w‘ escalate equipmebit c 10s · costs of fluegas desulfurization (fgd) systems, claw sulfur...

- Z i 3 Y 3 p&= m w‘ AIR POLLUTION CONTROL

ESCALATE EQUIPMEBIT C 10s William M. Vatavuk Chemical Engineer

uring fiscal year 1994, this author used the Bernard J. Steigerwald Opportunity for Independent Study, D sponsored by U.S. Envi-

ronmental Protection Agency’s Office of Air Quality Planning and Standards (Re- search Triangle Park, N.C.), to develop a group of quarterly indexes for adjusting or escalating air pollution control costs from one period to another. In all, nine indexes were developed, one equipment cost index (ECI) for each of nine control device categ0ries.l For convenience to the reader, additional indexes for two other equipment categories - available as part of the Producer Price Indexes compiled by the U.S. Bureau of Labor Statistics - are also presented here.

These 11 indexes - collectively known as the Vatavuk Air Pollution Con- trol Cost Indexes (VAPCCI) - can be used to escalate costs from the initial (base) period (first quarter 1994) forward to any quarter in the future. To date, final indexes have been calculated (and are presented at the end of this article) for the second, third, and fourth quarters of 1994, and first quarter 1995; preliminary indexes are provided for second quarter 1995. Quarterly updates of these cost indexes will be computed as soon as the required input data become available (box, p. 12). ’lo date, no other set of cost indexes has been developed for such a wide array of pollution-control devices.

Why use index data? Cost or price indexes are needed for two reasons: To record changes in costs or prices over time; and to escalate costs or prices from one date to another. Certain indexes, such as the widely used Con- sumer Price Indexes (CPI) or Producer

* An EPA report, Escalation Indexes for Air Pollution Control Costs [ I ] , provides the basis for this aiticle To receive a copy, see the box on p 12

Use these comprehensive cost indexes to determine

prices for 11 classes of gaseous- and particulate-

piled monthly by the US. / Bureau of Labor Statistics (BLS; Washington, D.C.), have been widely used for decades to adjust prices and wages. Nonetheless, the mix of goods and services they track may not always reflect the specific marketplace or industries in question.

For instance, to determine the price increase in widgets from 1947 to 1994, we could survey all the widget manufacturers and analyze the data to come up with an average price increase over time. But suppose we didn’t have the time or resources to do that. Perhaps we could get data on widgets from the Producer Price

control devices xes. However, if there is no PPI

specifically dedicated to widgets, then perhaps we could look up the PPI for “frobbits” (the category of devices that most closely resembles widgets). The PPI for frobbits would not give us the exact price history for widgets over this 47-year period, but it might provide a close approximation, and, it would be quicker and cheaper than surveying all of the manufacturers in the business.

The situation is much the same for air pollution control equipment. To track the change in equipment costs, or to escalate them to a future quarter, we could contact the relevant vendors for price information. However, unless you are a potential buyer, it isn’t always easy to obtain timely or comprehensive price data from busy vendors.

8 ENVIRONMENTAL ENGINEERING WORLD I NOVEMBER-DECEMBER 1995

This includes maintaining and rotating a variety of ad- dresses for sample drop loca- tions and billing, and a variety of phone and fax numbers. These phone numbers automatically forward to ERA or AS1 headquarters, where a separate phone system helps employees main- tain the charade.

Competition is good In addition to judging their own capabilities, many labs are interested in seeing how they stack up to their competitors. ASI’s Environmen- tal Performance Audit (EPA) combines a double-blind evaluation with statistical analysis to allow just such a competitive evaluation.

During the EPA program, all participating labs are evaluated using the same slate of test samples. Each is given an overall score, and each moni- tored parameter is given a score, all based on 100 points.

Environmental Resource Associates, Inc.

Clayton Environmental 245 Kemm EnvirQnmental

More than 200 labs regularly participate in ASl’s performance audits, on an annual, semi-annual or quarterly basis. While individual scores vary from audit to audit, the 10 labs mentioned above consistently score well in ASl’s independent, double-blind audits, says the firm

More than 200 labs already participate on a quarterly, semi-annual or annual basis, says Anderson. All partici- pants receive a copy of the final report, which includes all reported data, mean reported values, number of labs participating, acceptance limits, warning limits and an

overall performance evaluation. While codes protect the confidentiality of the participating labs, statistical quality-control charts allow each lab to compare its own performance to that of the other labs audited in the same period.

