Exploring a Variant of PTC 4-2013 for Real-Time Performance Monitoring of Fossil Fuel Power Plants

This paper describes a calculation protocol (based on ASME PTC 4-2013) and real-time performance-monitoring method that was developed for determining and reporting the annual heat rate for fossil fuel power plants. The ASME performance test codes [1] prescribe two methods for determining the boiler efficiency of steam electric generating unit (EGUs), i.e., the input–output method [2] and the loss method [3,4]. The latter is also referred to as the energy balance method. If every step must be followed rigorously in order to determine the boiler efficiency as suggested by the performance test codes, it would be difficult to implement the method in real-time. The tests are therefore conducted on a periodic basis. One of the main disadvantages of the loss method is that the same set of data can yield different values of boiler efficiency [5,6]. Likewise, in many instances, the input–output method does not utilize instantaneous values of the higher heating value. The reason for this occurrence is that different choices are made for items to be included as output, input, and credits, depending on the practices of that utility. It is therefore difficult to compare the performance of one unit to another based on the boiler efficiency value alone. Another drawback of these two methods is that the most important performance parameter, i.e., the unit heat rate, is not addressed.

The most important index to evaluate the performance of an EGU coal-fired power plant is the unit heat rate. This is the ratio of the rate at which energy is supplied to a unit at a given instant to the power generation at that instant. The engineering units for representing the unit heat rate are Btu/kW-hr, which implies that the unit heat rate is a single-valued parameter. However, there are two other performance indices, i.e., the boiler efficiency and the steam cycle heat rate. Under a certain strict set of definitions, the unit heat rate can be expressed as the quotient of the cycle heat rate and the boiler efficiency. Throughout the world, the power industry adopted the practice of calculating the cycle heat rate and the boiler efficiency independently of each other, ignoring the coupling between the two parameters, and then calculating the unit heat rate. The procedure for calculating the steam cycle heat rate appears to follow a universal practice. However, the boiler efficiency, which is calculated by the loss method, is calculated in many different ways, based on the control volume selected for the analysis. The practices for evaluating boiler efficiency differ from one location to another, and even in the same power station, the calculation procedures can vary from unit to unit. Thus, the unit heat rate does not have a single correct value, but has a value that depends on the boiler efficiency calculation method. This is the most serious shortcoming that exists within the power industry throughout the world. A unified method known as the output-loss method exists for determining all three indices, i.e., the boiler efficiency, the steam cycle heat rate, and finally the unit heat rate, consistently and uniquely. This real-time modeling approach will be described in this paper. The procedure will eliminate the current anomalies that exist universally in the area of power plant performance modeling.

The net unit heat rate is determined by measuring the mass flowrate of coal, the higher heating value (HHV) of the coal, and the net power generation [7]. The mass flowrate of coal is measured through weight belt gravimetric feeders [2], the higher heating value is used for calculating the heat rate in the input–output method, and the net power generation is the result of subtracting the auxiliary power from the gross power generation [7]. The HHV is measured in a lab environment [8], which implies the input–output method cannot be used in a real-time performance-monitoring system [9]. In Ref. [4], the tests performed indicated behaviors of varying conditions such as coal size distribution changing the shape of the heat rate versus O₂ curve, air flow distribution and tuning changes for different fuels, differences in kinetic behavior of the coal can have great effects on the heat rate behavior, and optimum levels of O₂ and other parameters. For real-time performance monitoring, a recent paper [10] describes the whole process models of coal-fired power plants and provides insight into important equations to determine real-time performance monitoring for coal-fired power plants.

The input–output method is a direct way to measure the net unit heat rate. This method uses the direct measurement of the energy input to the boiler and the net electrical output of the power unit [7]. The uncertainties for the input of this method are controlled by the uncertainties in the coal flowrate and HHV [7]. The flowrate uncertainty can be accurate within 1% with proper calibration, but can change over time as the calibration drifts, which causes larger uncertainties well over 1% [7]. Currently, there are no regulations for plant coal scales to be calibrated at certain intervals. The HHV measurements have an uncertainty of more than 1% in laboratory equipment [7]. The uncertainty of the coal fuel flow can vary substantially due to the accuracy and calibration of the feeder scales and can be between 2 and 8%. Per Ref. [1], the total steam generator efficiency uncertainty is normally around 3–6% for the input–output method.

