis based correlation for predicting the GCV of an entire spectrum of solid carbonaceous materials because a major difficulty with the correlation in eq 1 is that it requires costly equipment.10 ANNs are complex nonlinear dynamical system simulating the intelligent behavior exhibited.11 It is based on the concept that a highly interconnected system of simple processing units can learn complex nonlinear interrelationships existing between input and output.12 Using the feature of ANNs, the carbon content of fly ash and calorific value can be predicted on new operating conditions of the boiler. According to the network connections of neurons, neural networks can be divided into two categories of before and feedback networks. It has been proven that any linear mapping can be achieved using a threeforward network.13 A neural network can be used in the models of the carbon content of fly ash. Boiler operational parameters, boiler load, flue gas temperature, and coal characteristics are selected as neural network input parameters. The carbon content of fly ash is applied as the output node of the process. Measurements of flue gas composition and ash content in raw coal and interrelated elemental composition of coal have been studied. For example, the information on elements based on flue gas compositional analysis by a soft-sensing technique is applied in monitoring combustion characteristics of a utility boiler. Coal element data form the foundation of monitoring the ultimate composition and calorific value of firing coal. The Received: March 24, 2014 Revised: May 23, 2014 Published: June 12, 2014
Q = 0.3491CC + 1.1783C H + 0.1055CS − 0.1034CO − 0.0151C N − 0.0211CA
(MJ/kg)
© 2014 American Chemical Society
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calorific value after drying (eq 41) and an equation of moisture and theoretical power generation capacity (eq 42) were recommended to evaluate the integrated characteristics of combustion and drying. Figure 1 shows technology logic
linear relationship between GCV and a few constituents based on statistics data was indicated. Element analysis can be matched with monitoring the coal performance and alarm system of combustion by collecting the index of economic operation, ultimate composition, and calorific value at the same time. A variety of inspection methods are applied to measure moisture or calorific value. Moisture of coal into a mill can be observed and finished the diagnosis fault of the mill according to operating parameters.14 In the combustion system of common pulverized coal, biomass fuel, and waste fuel, the instability quality of fuel is more and more enhanced.15 Continuous measuring of the data of moisture is for the control signal of combustion.16 GCV of a coal particle is increased and benefited combustion upon giving off the volatile and water.17,18 The soft measurement algorithms of the calorific value have been applied in automatic control of the boiler. The assessment and improvement of energy efficiency can be achieved through thermal economic diagnosis of a coal-fired power plant.19 The combustion characteristics are reflected by contrasting the composite indicator of various kinds of thermodynamic parameters of the boiler, such as pressure and temperature of superheated and reheated steam, pressure and temperature of the furnace, and temperature and flow of the flue gas. However, coal drying can be widely applied in power generation, pyrolysis, liquefaction, and gasification processes. Dried coals have also been proven to improve plant efficiency and reduce greenhouse gas emissions. Various drying methods are mainly classified into evaporative and non-evaporative drying. High moisture content in coals can lead to high transportation costs and hazards of spontaneous self-ignition in transportation and storage. Moisture content, mechanism of heat and mass transfer, and spontaneous self-ignition of coal stockpiles were studied as important drying characteristics.20,21 Because of the high cost of a pilot-scale experiment, simulation technologies for coal drying are applied in design, analysis, optimization, and test of drying but a reliable drying theory or design and amplification are still lacking on the basis of mathematical models.22−24 Combustion characteristics influenced a variety of factors: design parameters, coal quality, steam parameters, operational conditions, etc. In supercritical and ultra-supercritical combustion, circulating fluidized-bed combustion, and IGCC, drying of coal is a favorite. Achievements of combustion characteristics were proclaimed, such as the correlation of moisture and calorific value and reduction of greenhouse gas emissions. Information of elements changed with combustion is not emphasized as sufficiently focused on the theory of thermodynamics and combustion performance. However, the correlations of moisture and drying characteristics were also proclaimed, such as the drying rate, porous structure of dried coal, reabsorption characteristics, isotherm adsorption, heatand mass-transfer characteristics, etc. High-grade coals are obviously chosen to optimize cleaning and high-efficiency combustion, which can be achieved by drying-upgrading technology. The integrated characteristics of combustion and drying-based element analysis not only evaluate the drying characteristics but also infer the evaluation of combustion. These functions can be fulfilled on the basis of element analysis by ANNs and soft measurement technology. Information of the calorific value matching the fine combustion is reflected to the drying process and adjusted. An equation of moisture and
Figure 1. Flowsheet of integrated characteristics on combustion and coal drying.
