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Cooper, Kim, and MacDonald

Volume 49 April 1999 Journal of the Air & Waste Management Association 4 7 1

ISSN 1047-3289J. Air & Waste Manage. Assoc.49:471-476

C o p yr i g h t 1 9 9 9 A i r & W a s t e M a n a g e me n t A s s oc i a t io n

T E C HN I C A L PA P E R

Estimating the Lower Heating Values of Hazardous andSolid Wastes

C. David Cooper, Brian Kim, and John MacDonald

Civil & Environmental Engineering Department, University of Central Florida, Orlando, Florida

ABSTRACT

A new equation is proposed to predict the lower heating

value of hazardous and non-hazardous materials. The

equation was developed by a statistical correlation of heat-

ing value and composition data for a variety of materials

as reported in a number of sources. The model takes intoaccount the carbon, hydrogen, oxygen, chlorine, and sul-

fur content of the material being combusted.

INTRODUCTION

The incineration of hazardous and solid wastes is an im-

portant technique for destroying and disposing of these

materials. Effective incineration achieves very high de-

struction and removal efficiencies of principal organic haz-

ardous constituents and minimizes the formation of prod-

ucts of incomplete combustion. Such performance re-

quires a sufficiently high temperature, a long enough resi-dence time, an excess of oxygen, and good turbulence in

the gases to promote completion of the oxidation reac-

tions to produce stable end products. In the design of the

combustion chamber and the afterburner, a good estimate

of the lower heating value (LHV) of the waste to be incin-

erated is important. However, in most instances, only the

higher heating value (HHV) of the waste is reported.

IMPLICATIONS

Prediction of heating values of hazardous and solid wastes,

and other hazardous and non-hazardous combustiblematerials, is important for several reasons, including com-

bustion analysis and the design of combustion equipment.

For mixtures of wastes, heating values often must be de-

termined experimentally, which may introduce questions

as to the accuracy of the testing and whether the sample

being tested is representative. Existing prediction correla-

tions either do not consider all the atoms often found in

hazardous waste, or have been based on data sets that

are too sparse. This paper presents a generalized equa-

tion developed from a diverse group of materials, which

performs well statistically and depends only on the ulti-

mate analysis. It is proposed for use on a variety of solid

or liquid materials.

The LHV is a better measure than the HHV of the heat

released by the waste under actual operating conditions.

The HHV is the gross heat released when a small sample of

the material is burned in a test calorimeter at a reference

temperature (usually 25 oC) and all products are in their

standard states at that temperature. The HHV includes theheat of condensation of water vapor formed in the com-

bustion reaction, which is not realistic for industrial com-

bustion equipment. The LHV is related to the HHV through

the heat of vaporization of water, as shown for methane:

CH4

+ 2 O2 CO

2+ 2 H

2O (liq) (1)

whereHHV= 212,800 cal/gmole CH4,

CH4

+ 2 O2 CO

2+ 2 H

2O (gas) (2)

whereLHV= HHV - 2*(Hv

of water) = 212,800 - 2*

10,519 = 191,762 cal/gmole CH4.

If the material burned contains chlorine, then HCl is

formed as a principal product of combustion and, by anal-

ogy, the HHV and LHV are related by the heats of vapor-

ization of both water and HCl.

In calculating the initial heat balance on an incinerator

to determine the amount of supplemental fuel required, the

usable heat released from the waste must be calculated. Us-

able heat can be defined as the LHV less the heat required to

vaporize any free water in the waste and adjusting for the

dilution effect of any noncombustible ash in the waste. For

design, the percent carbon combustion also must be taken

into account because small amounts of unburned carbon

usually remain in the ash. Likewise, heat losses through the

walls of the furnace must be subtracted. The remaining heat

is the net usable energy that is available to heat the combus-

tion gases and excess air. Thus, the LHV is needed to predict

the expected final temperature of the combustion gases.