AS1 issues an excellence

! award to labs with cumulative scores in the top tenth per- centile for a given cycle (only those that allow 80+% of the test parameters to be evaluated are eligible). The box shows ten labs that consistently score high in the AS1 program. ERA also offers a ranking system.

Independent audits may help labs satisfy state- required, external audit re- quirements,l says Susan Shorter of Lancaster (Pa.) Environmental. “We use it to identify problems and corrective actions that need to take place.”

There are economic incen- tives, too. “We would perform these studies anyway, but it would probably cost us more to do it ourselves,” says John Spurr, quality assurance spe- cialist at Clayton Environmen- tal (Novi, Mich.).

Kathy Suttem

_I

‘For a related article, see Uniformity in lab practices: The time is now, En- vironmental Engineering World, March-April 1995, p. 5.

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As a result, many engineers prefer to use data from a compiled cost index to track changing prices. With index data, the anticipated cost can be obtained by using the following equation:

costn,= cost& (Idexj, n,w/mdexj, old) (1)

where subscriptj denotes the index value (Le., the VAPCCI provided here) for control device j .

Suppose that in first quarter 1994, a wet scrubber cost $lOO,OOO. Now esti- mate how much this scrubber would cost in first quarter 1995. If the 1994 (old) VAPCCI value was 100.0, and the 1995 (new) VAPCCI value was 109.9, then, the 1995 estimated equipment cost is:

Cost,,, = $100,000 (109.9/100.0) = $109,900

In other words, according to the VAPCCI for wet scrubbers, the cost of this scrubber would increase by an estimated 9.9% during this one-year period. Users should keep in mind that if an index is designed for escalating equipment costs (as is the VAPCCI), it will not

update the cost of electricity, labor, materials or other items that are needed to keep the system in operation.

Why not use other indexes? Before developing the VAPCCI, this author routinely relied on certain published price indexes to escalate control equipment costs - mainly the Chemical Engi- neering Plant Cost Index (CE), computed by, and published each month in, Chemi- cal Engineering magazine, and the Mar- shall and Swift Equipment Cost Index (M&S), also published in Chemical En- gineering. While better than most, these indexes are not adequate indicators of specific costs associated with air pollution control.*

Developed in 1963, the CE index has been used mainly to escalate construction costs associated with chemical process plants. It encompasses such process equipment as heat exchangers, pipes and fittings, pumps and compressors [2,3].

However, the CE index can be used to escalate. the costs of fluegas desulfurization (FGD) systems, Claw sulfur recovery plants, sulfuric acid plants, and other standalone chemical processes that are often used to control air pollution.

While some of these items are used in air pollution control systems, most are not.

M&S compiles cost data by industry. The 47 industries covered include elec- tric power, mining and milling, refriger- ating, and process industries (which includes cement, chemicals, clay and rubber products); a separate index is developed for each. Unfortunately, each index reflects the specific mix of equipment for that particular industry, as well as costs for commodities, such as labor. Thus, the M&S is too broad-based for escalating air pollution control costs.

In recent years, new Producer Price In- dexes have been developed by BLS for fabric filters and mechanical collectors. Before discussing them in the context of this article, we should present some background on the PPI, as many cost ele- ments developed by BLS were key inputs to the development of the VAPCCI values presented here.

The PPI at a glance The Producer Price Indexes measure average changes in sales prices received by domestic commodity producers “in all stages of processing.” BLS presently


tracks prices for about 3,200 commodities and processes 80,000 price quota- tions each month.

There are three primary systems of indexes within the PPI program:

Stage-of-processing indexes Indexes for the net output of industries

and their products Commodity indexes Stage-of-processing indexes track

price changes for products organized by “class of buyer” and “degree of fabrication,” and have a base of 1982 = 100. They are grouped into three major categories: finished goods (i.e., commodities that will not undergo further processing); intermediate materials, supplies and components (i.e., commodities that have been processed but which still require further processing); and crude materials for further processing (i.e., products entering the market for the first time that have not been manufactured or fabricated, and that are not sold directly to consumers). Each category encompasses subcate- gories covering such products as food- stuffs, fuels, consumer goods, and capital equipment.