between the characteristics of coal in the drying process matching combustion and the drying characteristics reflecting the demands of cleaning combustion. This improved the coupling application of the combustion characteristics and the drying-based element analysis. These mathematical models and experiments are still studied to benefit the application of cleaning coal, reduction of greenhouse gas emissions and particulars to the atmosphere, and optimization of the technology of coal drying.
2. APPLICATION OF THE COAL ELEMENT 2.1. Analysis of Coal Drying. Saving energy and reducing greenhouse gas emissions have become two major topics in the world today. Application technologies of cleaning coal have been become an inevitable choice. The rapidly developing industry places more and more depends upon drying, although it is becoming an operating unit with the most consuming energy.25,26 Pre-drying fuel is a potential and effective technology to enhance energy utilization efficiency27 and decrease the carbon footprint and other pollution.28 The drying medium (nitrogen, flue gas, hot air, superheated steam, etc.) of the temperature, the sample size, and the gas flow rate effect on drying characteristics were studied.29 The kinetics of drying, pyrolysis, and combustion of fuel was investigated, and the impacts of parameters and exergy were analyzed.30,31 The combustion behavior of dryingupgrading lignite was also investigated experimentally and numerically.32,33 The impact of the moisture content, multiple air staging, pulverized coal fineness, and burnout air position on NOx emissions under deep, middle, and shallow air-staged combustion conditions was investigated.34 In coal drying, moisture is removed through heat transfer of drying medium and raw coal at the same time that the temperature of raw coal is raised. Moisture decreased after drying treatment, and energy consumption is reduced in combustion. Under the action of drying medium, the coal temperature and physical sensible heat are increased; thus, the energy consumption of preheating coal in the combustion process is reduced. The calorific value of dried coal is obviously improved, and combustion is optimized. In the preheating or drying process, the temperature, humidity, and compounds containing volatile need to be detected. The combustion conditions and associated systems should not be ignored. The coal property of the drying process is continuously changed. When the drying medium contacts raw coal, the fluid mechanics and heat and mass transfer become more complex and unpredictable. Therefore, the mathematical ability of describing wet material is still weak. Some results researching 4422
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the coal drying project can be quickly converted and applied to reduce energy consumption and carbon emissions. 2.2. Analysis of the Calorific Value. According to data of industrial analysis and element analysis, the empirical formulas are proposed to apply in the calculation of the calorific value. A real-time model of monitoring coal characteristics based on element analysis can be realized by the monitoring function by measuring the operational parameters of the boiler, turbine, mill, and air preheater. Moisture information can be extracted from the running mill. A dry and ash-free basis is derived from flue gas. The mill is the main equipment for grinding powder systems. The heat in the mill includes physical heat (qgz) of the drying medium, physical heat (qlf) of cold air leaking into the mill, grinding heat (qnm), and physical heat (qr) of raw coal. The heat out of the mill includes the heat consumption to evaporate moisture (qz), the heat value of heating fuel (qjr), the heat of the drying medium carried out of the mill (q2), and the heat loss of the system (q5). According to the principle of conservation of energy, the energy balance correlation is proposed by eq 2.
qgz + qif + qnm + qr = qz + qjr + q2 + q5
21 = RO2 + O2 + 0.605CO + β(RO2 + CO2 )
where RO2 = CO2 + SO2, β is the fuel characteristic coefficient, and CO2, SO2, CO, and O2 denote the percentages of the volume content of CO2, SO2, CO, and O2 in flue gas, respectively.