Several methods have been used in the past to estimate

the HHV and LHV of wastes. Brunner1 cites DuLongs

approximation (which was developed for coal) as follows:


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4 7 2 Journal of the Air & Waste Management Association Volume 49 April 1999

HHV = 14,544 C + 62,028 (H - 0.125 O) + 4,050 S

(3)

whereHHVis in Btu/lb of waste, and C,H, O, and S are

the weight fractions in the waste of carbon, hydrogen,

oxygen and sulfur, respectively. Theodore and Reynolds2

report a modified form of DuLongs equation

LHV = 14,000 C + 45,000 (H - 0.125 O) -

760 Cl + 4,500 S (4)

whereLHVis in Btu/ lb of waste.

In eqs 3 and 4, the term (H - 0.125 O) reflects the

assumption that any oxygen in the waste will preferen-

tially combine with hydrogen in the waste in the mass

ratio of 8:1 to produce water and, thus, prevent that much

hydrogen from reacting with atmospheric oxygen. Pre-

sumably, this will avoid the exothermic effect (heat re-

leased by) that amount of hydrogen, if it had reacted withmolecular oxygen in the air.

Recently, Liu, Paode and Holsen3 reviewed several re-

lationships that have been applied to municipal solid wastes,

including ones based on physical composition, proximate

analysis, and ultimate analysis. They conducted a statisti-

cal step-wise multiple regression analysis to develop an equa-

tion for predicting the net calorific value of the municipal

solid waste from Kaohsiung City, Taiwan, and concluded

that their equation based on ultimate analysis was better

than previous models/methods. They included the ultimate

analysis parameters (C, H, O, N, S, and water) as possibleindependent variables to develop their final equation

Hn = 1,558.80 + 19.96 (%C) + 44.30 (%O) -

671.82 (%S) - 19.92 (%W) (5)

whereHn = net calorific value (kcal/kg of waste), and %C,

%O, %S, and %W= % by weight of carbon, oxygen, sul-

fur, and water, respectively.

One disadvantage of this result is that it is purely sta-

tistical and, therefore, may be appropriate only for the spe-

cific municipal waste studied. For example, it has no sepa-rate coefficient for hydrogen and a negative coefficient on

sulfur. The authors note that their finding of no significant

effect of hydrogen is in contrast to the results of DuLong

and others. Their waste was low in sulfur and, thus, the

negative sign did not affect the result significantly. How-

ever, the oxidation of sulfur is exothermic, so this runs

counter to expectations. Therefore, this equation cannot

be extended with confidence to other materials.

OBJECTIVES

This paper results from the interest and efforts of two stu-

dents in a graduate class at the University of Central

Florida (UCF) on hazardous waste incineration. After re-

viewing ways to estimate heating values of wastes as part

of the class, the conclusion was made that a better method

for predicting LHV of hazardous wastes was needed. Thus,

the authors set out to develop an equation based on ulti-

mate analysis that could be applied to a variety of hazard-

ous compounds. A secondary objective was to see if the

same equation could be made applicable to non-hazard-

ous materials as well, so the authors included a number

of non-hazardous materials in the database.

METHODOLOGY

Data Sources and Data Reduction

In conducting this work, data were gathered from several

sources on the reported heating values of two kinds of

materials. Twenty different hazardous compounds were

selected that contained various percentages of carbon,

hydrogen, chlorine, sulfur, oxygen, and nitrogen, as

shown in Table 1. Twenty different non-hazardous ma-terials were selected, again with an eye for diversity of

composition. Because data on non-hazardous solid

wastes were difficult to find, some data on coals and

other solid materials were included in the database, as

shown in Table 2.

The HHVs for the hazardous materials were re-

ported for pure compounds; the HHVs for the non-

hazardous materials were generally reported by the

original authors on an as-is basis, which includes

free water and ash. These HHVs first were adjusted from

an as-is basis to a dry, no-ash basis as follows:

HHVdna

= (HHVasis

)/(1- fw

- fash

) (6)

where fw

and fash

= mass fractions of free water and ash in

the waste, respectively. The LHVs were then calculated as

follows:

LHVdna

= HHVdna

- mwH

vw- m

HClH

vHCl(7)

where mw

= mass of water produced by combustion (lb

H2O/lb waste), mHCl = mass of HCl produced by combus-tion (lb HCl/lb waste), and H

v= heat of vaporization

[Btu/lb (1049 for water, 884 for HCl)].