The PPI devoted to the net output of industries and their products are grouped according to the Standard Industrial Classification (SIC) and the Census product code extensions of the SIC. This makes them directly comparable with other economic data (e.g., employment, wage and productivity) that are organized by SIC code.

The third type of PPI, the commodity indexes, track prices of products by sim- ilarity of end-use or material composi-

10 ENVIRONMENTAL ENGINEERING WORLD

tion. They track commodity prices, irre- spective of the industries in which they are produced.

Some commodity indexes correspond to the industry price indexes, but differ in their reference bases and index levels. While most of the commodity-based indexes have a base of 1982 = 100, most of the industry-based indexes have different bases - each corresponding to the year and month that it was introduced.

As a result, the absolute values of each index are different, but the relative, month-to-month changes in the respec- tive commodity and industry price indexes are identical. For more on the PPI, please refer to references [4] and [5].

All three types of PPI are published monthly in the BLS periodical Producer Price Indexes. For each, index values are provided for the current and preceding periods, and for the period four months prior to the current period. Percent changes are provided from the previous to the current period, and from the preceding 12 months to the current period.

The percent changes listed with the PPI are “unadjusted,” meaning the index values have not been “seasonally adjusted” to account for price move- ments resulting from normal weather patterns, regular production and marketing cycles, model changeovers, seasonal discounts and holidays. Unadjusted values are of primary interest to cost ana- lysts, marketing specialists and purchasing agents to escalate long-term purchasing contracts. For this reason, the VAPCCI also have been developed using unadjusted PPI.

New PPI values are typically listed as

UOVEMBER-DECEMBER 1995

“preliminary” for four months. Then, these values are subject to one revision by BLS, to reflect the availability of late reports and corrections by respondents.

The PPI and pollution control Of the hundreds of existing PPI, only two (fabric filters and mechanical collectors) are potentially applicable for escalating equipment costs associated with air pollution control. Because these two categories were first compiled in June 1989, they are newer than most other PPI.

Bear in mind, however, that these two new PPI have two shortcomings. First, neither specifies sizes or system design (both of whch are are important to the formulation of an escalation index).

Second, the mechanical collectors (cy- clones) index is of limited value, because such devices are rarely used as primary particulate-control devices. Most often, they are used upstream of a fabric filter, electrostatic precipitator, or other particulate-control device to remove large parti- cles from a gaseous exhaust stream prior to subsequent treatment. Thus, they are more akin to auxiliary equipment.

Despite these weaknesses, the mechanical collector and fabric filter PPI are more relevant to escalating air pollution control costs than are the more general Chemical Engineering or Marshall & Swift cost indexes. As a result, these two contemporary PPI are now being tracked and reported with the nine new category- specific indexes introduced here.

Developing new indexes The VAPCCI presented here (Table 6) consist of a dedicated Equipment Cost

a Arithmetic averages of the three ECI values. b Calculated as follows: FUA = [(High index-Low index) / I (Average index-

C Because PPI inputs to the ECI have been denoted as “unrevised” by the Bureau of Labor Statistics, these ECI values should be considered preliminary, as well. Final ECI values will be disseminated once the final second quarter 1995 PPI values become available.

loo) I ] x 100%.

Index (ECI) developed for each of the nine categories of air pollution control devices described below. Each of these ECI will allow the user to adjust equipment costs forward from the first quarter 1994 to any future quarter: Carbon adsorbers (fixed bed, regenerable) Catalytic incinerators (fixed bed) Electrostatic precipitators (“ESPs”) Flares (elevated) Gas absorbers (packed column) Regenerative thermal oxidizers Refrigeration systems Thermal incinerators (recuperative) Wet scrubbers (e.g., venturi type) For seven of these equipment cate-

gories, this article also presents historical index data, from first quarter 1989 through first quarter 1994, gathered by surveying a host of equipment vendors.