β = 2.35
Mar − M mf 100ar − M mf
(2)
G3 = G1M1 − G2M 2 = G1
(3)
R=
t2
(4)
where R90 and t2 denote the percentages of pulverized coal fineness and temperature of the powder−wind mixture, respectively
C1
mf m W t1 + Klf f C lktA + K nm t1 mm mm mm ⎛ 100 − Mar ⎞ M + ⎜4.19 ar + Crd⎟tA ⎝ ⎠ 100 100 Mar − 0.048Mar = 100 − 0.048Mar
R 90 t 2 0.46 R 90
(2491 + 1.884t 2 − 4.19tA )
t 2 0.46
G3 G M − M2 = 1 1 dt d t 100 − M 2
V 0 = 0.0889Car + 0.265Har − 0.0333(Oar − Sar)
Q5
100 − Mar mf + C2t 2 + 100 mm mm R 90 ⎛ ⎞ 0.048Mar 0.46 ⎜ ⎟ t2 (t − t1) + 4.19 C rd ⎜⎜ R 90 ⎟ 2 100 − 0.048Mar 0.46 ⎟ ⎝ ⎠ t2
+ (1 + Klf )
M1 − M 2 100 − M1
(8)
(9)
where G1, G2, G3, M1, M2, and R denote gross of raw coal (kg/ h), coal gross after drying (kg/h), gross of evaporated water (kg/h), raw coal moisture (5−60%), coal moisture after drying (8−10%), and drying rate, respectively. 3.2. Theory Air Requirement Per Kilogram of Coal Combustion. Theory air requirement is calculated on the basis of a received basis element analysis. According to the normative appendix (Appendix B, China DL/T5145-2002: The Calculation Method of Theory Air Volume and Exhaust Gas Volume. Especially in calculation, “%” is adopted using the quantity percentages of the element). The volume of the standard condition is indicated by
R 90 0.46
(7)
3. ANALYSIS OF CORRELATION MODELS 3.1. Parameters of the Drying Process. In the drying process, the weight of raw coal is changed with the moisture evaporation as proposed by eq 8. The drying rate is proposed on the basis of element analysis by eq 9
where Mar and Mmf denote the percentages of received basic moisture and moisture of pulverized coal, respectively. Mmf is proposed by eq 4, and the equation of coal moisture is given by eq 535
M mf = 0.048Mar
Har − 0.126Oar + 0.038Nar Car + 0.375Sar
The analysis technology of γ-ray neutron inductive production is used to perform the online analysis for coal quality and element information on flue gas.37 Online coal quality detecting and analyzing devices have been used in the power plant because those devices are convenient for installation and it is in stable operation with accurate measurement. It plays a remarkable role in optimization of fuel management, such as directing the coal blending, regulating coal combustion, analyzing coal quality, etc. A connection of drying and combustion is possible to base on element analysis.
The relationship between the heat consumption to evaporate moisture (qz) and evaporate water (ΔM) of per kilogram of raw coal in the mill is proposed by eq 3 ΔM =
(6)
(10)
0
where V on the standard condition is the theory-required air requirement per kilogram of received coal (m3/kg) and Car, Har, Oar, and Sar denote the percentages of carbon, hydrogen, oxygen, and sulfur components of coal based on a received basis element analysis. The volume is also indicated by empirical eq 11, empirical eq 12, or empirical eq 13.38
(5)
where mm, mf, t1, t2, tA, W, C1, C2, C1k, Crd, Klf, R90, and Q5 denote parameters of the output of the mill, inlet airflow of the mill, import air temperature, export air temperature, environmental temperature, mill power, specific heat of mass in imports of drying medium, specific heat of mass in export of drying medium, specific heat of mass in cooling air, specific heat of mass on dry and ash-free basis, factor of air leakage, fineness of pulverized coal, and heat loss of the coal pulverizing system, respectively. Therefore, Mar can be performed as received moisture iterative solution. 2.3. Information of the Coal Element Based on Measured Parameters of Flue Gas. Components of flue gas are determined by various element contents in coal. A simple linear relationship existed between flue gas composition and element composition. According to chemical analysis of combustion, a constraint equation 36 is recommended, regardless of the loss of gas and solid incomplete combustion by