The resulting LHVs of the hazardous and non-hazard-

ous compounds are shown in Tables 1 and 3, respectively.

In contrast to the non-hazardous materials (discussed in

the next two paragraphs), the data reduction for the haz-

ardous compounds was much simpler due to the fact that

each compound was essentially pure (without free water or

ash) and was represented by one chemical formula. In or-

der to provide for a more robust data set, 3 and 10 data

sources were used for the hazardous materials and non-haz-

ardous materials, respectively.


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For the non-hazardous materials, assumptions some-

times were required when handling data from different

sources. In some cases, whether a reported heating valuewas the HHV or the LHV was not stated explicitly, but

from other comments in the text, the inference was that

the heating value was the HHV. When the authors were

unable to determine whether reported heating values

were for the as-received condition (including free water

and ash) or had been adjusted to a dry, no-ash basis, the

data were not used in this study.

The most difficult problem encountered in interpret-

ing the raw data was that many of the reported percent

compositions did not sum to 100%. (In the most severe

case, the constituents summed to 99.6%) How chlorine wasreported was a particular problem. Some sources listed chlo-

rine separately along with the major components (C, H, O,

N, and S). In other sources, the major constituents summed

to 100%, but percentages for certain trace elements (includ-

ing chlorine) were also reported. For the latter case, the

oxygen percentage was decreased by the reported chlorine

percentage. This was justified because oxygen content is

usually determined by difference. For those cases in which

chlorine was already listed as a major constituent, no ad-

justments could be made if the total did not add to 100%.

From these raw percentages, the percent water and ash were

subtracted to achieve raw dry, no-ash percentages for use

in determining the dry, no-ash HHV. However, for use in

the statistical modeling, the dry, no-ash composition was

normalized to 100%.

Statistical Treatment of Data

Numerous computer runs were made to try to correlate

the dry, no-ash LHVs of the materials to their composi-

tions. All statistical analyses were done using SAS.4 The

proposed models were formulated in two ways: as a no-

intercept model that included all the independent vari-

ables, or as intercept models leaving out nitrogen as an

independent variable. This reduced the potential infla-

tion of the variances due to collinearity. Collinearity ex-

ists because the independent variables are mass fractionsthat sum to 1.00.

The statistical modeling was conducted in several

ways. During the initial analysis, the hazardous com-

pounds were analyzed separately from the non-hazard-

ous materials data. Then the two data sets were combined

and analyzed as one data set. For each set of data, the

statistical treatment first produced a multiple regression

equation of the form:

LHVdna

= B1C + B

2H + B

3O + B

4S + B

5Cl + B

6N

(8)

or

Ta b le 1 . Hazardous compoundscomposition and heating values.

HHVa

% Wt. LHVa

Source

Compound Formula (Btu/lb) C H O N S Cl Total (Btu/lb) (Ref.)

Methylcyclohexane (l) C7H

1420,000 85.60 14.40 0.00 0.00 0.00 0.00 100.00 18,650 2

Benzene (l) C6H

617,990 92.24 7.76 0.00 0.00 0.00 0.00 100.00 17,270 2

DDT (l) C14H9Cl5 8,100 47.43 2.56 0.00 0.00 0.00 50.00 100.00 7,600 2Diallate (l) C