As mentioned above, new ECI were not developed for fabric filters and mechanical collectors. But, for the sake of completeness, the VAPCCI listings that appear at the end of this article (Table 6) - and will appear henceforth as a regular feature in both Environmental Engi- neering Forid and Chemical Engineer- ing magazines (box, p. 12) - include index data on fabric filters and mechanical collectors computed from the PPI.3

The vendor survey In structuring the Equipment Cost In- dexes (ECI) that make up the VAPCCI, we first selected the major categories of

While BLS supplies its data on a monthly basis, these two indexes have been converted to a quarterly basis - averaging the monthly PPI for each quarter -for ease of comparison with the other categories of the VAPCCI.

add-on control devices that are used to control air pollution from point sources. The selection was limited to add-ons because, despite the introduction of various process modifications, material substitu- tions, equipment changes and treatment- chemical injections, add-on controls are still the most commonly used way to address air pollution. And, their equipment costs can be easily quantified.

Next, a survey form was developed to gather historic price information from control equipment vendors. Because we wanted to compare historical price changes to current changes tracked by the individual ECI, the survey form also requested data to permit calculation of the ECI. And, since each type of control device has its own features, a different form was used for each of the nine categories.

Historical list prices were requested by quarter from 1989 to the beginning of 1994. The form asked for prices to be ex- pressed relative to the price in first quarter 1989 (which was then arbitrarily set at 100.0) because of a widely used cost engineering rule-of-thumb - which holds tiiat costs shouid not be escaiated over periods longer than five years.

In the second part of the form, the vendors were asked to supply price break- downs according to three device sizes - small, medium, and large. Each category- specific survey form specified the typical capacity for small, medium and large sized units. For instance, for flares, small corresponds to a flare tip diameter of 4 2 in., while medium and large correspond to 1 2 4 8 in. and >48 in.

For each device, within each size range, the survey asked for the price

‘ TABLE 4. The Equipment Cost Indexes that comprise

ai the VAPCCI are shown here for each quarter

c from second quarter L 1994 through first

quarter 1995. Preliminary ECI are shown for second quarter 1995

JpercE:kFYit price) attributable to the pri-

mary components that make up the device (Le., fans and instrumentation, flare burner tips, and so on). These break- downs were used to compute the various equipment cost indexes presented below.

The survey responses Survey responses were simply averaged and tabulated. No effort was made to weight the data before averaging; rather, each response was given equal credence.

Historical price data. Some vendors reported annual, rather than quarterly prices. To make annual-basis data com- patible with quarterly-basis data, we as- signed the same price to each quarter of a given year. Also, a few vendors were unable or unwilling to report prices for one or more quarters during the five-year period. Rather than entering zeroes or as- sumed figures for these quarters, we reported ‘‘no data” for that period, and omitted them from the averaging.

Component price data. I’here were some blanks in these responses, as well. For example, a few vendors did not report data for one or more size categories, because they had not built units of these sizes. Here again, blanks were reported as “no data” and omitted from averaging.

While some vendors reported zeroes for the “other” category of components, a few completed that line on the form. “Other” typically meant engineering, project management and startup.

Finally, some vendors indicated that the size categories on the survey form

__

ENVlRONMENTAL ENGlNffRlNG WORLD I NOVEMBER-DECEMBER 1995 1 1

aEwaaseot dnwcomparh wlfh the M developrtmT&le4, Furthermod uterI*9!W(T.bk4a) iheCfiindexmhh?ewm thiaiyuarter 1% (Tablclh). *ecl?ll;dtlx is h*gb"a" 'in Of th of the avwa e EC2 v s i w (outafpc incineram ami ESPd and4e).ebp~valI)CICx'~BftVrOfthenineuvcrogeFCi value tnaerh of the eemnd pnd thwdquar%en 1994, fhc M&S value e x a Fflvnlwinthefounhquatler 1994 Howe%er,mthrthstmMJw checenpanh. M a \due

did not correspond directly to the 5 1 7 ~ s of the units thcy mnnufxtured Instcad of reporting no data for the5e w e range\, they entcred result\, but noted that they applied to different {izcs.