V 0 = 0.239
Q ar.net 1000
+ 0.6
(Vadf < 15% anthracite, lean coal) V 0 = 0.251
Q ar.net 1000
+ 0.278
(Vadf > 15% bituminous coal) V 0 = 0.239
Q ar.net 1000
(12)
+ 0.45
(Q ar.net < 12540 kJ/kg of inferior coal) 4423
(11)
(13)
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moisture on a received basis. Exergy is connected with standard exergy of an element (kcal/kmol).41 Activation energy is one of the important index parameters reflecting combustion activity of coal particles. According to Arrhenius’s law, calculation of activation energy of coal particles is by eq 22. The activation energy and burnout ratio are usually used to distinguish combustion stability and burnout performance
Here, calculation of the low calorific value is based on the Mendeleyer formula38 by Q ar.net = 339Car + 1030Har − 109(Oar − Sar) − 25.1Mar (14)
where Car, Har, Oar, Sar, and Mar denote the percentages of carbon, hydrogen, oxygen, sulfur, and moisture components of coal based on a received basis element analysis (%). The theory of air quantity of solid fuel is indicated by eq 15 0
L = 1.293V
dα = kf (α)f (PO2) dτ
0
= 0.1149Car + 0.3426Har − 0.0431(Oar − Sar)
= k 0 exp[−Ef /(RT )]f (α)f (PO2)
(15)
0
where f(α) = (1 − α) , α is the portion of burning combustible material, k0 is the frequency divisor, R is the gas reaction constant, T is the temperature, τ is the time, Ef is the activation energy, f(α) is the function associated with the coal combustion reaction, and f(PO2) is the function associated with the oxygen partial pressure. Activation energy may respond in the power generation capacity of fuel by
where L is the theory-required air quality per kilogram of received basis fuel (kg/kg) and 1.293 is the air density on a standard state (kg/m3). 3.3. Actual Required Air Quantity Per Kilogram of Coal Combustion. According to the normative appendix (Appendix B, China DL/T5145-2002), the actual air quantity is indicated by V = αV 0(1 + 0.0012dk )
(16)
P=
where α and dk denote the excess air ratio (%) and the water vapor content in dry air per m3 on the standard state (g/m3), respectively. 3.4. Flue Gas Volume of Coal Combustion Per Kilogram. The flue gas volume of theory is indicated by
=
(17)
The calculation of SO2 is indicated by MSO2 = BC
Sar 64 100 32
(18)
(19)
⎤ ⎡ A CCA η = 100⎢1 − ⎥ (100 − CA )(100 − A C) ⎦ ⎣
⎛ H O ef = Q ar.net⎜1.0064 + 0.1519 ar + 0.0616 ar Car Car ⎝ Nar ⎞ ⎟ Car ⎠
(20)
Mar 100
(23)
(24)
(25)
where η is the burnout rate (%), AC is the ash content (%), and CA is the reference value of fly ash carbon content (%). CA (reference value of fly ash carbon content) is 1.4%. The char burning rate was calculated using the model by Field et al., and this model is referenced in combustion after drying45
where Car, Har, Oar, Sar, and Nar denote the percentages of carbon, hydrogen, oxygen, sulfur, and nitrogen components of coal based on a received basis element analysis (%). Exergy of fuel per kilogram supplied to the boiler is indicated by40 Ef = Q ar.net + 2438
339Car + 1030Har − 109(Oar − Sar) − 25.1Mar 3600
where Z is the ignition characteristic coefficient of coal, Vy is the volatile component on a received basis, T2 is the ignition temperature, and (dG/dτ)max is the maximum of the weight loss rate of coal.42−44 In an actual combustion process, the output of volatile relates with coal characteristics, granularity, furnace type, and parameters of running conditions. The burnout rate can accurately reflect the impact of pulverized coal to combustion. The burnout rate is indicated by
3.5. Analysis of Exergy and Capacity Generating Power. Exergy efficiency of the boiler is calculated by eq 2039