10H

17Cl

2NOS 10,120 44.44 6.35 5.92 5.18 11.86 26.24 100.00 9,357 5

Dimethyl carbamoyl chloride (l) C3H

6ClNO 9,140 33.50 5.63 14.88 13.03 0.00 32.96 100.00 8,400 5

3-Chloropropionitrile (l) C3H

4ClN 8,100 40.24 4.51 0.00 15.65 0.00 39.60 100.00 7,420 5

Endosulfan (s) C9H

6Cl

6O

3S 4,190 26.56 1.49 11.80 0.00 7.88 52.27 100.00 3,720 5

1-Acetyl-2-thiourea (s) C3H

6N

2OS 8,190 30.49 5.13 13.54 23.71 27.13 0.00 100.00 7,710 6

Benzidine (s) C12

H12

N2

16,500 78.22 6.58 0.00 15.21 0.00 0.00 100.00 15,900 6

Chloroacetaldehyde (l) C2H

3ClO 5,260 30.60 3.86 20.38 0.00 0.00 45.16 100.00 4,600 6

p-Chloroaniline (s) C6H

6ClN 11,100 56.48 4.75 0.00 10.98 0.00 27.79 100.00 10,400 6

1-(o-Chlorophenyl) thiourea (l) C7H

7ClN

2S 9,540 45.04 3.79 0.00 15.01 17.17 18.99 100.00 9,060 6

Dinitrobenzene (s) C6H

4N

2O

47,470 42.86 2.40 38.07 16.67 0.00 0.00 100.00 7,250 6

2,4-Dithiobiuret (s) C2H

5N

3S

23,820 17.76 3.73 0.00 31.08 47.42 0.00 100.00 3,470 6

1-Naphthyl-2-thiourea ( s) C11

H10

N2S 13,500 65.31 4.99 0.00 13.85 15.85 0.00 100.00 13,000 6

p-Nitroaniline (s) C6H

6N

2O

29,900 52.16 4.39 23.16 20.28 0.00 0.00 100.00 9,490 6

Propylthiouracil (s) C7H

10N

2OS 11,300 49.38 5.93 9.40 16.46 18.83 0.00 100.00 10,700 6

TCDD (s) C12

H4Cl

4O

26,170 44.76 1.25 9.94 0.00 0.00 44.04 100.00 5,770 6

Toluene (l) C7H

818,250 91.23 8.77 0.00 0.00 0.00 0.00 100.00 17,400 6

2,4,6-Trichlorophenol (s) C6H

3Cl

3O 5,180 36.50 1.53 8.10 0.00 0.00 53.86 100.00 4,690 6

aRaw data were reported in different units and required conversion to Btu/lb. All values derived from the raw data were rounded to indicate significant figures of the original data.


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LHVdna

= Int. + B1C + B

2H + B

3O + B

4S + B

5Cl

(9)

where Bi= best-fit coefficient, and C,H, etc. = mass frac-

tion of that element in the material, on a dry, no-ash ba-

sis. Furthermore, some correlations with models of other

forms were attempted, such as

LHVdna = Int. + B1C + B2(H - O/8) + B3 S + B4Cl(10)

LHVdna

= Int. + B1C + B

2(H - Cl/35.5) + B

3O + B

4S

(11)

and

LHVdna

= Int. + B1C + B

2(H - O/8 - Cl/35.5) + B

3S

(12)

During the statistical work, F tests were performed on the

models developed from each of the two individual data

sets and from the combined data set. The model based on

the combined 40-point data set was as good as either of

the models based on individual data sets, and was not

being driven by one or the other data set.

Regression diagnostics were performed to check for

non-constant variance of the residuals, bias among the vari-

ables, tolerances, and outliers. Partial residual plots showed

no bias or non-linearity occurring among the variables.

There were some fan-shaped variance plots, which were

corrected by using weights equal to the squares of the fit-

ted values. Collinearity was minimized by using the formof the model equation shown in eq 9 or 12. Tolerances were

checked and found to be greater than 0.08, indicating that

collinearity was not a problem. Externally studentized

residuals were evaluated to determine if any of the points

were outliers. One possible outlier was identified (sewage

sludge), but that datum was retained in the modeling.

RESULTS

Eqs 9 through 12 were modeled using each of the 20-

point data sets (hazardous and non-hazardous materials),

in addition to the combined data set. For all equations

and for all data sets, very similar values were obtained for

T ab le 2 . Non-hazardous materialsreported values.

Reported % Wt.b

(Reported) Source

Fuel HHVa

(Btu/lb) C H O N S Cl H2O Ash (Ref.)