The loss method [5,6] utilizes the concepts of output and losses but therein introduces heat balance energy and energy credits into the overall energy balance. While testing this method, it was concluded that the blow-down heat and auxiliary equipment heat, which previously were considered to be an output and input, respectively, were both losses. The differences between using the U.S. customary HHV and the European customary lower heating value (LHV) have on the steam generator efficiency were also considered [5]. In [5] it was noted that uncertainty analyses had yet to be conducted. However, upon discussions with steam generator test engineers [5], it was contended that most unanimously supported the heat balance method [5]. They noted that on large solid fuel steam generators, the loss data were more accessible than the data the input–output method required. It was concluded that the HHV emphasized the efficient use of all the heat in the fuel and that the increased use of continuous flue gas analyzers (CEMS) has reduced the uncertainty of flue gas loss computations. Furthermore, the loss method costs less for any size or fuel-fired unit compared to the input–output method [5]. In Ref. [6], what was considered an input, output, or loss was clarified. In that instance, the input was defined as the fuel mass flowrate multiplied by the as-fired higher heating value. Similarly, the output was defined as the heat transferred to the steam mass flows, whereas the fuel efficiency was defined as the output divided by the input. It was noted that with the varying steam generator efficiency definitions, some of the terms can be moved between categories [6].

The output-loss method, which is the focus of this paper, is a method that was developed in Refs. [2,3] and was further refined as discussed in Refs. [7,11–16]. This method, when compared to the previously mentioned input–output method and the heat balance method, has fundamental advantages. The calibration algorithm (which is one aspect of the output-loss method) assumes that the coal ultimate analysis, unburned carbon in ash, and the fuel moisture are known quantities; it is discussed subsequently. In that case, CO and O₂ concentrations measured at the economizer exit are used to derive the balance combustion equation and thus account for losses. On the other hand, the real-time algorithm (CEMS-based method) employs measured CO₂, CO, SO₂, and O₂ concentrations in the flue gas; it will likewise be described further below. In addition, the output-loss method utilizes feedwater flowrate, steam temperatures, and pressures entering and leaving the boiler. Then, in conjunction with coal mass flowrate measurements (as well as further simplifying assumptions), the fuel composition is inferred instantaneously, and that information is utilized to evaluate the unit heat rate on a real-time basis.

In the determination of the air–fuel ratio for the combustion of solid fuels, it is important to account for the ash and moisture in the fuel in the as-fired condition. Thermal modeling of a coal-fired boiler requires the balanced chemical reaction equation for the combustion of coal with moist air. Several assumptions were employed in this study: (i) an excess of air is supplied, (ii) the formation of NO_x is disregarded, (iii) the unburned carbon is assumed to be known, and (iv) the composition of coal is known by means of an ultimate analysis; this information is required to calibrate the real-time heat rate algorithm.

In Eq. (3), all molar coefficients are expressed per mole of fuel carbon. The following terms are defined:

1. a, b, c, and d are the number of moles of each element per mole of fuel carbon

2. f is the number of moles of fuel moisture per mole of fuel carbon

3. β is the stoichiometric combustion air molar coefficient per mole of fuel carbon.

4. E is the excess air.

5. w is the number of moles of ambient air moisture per mole of oxygen, available from a psychrometric analysis.

6. x is the number of moles of unburned carbon per mole of fuel carbon in the fly ash.

7. α is the number of moles of carbon monoxide per mole of fuel carbon

The fly ash generally comprises a significant fraction of all the ash generated by coal combustion. The remainder is attributed to bottom ash. Plant personnel can specify the value of FLYASH based on typical operating conditions. It is assumed that all unburned carbon resides in the fly ash, which is relatively cooler than the much warmer bottom ash. That implies the following:

Based on measurements of %O₂ and ppm CO in the flue products, the excess air E and the carbon monoxide molar coefficient α are determined by simultaneously solving Eqs. (16) and (18) for wet basis CEMS measurements, or Eqs. (19) and (20) for dry basis CEMS measurements.

Analogous expressions are applied to determine the number of moles of nitrogen and water vapor present in the boiler leakage.

This study utilizes a methodology to evaluate ℓ based on Ref. [1]. The following losses are considered: (i) dry flue gas loss, (ii) moisture in air loss, (iii) fuel moisture loss, (iv), hydrogen burning loss, (v) unburned carbon loss, (vi) formation of CO loss, (vii) fly ash and bottom ash loss, and (viii) surface radiation and convection loss. The losses are evaluated on a per-pound of as-fired coal basis, as opposed to a percentage of HHV of the coal, which is not known in real-time. Complete details regarding the calculation of the losses are provided in Ref. [17].