+ 0.0429
3600
Z = V y(dG /dτ )max /T2
where MSO2 is the SO2 content of flue gas (mg/m3) and Sar is the sulfur content on a received basis (%) . For different quality grades of coal, even of the same kind of coal, different combustion efficiency leads to different emissions. Here, incomplete combustion emissions formed by the form of particles or cinder. The actual flue gas volume is indicated by V y0 = VRO2 + V N02 + V H0 2O + 1.0161(α − 1)V 0
Q ar.net
3.6. Ignition Characteristic Coefficient of Coal and Burning Rate. Favorable or unfavorable factors of moisture should all be considered. The ignition characteristic of coal is indicated by eq 24. It is influenced by the volatile on a received basis (Vy), ignition temperature (T2), and maximum value of the weight loss rate (dG/dτ)max. The ignition of the coal particle will be easier with a bigger value of the characteristic coefficient of coal (Z)
V y0 = VRO2 + V N02 + V H0 2O = 0.01867Car + 0.007Sar + V N02 + V H0 2O
(22)
n
⎛ kk ⎞ dC = −⎜ c d ⎟PgπDp2 dt ⎝ kc + kd ⎠
(21)
kd =
where Qar.net is the low calorific value of fuel on a received basis, 2438 is the latent heat of vaporization of water, and Mar is the 4424
0.75 5.06 × 10−7 ⎛ Tp + Tg ⎞ ⎟ ⎜ Dp ⎝ 2 ⎠
(26)
(27)
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Figure 2. Water content (moisture) and calorific value in four kinds of coal samples.
⎛ 5.0 × 104 ⎞ ⎟ kc = 1.2 exp⎜⎜ − RTp ⎟⎠ ⎝
in a significant statistical law. Element analysis was used for the prediction of GCV by mathematical models and experiments. The relationship between the carbon content, hydrogen content, and calorific value revealed the linear characteristic. Not only moisture but also ash content had an obvious effect on the calorific value. Others, such as volatile, sulfur component, and oxygen component, also had effects on the calorific value, although they were less affected and had a poor linear relationship. The calculation results and curves based on the literature data47−50 are described in the paragraph. To clearly distinguish drying characteristics, four coal samples are labeled in the figures as 1, bituminous coal; 2, anthracite coal; 3, lignite; and 4, coal refuse. For a system, the system characteristic P is influenced by n parameters.
(28)
where C, Kc and Kd, Pg, and Tg denote the char mass (kg), chemical and diffusion rate coefficients (kg m−2 s−1 Pa−1), partial pressure of oxygen (Pa), and gas temperature (K), respectively. The moisture impact on pulverized coal combustion characteristics is investigated by means of a three-dimensional numerical simulation. Results show that, as the moisture increases, the flame temperature and NOx mole fraction decrease, while the O2 mole fraction increases in the region near the burner and the peaks of the flame temperature and NOx mole fraction shift downstream. Also, with the increase in the moisture, the unburned carbon fraction is increased and the NOx conversion is decreased at the outlet of the furnace. It is found that the contribution of thermal NOx to total NOx drastically decreases with an increasing moisture content, whereas emissions of fuel NOx increase.46
α = {α1 , α2 , α3 , ..., αn} P = f {α1 , α2 , α3 , ..., αn}
4. ANALYSIS OF EXPERIMENTAL DATA 4.1. Relationship of Coal Elements and Calorific Value. These seven elements of coal and calorific value exist
When α = α* = {α*1 , α*2 , α*3 , ..., α*n }, the system characteristic is P*, P* = f{α1*, α2*, α3*, ..., αn*}. The sensitivity analysis is defined to investigate the trend of system characteristic P deviating 4425
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Figure 3. Water content (moisture) and theoretical power generation capacity in four kinds of coal samples.