Results - Historical prices We received enough vendor rerponws to present hi\torical price data for {even control device catcgorie\ (Table 1 : mi\$- ing are ESP5 and carbon :id\orption). Over the period reflected in the tablc. the price\ of six of the \even equipment categoric\ incicased, by amounts ranging from 6.7% (catalytic incinerators) to 205% (thermal incineidton). The ex- ception I ) the refrigeration system\, who\e prices decrea\ed by 0.7%, relative to tirst quarter 1989 priccc. The average change for the \even device\ was + 10.4%.

I t is diflicult to account lor each of the\e price change\, a\ many factor4 - raw material\, fabrication labor co\t\. and market force\ - ulfcct equipment pricing. However, for catalytic incinerator\ and refrigeration system\, the changes can be partly explained.

With catalytic incinerator\, ;I drop i n catalyst price\ led vendors to lower their priccs by roughly 7% between fourth quarter 1991 and l i n t qudrter 1992.-'Al- though catalytic incinerator prices increased during the next two years. thcy did not return to the thiid quarter I99 I peak level of 107.1. Similarly, a design innovation markcted by a 1e.d 'I er in re- frigcration \y\tems prompted thc com- pany to lower prices in mid-1993, c a w ing its competitors to follow w i t .

How do thex historic price change\ compare to changcc reflected by published indexe\) 'Ih anlwer th i \ question, wc compared Table 1 hi\torical pnce changes to the Maishall 8r Swift Plant

and the Chemical Engineering Equip- ment cost indexes from 1989 to first quarter 1994 [7J

Over this five-year period, the M&S increased by +10.8%. This is higher than the price increases for three of the seven devices, but is quite close to the average overall price increase (+10.4%). As close as this agreement may be, the fact re- mains that the general M&S index increase does not reflect the wide range in the category-specific price changes shown in Table 1 (from -0.7 to +20.5%).

For direct comparison, the monthly CE index (equipment component) values were converted to quarterly figures by averaging the index values for the months of a given quarter. The CE reflected a much smaller increase in the index price (+3.2%) than that predicted by the M&S index. This was also much lower than the cost increases shown for seven of the eight devices, though higher than the change registered by the eighth (refrigeration systems, -0.7%).

Clearly, during this five-year period the CE was not a good barometer of price changes for air pollution control devices. This comparison of historical data shows that the price histories for different categories of pollution control equipment are so variable that no generalized industrial index can provide an acceptable indica- tor of category-specijic price changes.

Results - Component prices While we received enough survey data to compile historic records for only seven of the nine equipment categories in question, we received enough component data from vendors to develop equipment cost indexes (ECI) for all nine control device categories.5 Below, we will examine gas absorbers as an example.

First, consider the gas absorber com-

ponent price data, shown in Table 2 [ I ] . Of the components that contribute to the cost of a gas absorber, the absorber column represents the largest fraction for all three size categories. The next largest contributors are instrumentation and controls, and system fan, with the remainder of the price more or less equally divided among the other components.

Building the cost indexes Building the individual ECI for each of the nine equipment categories required two types of data: Component price fac-

- - -~

Telephone conversation between the author and Darrell Bump (ABB Air Preheater, Wellsville, N.Y.), August 1994. 5 The averages of the reported component price factors for the nine devices are tabulated and presented in Appendix A of Reference [ I ] .

12 ENVlRONMENTAL ENGlNEERlNG WORLD / NOVEMBER-DECEMBER 1995

the average of the Fmlucer Pnce Indexes for the threz

, ewh quarterly ~ralueshowo IS the werage of rhp Producer Pnce Indexes for the

a regular feature in both Envirolp-

tors gathered from vendor surveys; and any relevant Producer Price Index data, to be matched to each of the components listed in the vendor surveys. Of the hundreds of PPI published, certain ones correspond to the individual equipment components used to build air pollution control systems.

First, we reviewed the lists of control device components and attempted to find matches among the PPI. For example, for pumps (a component of both gas absorbers and wet scrubbers), we used price data from the PPI for centrifugal pumps

We were able to find PPI matches for most of the components in this way. For those components for which there were no published PPI, we chose surrogates. For instance, as a surrogate for the column component used in gas absorbers, we used PPI data for laminated plastic plate, sheet and profile shapes (PPI# 3083-i). Our reasoning was that, as most absorber columns are fabricated of fiber- glass-reinforced plastic, the price of these columns would follow changes in the price of laminated plastic plate. Table 3 shows the PPI and other data used to con- struct the ECI for gas absorbers, and con- tains the various index values for the first and second quarters of 1994.