The left-hand side of Eq. (43) denotes the “Input” referred to in Eq. (37), whereas $Q˙steam$ represents the “Output.” The terms in square brackets indicate the “Losses.” The coal mass flowrate must be known, or it can be solved algebraically. To calculate the losses per unit mass of coal, the balanced chemical reaction equation is first obtained as outlined previously. In Eq. (43), the quantity $Q˙steam$ denotes the net heat transferred to the steam from the combustion products. Flow rate, pressure, and temperature data for the feed water, main steam, cold reheat steam, and hot reheat steam are needed to calculate the steam heat transfer $Q˙steam$⁠. These calculations are performed per PTC 4 (2013) [1].

This correction factor is a static variable and is supplied by the calibration algorithm to be used in the real-time algorithm to calculate the actual coal flowrate.

The mass of air leaking into the stack from the preheater is calculated as

In this investigation, APH_leak is a static variable and is supplied by the calibration algorithm to the real-time algorithm.

In Eqs. (53) and (54), the mole fractions for CO₂ and SO₂, respectively, are evaluated using the balanced reaction equation given by Eq. (3). These correction factors are static variables and are supplied by the calibration algorithm to the real-time algorithm.

Since the ambient pressure, temperature, and relative are known, there is sufficient information to calculate the ambient air moisture variable “w” in Eq. (3).

These measurements may be performed on either a dry or wet basis.

The corrected value of the coal mass flowrate calculated per Eq. (66) is used to evaluate all plant performance parameters as described previously. For example, the real-time algorithm utilizes the methods described previously to calculate the steam cycle heat rate using Eq. (34). Likewise, the real-time algorithm determines the gross heat rate and net heat rate per Eqs. (35) and (36), respectively. The boiler efficiency is then calculated using Eq. (33).

For the calibration algorithm, an accurate coal ultimate analysis (representative of the bulk coal being burned during the calibration) must be available. This analysis yields the coal composition and higher heating value. The calibration algorithm likewise requires such dynamic plant operation data as coal mass flowrate, steam heat transfer rate, primary and secondary air temperatures, ambient temperature and relative humidity, stack temperature, and continuous boiler rating, corresponding to when coal sampling occurred. Additional information needed by the calibration algorithm includes such quantities as the primary air/coal mass flow ratio, the LOI and fly ash percentage, the primary air/coal mass ratio, and boiler leakage. These parameters typically do not change significantly over time and were therein assumed to be static variables. If plant engineers know that one or more of these “constants” has changed they should perform another calibration with the new values. The calibration algorithm likewise relies on oxygen and carbon monoxide concentration data taken at the economizer exit to balance the combustion reaction equation. A representative flowchart of the calibration algorithm calculations is provided in Fig. 3. Further details regarding the calibration algorithm are provided in Ref. [17].

The implication is that the calibration algorithm presumes that the left-hand side of Eq. (67) is accurately known and can therein be used to evaluate the right-hand side. The real-time algorithm reverses this calculation, i.e., it utilizes CEMS data to analyze the composition of the combustion products and thus attempts to deduce the left-hand side of Eq. (67). This yields sufficient information to closely approximate the actual MAF coal composition. CEMS measurements of CO₂, O₂, and SO₂ concentrations are required. If a scrubber is installed before the stack, the method discussed in this paper requires that CEMS measurements must be performed before the scrubber.

For the real-time algorithm of the output-loss method, the ultimate analysis of the coal is assumed to be an unknown. To solve for the coal composition, values for the molar ratios of oxygen and nitrogen to carbon, as well as values of fuel moisture and ash percentages, are borrowed from the latest ultimate analysis. This permits an estimation of the moisture-ash-free coal composition to be calculated. A static LOI value is used to calculate a preliminary of unburned carbon x. Based on plant data for the ambient pressure and relative humidity, there is sufficient information to determine the moisture in the air. Correction factors obtained from the calibration algorithm are used to adjust the CEMS measurements taken in the air preheater exit back to their presumed values at the economizer exit, thereby accounting for dilution caused by air preheater leakage. Since the fuel composition is unknown, a series of iterative calculations are performed, commencing from an initial estimate that the MAF coal is comprised entirely of carbon. Five iterations are then performed to continually update the coal composition, including solving simultaneous equations for β and E, as well as values for unburned carbon and carbon monoxide concentration. The most recently available ultimate analysis is provided to the real-time algorithm, to ensure its best accuracy. Once the coal composition is inferred using the iterative procedure, the remaining losses, efficiency, and heat rate calculations in the real-time algorithm are identical to those employed by the calibration algorithm. Figure. 4 depicts the flowchart for the real-time algorithm. For additional aspects of the real-time algorithm calculation procedure, refer to Ref. [17].