standard state P* as a result of parameters ranging, when every parameter range is within a suitable scope.51 The dimensionless sensitivity function is defined as dP(αk) αk S(αk) = dαk P
(29)
When αk = α*k , the sensitivity factor is given as dP(αk) S*(αk) = dαk M:
αk = αk*
αk* P*
Q = −25.1M + 21181.2425
(30)
25.1M −25.1 + 21181.2425
(32)
C:
Q = 339C + 1650.84375
(33)
SC = −
339C 339C + 1650.84375
(34)
H:
Q = 1030H + 17354.01125
1030H 1030H + 17354.01125
(36)
O:
Q = −109O + 22643.37625
(37)
SO = −
109O −109O + 22643.37625
(38)
S:
Q = 109S + 20872.67125
(39)
SS = −
109S 109S + 20872.67125
(40)
Before drying, M = 37.15, SM = 0.070 357; C = 37.86, SC = 0.963 215; H = 2.13, SH = 0.678 53; O = 7.53, SO = 0.061 598; and S = 0.50, SS = 0.004 09. After drying, M = 10.1875, SM = 0.012 22; C = 56.8575, SC = 0.921 109; H = 3.4675, SH = 0.170 678; O = 15.76, SO = 0.082 093; and S = 0.485, SS = 0.002 526. According to these data, the calorific value is mainly effected on elements of C and H. The elements of C and H will be applied in relevance of combustion and elements of coal. 4.2. Relationship of the Water Content (Moisture) and Calorific Value in Combustion. Bituminous coal, anthracite coal, lignite, and coal refuse have different ranks of calorific value and respective material characteristics. The increasing
(31)
SM = −
SH = −
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Figure 4. Water content (moisture) and generator coal consumption in four kinds of coal samples.
4.3. Relationship of the Water Content (Moisture) and Capacity of Theoretical Power Generation. In curves of Figure 3, the theoretical power generation capacity of four coal samples shows better linear magnification. Coal refuse is widely applied to the coal-fired power plant near the coal mine as its low rank. The theoretical power generation capacity will be obviously magnified in curves, while coal refuse will be applied in a coal-fired power plant. On the basis of the above analysis, its relation is recommended by
range of the calorific value is used to evaluate drying characteristics of coal. According to data of the coal sample, the calorific value is rapidly increased after drying (Figure 2). On the basis of the above analysis, the calorific value of combustion after drying is recommended by Q cvc = Hdc( − KdMar‐online + Q d1) ⎛ ⎞ R 90 = Hdc⎜ − Kd 0.048Mar 0.46 + Q d1⎟ t2 ⎝ ⎠
(41)
Ppgc = λd‐pgcλc‐pgc( −βd Mar‐online + Pb‐pgc)
where Qcvc, Hdc, Kd, Mar‑online, Qdl, R90, t2, and Mar denote the calorific value of coal after drying, relevance coefficient of drying and combustion, coefficient of drying coal, online moisture of coal, calorific value of the drying limit, percentage of pulverized coal fineness, temperature of the powder−wind mixture (i.e., temperature of dried coal), and percentage on a received basis of moisture, respectively. Equation 41 connects with drying and combustion. Of course, eq 41 is by no means an all-inclusive drying model. Extraordinary, Hdc and Kd are recommended on the basis of an industrial trial of drying and combustion.
(42)
where Ppgc, λd‑pgc, λc‑pgc, βd, Mar‑online, and Pb‑pgc denote the power generation capacity, relevance coefficient of drying and power generation capacity, relevance coefficient of combustion and power generation capacity, coefficient of drying characteristics, online moisture of coal, and basic power generation capacity of coal, respectively. Equation 42 connects with drying and the power of combustion. Extraordinarily, βd, λd‑pgc, and λc‑pgc are recommended on the basis of an industrial trial of drying and combustion. 4427
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Figure 5. Water content (moisture) and theoretical air volume in four kinds of coal samples.
Figure 6. Water content (moisture) and theoretical flue gas emissions in four kinds of coal samples.
4.4. Relationship of the Water Content (Moisture) and Generator Coal Consumption. Generator coal consumption is monitored as a target parameter of saving coal and reducing greenhouse gas emissions. In Figure 4, while low moisture (