After all relevant component data were compiled, the equipment cost indexes (ECI) for gas absorbers were constructed by “marrying” the data in Tables 2 and 3, in the following manner. First, we se-

(PPI #3561-13).

lected cost-weighting factors, which de- note the fraction of the total equipment cost that a given component contributes (these cost-weighting factors are identical to the component price factors listed in Table 2). Referring to Table 2, the weighting factors for the absorber column would be 0.235, 0.257 and 0.170, respectively, for the small, medium, and large units.

Next, we calculated the ratio of PPI (or Employment Cost Index) for the most recent quarter, divided by the corresponding index value for the base quarter (first quarter 1994), and multiplied that quo- tient by the weighting factor. For example, using data from Table 3, this ratio for absorber columns would be 130.5A29.4 = 1.009, or a 0.9% increase. The product of this ratio and the weighting factors for absorber columns would be 0.237, 0.259 and 0.172 respectively, for the small, medium, and large units.

Finally, to obtain tic ECI h r h e pciivd in question (second quarter 1994), we added all of the products of these weighting calculations, and multiplied the sum by 100. For gas absorbers, the resulting ECI were 100.51 (small), 100.53 (medium), and 100.48 (large).

Equation 2 shows this calculation:

EcI@?cIb=c( wF)j(’J4/‘Jb) (2) where: ECIq,b = Equipment cost index for given quarter ( 4 ) and base quarter (b) (WF)j = Weighting factor, component j

TABLE 5. The normalized CE and M&S index values can be compared to the “average” ECI values provided in Table 4. In general, the CE and M&S values track fairly closely to the ECI averages, but are too broad-based to accurately track changes in individual categories of air pollution control devices

TABLE 6. Using index data provides a quickand easy method for tracking and escalating costs, especially over several time periods

Ij,b = Index (PPI, Employment Cost Index, and so on) for given quarter (4) and base quarter (b)

Table4 shows the resulting ECI data for each of the nine equipment categories, from second quarter 1994 through second quarter 1995, respectively. Ex- cept for second quarter 1995 (which is preliminary), all the other ECI values are considered final. In each table, index values are provided for small, medium, and large size ranges, as well as the average (arithmetic mean) of these three.

To assess how sensitive each index value is to device size, we devised the following statistic, which appears in the column labeled Range/Average x

For example, using data from second . . YL~atei- 1895 (T&k 4ej, the (RIA) ratio is for electrostatic precipitators:

(1 11.21-107.S5)/(109.48-100)] x 100% = 35.4%

The next highest WA ratios shown in Table 4e are 30.2% (carbon adsorbers) and 24.9% (wet scrubbers), meaning these devices are most sensitive to size- related price changes. The size-sensitiv-

6 Because ECI,,,, e may be less than 100, the absolute value of the &nominator (ECIaVer,,,-ECI~,,,. line) should be used in this calculation.


ity ratio for the other categories are somewhat lower, ranging from 1.1 to 9.6%. The FUA ratios for earlier quarters (Tables 4a to 4d) behave similarly -one or two categories show a large sensitivity (ratio value > 20%), while others showed lower size-related sensitivity.

The only conclusion that we can draw at this time is that for the majority of equipment categories in a given quarter, the R/A ratios are relatively low - typically below 20%. On the whole, this sug- gests that the ECI has a somewhat weaker dependence on equipment size than we might otherwise expect. This issue will remain under study as these indexes are updated quarterly.

Reflections on the ECI Finally, it is interesting to compare the ECI values developed here with the Chemical Engineering, and Marshall & Swift (M&S). The CE and M&S values for 1994, and for first and second quarter 1995, are listed in Table 5.

Overall, the differences among the ECI values in Table 4 and the normalized CE and M&S values are not large, absolutely or relatively. However, longer-term com- parisons could produce larger or smaller discrepancies. And, as noted in the earlier comparison of CE and M&S indexes with historical data gathered from ven-

14 ENVIRONMENTAL ENGlNEERlNG WORLD

dors, these generalized industrial indexes are not adequate for tracking category- specific changes among air pollution control devices.