To verify that the real-time algorithm can provide values of power plant performance parameters such as heat rate that compare closely to those resulting from PTC 4 testing, data from a series of performance tests conducted by a reputable company that specializes in power plant performance testing were made available to this investigation. The identity of the test agent is not herein revealed due to concerns about the confidentiality of the plant's data. The testing occurred over the span of several days on a particular coal-fired steam EGU. The goal was to determine the instantaneous heat rate for the plant, which was operated over a range of power outputs, i.e., the gross generation. In one instance, the plant was operated at full load, herein designated as valves wide open (VWO), where the nominal power output was 470 MW (1.60 × 10⁹ Btu/h). Additional performance tests were carried out at loads of 400 MW (1.37 × 10⁹ Btu/h), 350 MW (1.19 × 10⁹ Btu/h), 280 MW (0.96 × 10⁹ Btu/h), and 200 MW (0.68 × 10⁹ Btu/h). For each case, the testing strictly conformed to ASME PTC 4-2013 [1]. Over the span of loads considered in the performance testing, an ultimate analysis of the coal was obtained per Ref. [18], thereby enabling evaluation of its current composition and corresponding higher heating value. Additional measurements to support the heat rate assessment at each load were likewise executed, including such quantities as steam heat transfer rate, coal flowrate, station service, and CEMS data.

Table 1 summarizes static data for boiler leakage, AIRCOAL, LOI, FLYASH, and T_ash that were employed in the real-time analysis. These quantities represent the amount of leakage air into the boiler compared to the total amount of air entering the boiler, the air-to-fuel ratio, the loss on ignition, the percentage of the total ash amount contained in the fly ash, and the bottom ash temperature, respectively. During the period of testing representative, coal samples were collected and sent to a laboratory for an ultimate analysis test. This test includes mass percentages for the constituents of the coal, fuel moisture and ash content of the coal, and the higher heating value for the coal. Table 2 contains the averaged ultimate analysis results for each load case, as supplied to the present research program. The dynamic data shown below in Table 3 provide those values measured during the period in which the coal samples used for the ultimate analysis were evaluated. These values consist of environmental data, as well as temperature data for the air flow streams, the amount of heat transferred to the steam, the coal flowrate, and CEMS measurements for certain emissions. It is important to note the oxygen and carbon monoxide measurements were performed at the economizer exit while the carbon dioxide and sulfur dioxide measurements were performed at the air preheater exit.

Plant performance metrics obtained by the testing company was made available to the present research program to allow assessment of the ability of the real-time algorithm to calculate the heat rate. The power plant performance company reported values of measured heat rate obtained per PTC 4. These results are summarized for each plant load in Table 4, which likewise provides a comparison to the values predicted using the real-time algorithm. In these instances, the calibration algorithm utilized the most recent ultimate analysis to provide calibration factors needed by the real-time algorithm to evaluate the plant performance. Boiler efficiency was calculated per Eq. (41), wherein the control volume employed for the energy balance conformed closely to the steam boiler. As discussed previously, PTC 4 utilizes an alternate control volume analysis to determine boiler efficiency. Similarly, the cycle heat rate, gross heat rate, and net heat rate were determined using Eqs. (34), (35), and (36), respectively, using the real-time algorithm. The performance testing company did not supply data for the cycle heat rate at any plant load. It is apparent that the use of the latest calibration data yielded boiler efficiencies that closely matched the values obtained by PTC 4 test methods. The maximum discrepancy between boiler efficiencies determined using the real-time algorithm and PTC 4 test procedures was obtained at full load (VWO) and was less than a 1.7% difference. Likewise at VWO, the maximum disagreement between gross heat rate evaluated by the real-time algorithm and PTC 4 testing equaled −148 Btu/kW-h, corresponding to a 1.7% difference.