Sewage sludge 1,700 13.0 2.0 6.0 2.3 0.3 0.01 67.0 9.0 7

RDF 5,590 33.0 6.0 22.5 1.0 0.2 0.2 22.5 14.5 7

Tire-derived fuel 16,250 83.87 7.09 2.17

c

0.24 1.23 0.16

d

0.62 4.78 8Black liquor 5,880 34.9 3.05 35.1

c0.11 2.9 0.67 0

e0

e9

Auto fluff 7,810 39.72 4.58 11.76c

0.92 0.25 0.77 0f

42.77 10

Byker densified RDF 6,914 36.0 5.1 31.82c

0.5 0.12 0.32 11.4 14.8 11

Castle Bromwich densified RDF 8,685 46.5 6.7 32.3c

0.8 0.18 1.09 2.3 11.0 11

Coal 12,890 70.3 4.72 6.43c

1.56 1.59 0.37d

7.6 7.8 11

Average RDF 4,020 23.48 3.17 17.84 0.64 0.21 0.46d

37.79 16.87 12

RDF 7,313 42.73 6.37 26.53 0.79 0.47 0.27 0f

22.83 13

RDF 7,398 42.49 5.46 25.46 0.56 0.14 0.35 0f

25.54 13

Slurry (Upper Freeport coal) 13,500 73.73 4.89 6.30c

1.34 1.29 0.17 0f

12.28 14

West Virginia bituminous coal 10,100 63.27 4.40 4.73 1.25 3.38 0.04 8.00 14.93 15

Texas lignite coal 6,900 40.60 3.10 13.10 0.70 1.00 0.04 32.20 9.26 15

Illinois bituminous coal 10,100 57.50 3.70 5.80 0.90 4.00 0.10 12.00 16.00 15

Wyoming subbituminous coal 8,020 47.87 3.40 10.83 0.62 0.48 0.03 30.40 6.37 15

Absaloka, MT, coal 8,810 65.6 4.5 15.1 0.8 0.8 0.02 0f

13.2 16

Navajo, NM, coal 8,745 56.6 4.3 12.5 1.1 0.8 0.03 0f

24.6 16

River-King, IL, coal 8,900 54.5 4.0 8.2 0.9 4.2 0.05 0f

28.2 16

Pyro, KY, coal 12,200 68.5 5.9 5.7 1.4 4.3 0.25 0f

15.2 16

aRaw data were reported in different units and required conversion to Btu/lb. All values were rounded to indicate significant figures of the original data.

bPercent compositions may not sum to 100% due to rounding and the inclusion of other elements (sodium, potassium, etc.) in the overall composition.

cOxygen content determined by difference.

dValue represents a trace percentage for an oxide compound. This percentage was incorporated into the overall composition by reducing the oxygen percentage by an equivalent

amount.eComposition reported for dry, no-ash basis.

fComposition reported for dry basis.


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F i gu re 1 . P r e di c t e d v e r s u s a c t u a l L H V s, u s i ng m o de l s o f t h e fo r m of

e q 1 2 o n t h re e d at a s et s .

Table 3. Non-hazardous materialsdry, no-ash basis.

HHVa

(Btu/lb) % Wt.a, b

LHVa

(Btu/lb) Source

Fuel Dry & No ash C H O N S Cl Dry & No Ash (Ref.)

Sewage sludge 7,300 55.1 8.5 25.4 9.7 1.3 0.04 6,500 7

RDF 8,880 52.5 9.5 35.8 1.6 0.3 0.3 7,980 7

Tire-derived fuel 17,180 88.66 7.49 2.14 0.25 1.30 0.16 16,480 8Black liquor 5,880 45.5 3.97 45.7 0.14 3.8 0.87 5,500 9