In some cases, large variations in the price of a certain component may se- verely influence the overall price of the pollution-control device. In other cases, vendors may absorb a rise in individual component prices, rather than raising the price of the air pollution control device. Since price fluctuations in each of the component prices will typically be chronicled by the PPI, there is a higher probability that a weighted index (such as the ECI developed here) will track price changes more closely than would another, more generalized type of index.7

Putting the indexes to work The following example illustrates how to use the index data presented here. Con- sider a gas absorber with a first quarter 1989 equipment cost of $100,000 (Costold). What would the unit cost in third quarter 1991, and second quarter 1994 dollars, respectively?

First, refer to Table 1, where a relative

7 While it is beyond the scope of this article to address this point fully, readers are encouraged to review the full report [I] on the VAPCCI development and methodology.

UOVEMBER-DECEMBER 1995

price of 106.8 is the index value for third quarter 1991 (Index,,,). Because the original cost is in first quarter 1989 dollars, the base value is 100.0 (Indexold). After substituting these values into Equa- tion (l), we obtain:

Cost (3rd quarter 1991) = $100,000 x ( 106.8/100.0) = $1 06,800

However, escalating the original cost to second quarter 1994 dollars requires two steps. First, escalate the equipment cost to first quarter 1994 dollars via the historical price data in Table 1. The corresponding index value for first quarter 1994 is 112.2. The resulting cost is then escalated from first to second quarter 1994 using the ECI for gas absorbers in Table 4a. Here, we find the average index value of 100.51, where the base value is 100.0 (first quarter 1994).

We can combine both steps into one:

Cost (2nd quarter 1994) =

(100.51/100.0) = $112,772 (rounded to $1 12,800)

$100,000 x (112.2/100.0) x

In this calculation, note that the base value “100.0” appears twice. This hap- pens because 100.0 was chosen as the base for both the historical and calculated equipment cost indexes.

References 1 Vatavuk, William M , “Escalabon Indexes for Air Polluhon Con-

trol Costs,” u s E h ” m e n t a l Protection Agency (EPA- 452/R-95-006), Research Tnangle Park, N C , Octoher 1995

2 Matley, J , Chemical Engineering Cost Index - Revised, Chem Eng ,April 19, 1982, p 153

3 Stevens R W , Equipment Cost Indexes for the Process Indus- tnes, Chem Eng , November 1947, pp 1 2 4 126

4 Bnef Explanation of Producer Price Indexes, Producer Price In- dexes, Bureau of Labor Statlshcs, U S Dept of Labor, Wash- ington, D C., November 1993

5 “BLS Handbook of Methods,” Chapter on Producer Pnces, Bn- rean of Labor Statistics, U S Dept of Labor, Washington,

Dept of Labor, Washington, D C 1992 7 Telefax from Ellen Rafferty (Chem Eng ) to W M Vatavuk

(U S EPA, Research Triangle Park, N C ), Sept 26, 1994 8 them Eng (various issues 1989-1993) 9 Vatavuk, William M , E d , “OAQPS Control Cost Manual,”

Fourth Ed , Supplement 2, Environmental Protectlon Agency, Research Triangle Park, N C , October 1992

10 Letter from Jorgen G Hedenhag (Air Pol, Inc . Teterboro, N J ) to W M Vatavuk (U S EPA, Research Tnangle Park, N C ), June 21, 1994

11 Caustic surge leads to potent price hike, Chemical Marketing Reporter, May 28, 1994, p 5

12 Vatavuk, William M , “Eshmating Costs of Air Pollution Con-

William M. Vatavuk (3512 Angus Rd , Durham, NC 27705, tel 919-541-5309) is a senior chenucal engineer with the U S Environmental Protection Agency, Office of Air Quality, Planning and Standards (Re- search Tnangle Park, N C ) He has more than 20 years of expe- nence In the analysis of air-pollution-control costs He IS the

* During this independent study, the author also developed a set of Total Annual Cost Indexes (TACI), to comple- ment the equipment cost indexes (ECI) discussed here. The TACI attempt to factor in the impact of changing electricity, labor, and other operations and maintenance costs on equipment costs. For a detailed discussion of the

than 40 technical articles. Vatavuk is the inventor of the Vatavuk Air Pollution Control Cost Indexes for escalating equipment costs. He holds a B.S.Ch.E. from Youngstown (Ohio) State University, and is a

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ver the last 10-25 years, strict environmental regula- tions and the availability of personal computers have 0 stimulated the use of math-

ematical models to predict the dispersion of stack gases. Unfortunately, many users of air dispersion models lack an in-depth understanding of the assumptions and constraints involved in the widely used Gaussian models - and mistakenly believe that the precision achievable with today’s computers equates to accuracy.