The ability of the real-time performance algorithm to accurately assess instantaneous plant heat rate is presumably enhanced if the most current coal ultimate analysis is utilized. To investigate that contention, the coal composition obtained from the 400 MW test only was used to calibrate the real-time code; all other plant data such as steam heat transfer rate, coal flowrate, and station service corresponding to the actual data acquired at each load by means of the PTC 4 tests. The result of that analysis is summarized in Table 5, which likewise compares heat rates calculated using test methods mandated by PTC 4. In that instance, the maximum disagreement between boiler efficiencies was obtained at full load (VWO), which was less than a 1.5% difference. Therein, the subsequent difference in gross heat rate at VWO was −134 Btu/kW-h, once again equivalent to a 1.5% difference from the PTC 4 value. However, modest reductions in prediction comparisons of boiler efficiency and gross heat rate between the real-time algorithm and PTC 4 testing were noted when using a single (more outdated) calibration for the other plant loads. It must be noted that only limited data were provided by the performance testing company. In this study, the maximum time lag between obtaining an ultimate analysis for the plant operating at steady-state at a load of 400 MW, and a real-time calculation based on that calibration was no more than 96 h. Few EGUs update their coal analyses stored in their distributed control systems (DCS) at such short intervals. Unfortunately, many plants fail to update their DCS coal ultimate analyses even on a monthly or yearly basis. Therefore, this real-time adaptive performance-monitoring approach potentially represents a distinct improvement. It was shown that the real-time algorithm was capable of evaluating performance parameters such as boiler efficiency and heat rate that differed from similar values obtained using PTC 4 testing by less than 2% when a recent ultimate analysis of the coal was available for calibration.

In this investigation, it was shown that the real-time algorithm accurately calculated such plant performance parameters as boiler efficiency, and gross and net unit heat rate. It can likewise be used in conjunction with a modified F-factor approach reported in Ref. [19] to more accurately evaluate the CO₂ produced per MW-h of gross plant generation as compared to CO₂ reporting per the standard EPA F-factor approach. Generally, power plant performance predictions of heat rate from the real-time algorithm were found to compare very closely to test data obtained using PTC 4-2013 test methods. It was shown that real-time performance assessments of instantaneous plant heat rate could be improved if the most current coal ultimate analysis was utilized. Since the coal composition is typically not known in real-time, the procedure used a modified output-loss approach applied to a control volume that closely conformed to the boiler. A calibration algorithm utilized an ultimate analysis obtained while holding the plant load and other factors steady, thus permitting the calculation of correction factors used during real-time performance monitoring. The losses were calculated similarly to PTC 4-2013. However, the losses were expressed on a per-pound of as-fired coal basis, as opposed to a percentage of HHV of the coal, since that quantity is not known in real-time. Small differences were observed between the heat rates calculated by the real-time algorithm when an ultimate coal analysis measured for each load was utilized, versus heat rate values predicted by the real-time algorithm using only one (presumably outdated) coal ultimate analysis. These minor discrepancies were more apparent at the lower plant loads.

An advantage of the real-time algorithm based on the output-loss method is that it requires only permanent station CEMS sensors. This is unlike PTC 4 test procedures, where temporary instrumentation is needed. Furthermore, the results can be calculated and monitored in real-time. The real-time algorithm minimizes the need for expensive new instrumentation and permits the continuous monitoring of plant heat rate as the load changes. The real-time method is rooted in ASME PTC 4 and generally exhibits improved performance prediction uncertainty (1–3%), as compared to other current approaches (e.g., F-factor), where the accuracy may only be 3–14%. The real-time method relies on station instrumentation that is typically available and is easily calibrated. It accounts for cycling and load following and allows for Relative Accuracy Test Audits (RATA).

The investigation reported in this paper was performed on a single power plant. In anticipation of new EPA heat rate regulations [20] currently being promulgated for coal, gas, and industrial units, it would be advantageous to further develop the real-time code in order to apply this method to additional EGUs, with a priority for cogeneration and gas-fired units. Such studies would provide further guidance concerning whether additional costly instrumentation would be needed in practice or would require only the existing CEMS data currently available to the plant computers. For example, the applicability of the real-time algorithm is adversely affected if CEMS sensors are only available downstream of scrubbers. It is desirable to install real-time software in plants that utilize a combination of dry- and wet-based CEMS measurements to emphasize the generality of the method. In Ref. [2], it was noted that the accuracy of the real-time heat rate monitoring protocol would be enhanced if accurate and robust methods were available to measure the moisture content in the flue gases in real-time. Such a deficiency is inherent to all currently available online EGU performance-monitoring methods. Further research would facilitate the determination of the uncertainty associated with coal analysis and fuel moisture on heat rate/CO₂ emissions on annual reporting. It is likewise proposed to investigate proposed metrics for observed plant changes using raw inputs as suitable triggers for calibration. Based on the analysis of these studies, recommended calibration triggers would be incorporated into the code. Specific suggestions include the collection of more extensive actual data at the plant(s) for a year, with new ultimate analyses being taken at least weekly, thereby yielding a minimum of 52 calibration data sets over all seasons and weather conditions. This would permit the determination of appropriate “flags” for recommended recalibrations. To provide further guidance regarding recommended re-calibration intervals, it is suggested to post-process the heat rate and other outputs from the real-time algorithm under several scenarios, e.g., using a single calibration for the year, or weekly calibrations for the year, etc. This would allow further comparison of the results from the real-time algorithm to corresponding evaluations of heat rate and CO₂ production per MW as measured by means of testing conducted strictly according to PTC 4-2013. The accuracy of the heat rate monitoring protocol described in this paper could be enhanced if accurate and robust methods were available to measure the moisture content in the flue gases in real-time. This would provide additional information needed to evaluate fuel moisture on an instantaneous basis. Such a deficiency is inherent to all currently available online EGU performance-monitoring methods. This issue merits further investigation.