Auto fluff 13,650 69.40 8.00 19.20 1.61 0.44 1.35 13,560 10

Byker densified RDF 9,616 48.7 6.8 43.11 0.7 0.16 0.43 9,559 10

Castle Bromwich densified RDF 9,780 53.1 7.7 36.9 0.9 0.20 1.25 9,701 11

Coal 15,240 83.1 5.58 7.16 1.84 1.88 0.44 15,190 11

Average RDF 8,860 51.50 6.95 38.67 1.40 0.46 1.01 8,790 12

RDF 9,477 55.38 8.26 34.38 1.02 0.61 0.35 9,409 13

RDF 9,936 57.06 7.33 34.19 0.75 0.19 0.47 9,881 13

Slurry (Upper Freeport coal) 15,400 84.05 5.57 7.18 1.53 1.47 0.19 15,300 14

West Virginia bituminous coal 14,930 82.09 5.71 6.14 1.62 4.39 0.05 14,890 14

Texas lignite coal 11,800 69.35 5.30 22.38 1.20 1.71 0.07 11,700 15

Illinois bituminous coal 14,030c

79.86 5.14 8.06 1.25 5.56 0.14 13,990c

15

Wyoming subbituminous coal 12,700 75.71 5.38 17.13 0.98 0.76 0.05 12,600 15

Absaloka, MT, coal 13,400 75.6 5.2 17.4 0.9 0.9 0.02 13,360c

15

Navajo, NM, coal 13,380 75.2 5.7 16.6 1.5 1.1 0.04 13,330 16

River-King, IL, coal 13,800 75.9 5.6 11.4 1.3 5.9 0.07 13,700 16

Pyro, KY, coal 14,810 79.6 6.9 6.6 1.6 5.0 0.29 14,750 16

aAll values were rounded to indicate significant figures.

bPercent compositions are normalized to 100%, but may not sum to 100% due to rounding.

cValues should be rounded to three significant figures, but were kept at four to indicate a difference between HHV and LHV values.

the correlation coefficients (R2) and variances. Therefore,

only the final results for the models depicted by eq 9 and

12 are presented here.

In the scatter plot shown in Figure 1, the predictions

using models of the form of eq 12 for both 20-point data

sets are plotted, along with the predictions from the 40-

point combined data set. All the data sets are modeled

well. The R2 values were all about 0.95 for all three data

sets (using all data points) and were about 0.97 when the

tests excluded the suspected outlier.

The models represented by eqs 9 and 12 were com-

pared using the combined data set. The results are

shown in Table 4 and Figure 2. As can be seen, both

models fit the data well. In fact, in Figure 2, the two

lines representing the least-squares fit of the predicted

versus actual data for each equation appear to lie al-

most on top of each other. However, eq 12 has fewer

coefficients (resulting in a slightly lower variance) and

has a positive coefficient on sulfur; therefore, it was

selected as the best model. In addition, with this equa-

tion, all coefficients were found to be statistically dif-

ferent from zero, except for the intercept.

CONCLUSIONS

The high R2 values and the good appearance of Figures

1 and 2 lead to the conclusion that the fitted equa-

tions provide accurate predictions of the LHVs for a

variety of materials. Based on the work done in this

study, the authors conclude that a good general for-

mula for predicting the LHV of a hazardous or non-

hazardous waste or other combustible material is


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F ig ur e 2 . P r ed i ct e d v e rs u s a c tu a l L H Vs , co m pa r in g e q 9 t o eq 1 2

u s i n g t h e c o mb i n e d d a t a s e t .

LHVdna

= -791 + 17,050 C +

32,030 (H - O/8 - Cl/35.5) + 4,591 S (13)

whereLHVdna

= lower heating value on a dry, no-ash basis

(Btu/lb); and C, H, O, Cl, and S = mass fraction of that

element in the material on a dry, no-ash basis. For any

given waste or other combustible material, once theLHVdna

is predicted using eq 13, eqs 6 and 7 can be solved to

estimate theHHVasis

if desired.

ACKNOWLEDGMENTS

The authors acknowledge the advice of Mortaza

Jamshidian, Department of Statistics, UCF, in the statisti-

cal testing.

REFERENCES1. Brunner, C.R.Incineration Systems Handbook; Incinerator Consultants:

Reston, VA, 1996.2. Theodore, L.; Reynolds, J. Introduction to Hazardous Waste Incinera-

tion; John Wiley & Sons: New York, NY, 1987.3. Liu, J.-I.; Paode, R.D.; Holsen, T.M. Modeling the energy content of

municipal solid waste using multiple regression analysis, J. Air &Waste Manage. Assoc.1996, 46(7).