In most dispersion models, determin- ing the ground-level pollutant concentrations beneath an elevated, buoyant plume of dispersing gas involves two major steps: First, the height to which a buoyant plume rises at a given distance downwind from the plume source is calculated. The plume rise is added to the actual height of the plume’s source stack or emission point to obtain the so-called ef- fective stack height (also called the plume centerline height or, more simply, the emission height).

Second, ground-level concentrations beneath the plume (in the absence of in- version layers aloft, which limit upward dispersion) are predicted using the Gaussian dispersion equation:

(1) C=- Q e - y 2 / 2 0 : ,-H:/20:

where: C = Predicted ground-level concentration, pg/m3, located at x meters downwind of the source stack and y meters crosswind from the plume centerline Q = Plume source emission rate, pg/s u = Horizontal windspeed at plume centerline height, d s He = Plume centerline height above ground, m (5, = Vertical dispersion coefficient (i.e., the standard deviation of the vertical emission distribution), m

UCTzCJy7C

1-HOUR GROUND-LEVEL CONCENTRATIONS p /m3) CALCULATED UNDER THE PLUME CENTER Is I E

(A) (CI . -

Seemingly minor changes in some of the variables of the Gaussian model can have a profound impact on predicted results. The predicted dispersion values for each of the four comparative models discussed in the text are provided here. As shown, when comparing the base model with the final adjusted model, the overprediction factor varies from a factor of 6 to a factor of 80, depending on the distance from the source

oy = Crosswind dispersion coefficient (i.e., the standard deviation of the crosswind emission distribution), m

Computerized dispersion models easily yield results with 10 to 12 digits beyond the decimal point. This does not necessar- ily make the results accurate. A careful study will reveal the host of simplifying assumptions and constraints [ I ] involved in deriving the simple, Gaussian model shown in Equation 1, which is used to predict the dispersion of a buoyant plume from a continuous, point-source gas re- lease. Even more assumptions are needed when engineers attempt to predict gas-dispersion behavior over complex terrain, or from multiple gas sources.

The most important constraints and assumptions involved in a Gaussian dispersion model are related to:

The accuracy of predicting the plume rise at any given downwind distance from the plume source, since plume rise affects the value of He in Equation 1

The accuracy of the dispersion coeffi- cients (oz and oy) used in Equation 1

The assumption of the averaging time period represented by the concentration C, as determined by Equation 1

To date, there is no consensus over whether C represents a 5-min, 10-min,

15-min, 30-min or 1-h average concentration. Other constraints and assumptions are discussed below.

Leaving aside the assumptions and constraints involved in the basic Gauss- ian dispersion equation, the methods for obtaining certain inputs and other specifics for the equation leave much to be desired, and depend on certain assumptions and constraints. These include atmospheric stability classifications for characterizing the amount of turbulence present; the profiles of windspeed vs. plume height; and the conversion of short-term average concentrations from one averaging time to another.

This discussion of the shortcomings in Gaussian dispersion models is not unique [ 2 4 . Unfortunately, there is still a wide- spread belief that dispersion models can predict dispersed gas plume concentrations within a range that is a factor of two or three times the actual concentrations in the real world. Others believe the models to be more accurate than that.

The apparent rationale for such opti- mism is based on sensitivity studies of the Gaussian dispersion equation, such as the one conducted by Pasquill [ 3. A sensitivity study assumes the degree of un- certainty in some key parameters used in

16 ENVlRONMENTAL ENGlNEERlNG WORLD I NOVEMBER-DECEMBER 1995

w‘ escalate equipmebit c 10s · costs of fluegas desulfurization (fgd) systems, claw sulfur...

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