This work was performed under a DOE contract awarded to ASME Standards Technology LLC (ASME ST-LLC) to develop the Annual Heat Rate Determination for Coal EGU's Project ID FE0031933 “Standardized Test Method and Calculation Protocol for Determining and Reporting Annual Heat Rate for Coal-Fueled Electricity Generating Units” Project 0166.

There are no conflicts of interest. This article does not include research in which human participants were involved. Informed consent is not applicable. This article does not include any research in which animal participants were involved.

The data sets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.

a =

moles of hydrogen per mole of fuel carbon mole/mole

b =

moles of sulfur per mole of fuel carbon mole/mole

c =

moles of oxygen per mole of fuel carbon mole/mole

d =

moles of nitrogen per mole of fuel carbon mole/mole

f =

moles of H₂O per mole of fuel carbon mole/mole

ℓ =

thermal losses per unit mass of coal MW/kg (Btu/lbm)

m =

mass, kg (lbm)

n =

moles mole

w =

moles of H₂O per mole of O₂ mole/mole

x =

moles of unburned carbon per mole of fuel carbon kg/kg (lbm/lbm)

y =

mole fraction mole/mole

$Q˙$ =

heat transfer rate MW (Btu/h)

A =

ash mass fraction kg/kg (lbm/lbm)

E =

moles of excess O₂ per mole of fuel carbon mole/mole

M =

moisture mass fraction kg/kg (lbm/lbm)

P =

plant electric generation MW

$m˙$ =

mass flowrate kg/h (lbm/h)

$n˙$ =

molar flowrate mole/h

$E˙$ =

energy transfer rate kW (Btu/h)

AIRCOAL =

air-to-coal mass ratio in primary air stream kg/kg (lbm/lbm)

ASH =

ultimate analysis ash mass %

  CoalFlCorFac =

coal flow correction factor kg/kg (lbm/lbm)

CO2CorFac =

CO₂ correction factor mole/mole

FC =

fuel carbon per unit weight of coal kg/kg (lbm/lbm)

FLYASH =

fraction of total ash appearing as fly ash kg/kg (lbm/lbm)

FM =

ultimate analysis of moisture mass %

FM2 =

moles of H₂O per mole of fuel carbon mole/mole

Le =

moles of O₂ leaking from air preheater mole

Lo =

thermal losses kW (Btu/h)

SO2CorFac =

SO₂ Correction Factor mole/mole

TOTAL ASH =

mass of total ash in exhaust, kg (lbm)

UC =

mass of unburned carbon in fly ash, kg (lbm)

UCTASH =

fraction of unburned carbon in total ash, lbm/lbm

X2 =

moles of carbon per unit weight of MAF coal, kg/mole (lbm/mole)

α =

moles of CO per mole of fuel carbon, mole/mole

β =

stoichiometric combustion air molar coefficient, mole/mole

       η_B =

boiler efficiency, %

ω =

humidity ratio, kg/kg (lbm/lbm)

AF =

as-fired

air =

air

ash =

ash

Cycle =

cycle

components =

components

coal =

coal

dry air =

dry air

dry gases =

dry gases

econ =

economizer exit

flue gas =

flue gas

formation =

formation

Gross =

gross

in =

inlet

LA =

leakage air

leak =

leak

leakage air =

leakage air

MAF =

moisture-ash-free

moisture =

moisture

Net =

net

out =

outlet

PA =

primary air

Plant =

plant instrumentation

rad =

radiation

SA =

secondary air

Stack =

air preheater exit

Standard =

standard conditions

Steam =

steam

total =

total

Unburned carbon =

unburned carbon