4. SAS (Release 6.1.2). SAS Institute, Inc., Cary, NC, 1989-1996.5. Surprenant, N.; Nunno, T.; Kravett, M.; Breton, M. Halogenated-Or-

ganic Containing Wastes, Noyes Data Corporation: New Jersey, 1988.6. Harris, J.C.; Larsen, D.J.; Rechstiner, C.E.; Thrum, K.E. Combustion of

Hazardous Wastes, Noyes Publications: New Jersey, 1985.7. Steinruck, P.; Ganster, G. The FICB ProcessA Novel FBC Solution.

InProceedings of the 1989 International Conference on Fluidized Bed Com-bustion; Manaker, A.M., Ed.; American Society of Mechanical Engi-neers: New York, 1989; Vol. 2, pp 863-867.

8. Characteristics of TDF (tire-derived fuel). Bulletin 20.20.1C. WasteRecovery Inc.: Dallas, TX, 1990.

9. Dayton, D.C.; Frederick, W.J., Jr., Direct observation of alkali vaporrelease during biomass combustion and gasification: 2Black LiquorCombustion at 1,100 oC,Energy & Fuels. 1995, 9(5), 765-774.

10. Rehmat, A.G., et al. Auto fluff combustion and ash agglomerate for-mation studies in a fluidized-bed combustor,Energy & Fuels. 1995,9(5), 765-774.

11. Salam, T.F.; Anjum, A. The Combustion of Densified-Refuse DerivedFuel (d-RDF) Pellets on a Chain Grate Stoker. In Proceedings of The

Institute of Energys First International Conference on Combustion & Emis-sions Control; Institute of Energy: London, 1993.

12. Sanyal, A., et al. Field Evaluation of Gas Cofiring in a Large RDF In-cinerator in the United States. The Institute of Energys First Interna-tional Conference on Combustion & Emissions Control; Institute of En-ergy: London, 1993.

13. Department of Commerce. 25 Gram Capacity Combustion Flow Calo-rimeter. NBSIR 82-2457. March 1982.

14. Masi, S., et al. Combustion Rates of Carbon Fines from FWS Feeds ofa Fluidized Combustor. InProceedings of the 1989 International Confer-ence on Fluidized Bed Combustion; Manaker, A.M., Ed.; American Soci-ety of Mechanical Engineers: 1989; Vol. 2, pp 775-781.

15. Hoskins, W.W.; Keeth, R.J.; Tavoulareas, S. Technical and EconomicComparison of Circulating AFBC vs. Pulverized Coal Plants. InPro-ceedings of the 1989 International Conference on Fluidized Bed Combus-tion; Manaker, A.M., Ed.; American Society of Mechanical Engineers:New York, 1989; Vol. 1, pp 175-180.

16. Kalmanovitch, D.P.; Hajicek, D.R.; Mann, M.D. The Effects of CoalAsh Properties on FBC Boiler Tube Corrosion, Erosion, and Ash Depo-sition. In Proceedings of the 1989 International Conference on Fluidized

Bed Combustion; Manaker, A.M., Ed.; American Society of Mechani-cal Engineers: New York, 1989; Vol. 2, pp 847-862.

Ta b le 4 . Final model parameters for predicting lower heating values.

Parameter Estimates

Model Intercept B1

B2

B3

B4

B5

R2

Eq 9 3,918 12,650 24,340 -9,725 -3,240 -5,471 0.953

Eq 12 -791 17,050 32,030 4,591 NA NA 0.948

Note: NA = not applicable.

About the Authors

C. David Cooper, PE, QEP (corresponding author), is pro-

fessor of engineering in the Civil and Environmental Engi-

neering (CEE) Department at University of Central Florida,

Orlando, FL, 32816-2450. Brian Kim and John MacDonald

are Ph.D. students in the CEE Department.

estimativa pci

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