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Recommended Practice

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Copyright 2011 AACE® International, Inc. AACE

® International Recommended Practices

AACE International Recommended Practice No. 59R-10

DEVELOPMENT OF FACTORED COST ESTIMATES – AS APPLIED IN ENGINEERING, PROCUREMENT, AND

CONSTRUCTION FOR THE PROCESS INDUSTRIESTCM Framework: 7.3 – Cost Estimating and Budgeting

Acknowledgments:Rashmi Prasad (Author)Kul B. Uppal, PE CEP

A. Larry Aaron, CCE CEP PSP

Peter R Bredehoeft Jr., CEPLarry R. Dysert, CCC CEPJames D. Whiteside II, PE

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AACE International Recommended Practice No. 59R-10

DEVELOPMENT OF FACTORED COST ESTIMATES – AS APPLIED IN ENGINEERING, PROCUREMENT, ANDCONSTRUCTION FOR THE PROCESS INDUSTRIES TCM Framework: 7.3 – Cost Estimating and Budgeting

June 18, 2011

INTRODUCTION

As identified in the AACE International Recommended Practice No. 18R-97 Cost Estimate ClassificationSystem – As Applied in Engineering, Procurement, and Construction for the Process Industries , theestimating methodology tends to progress from stochastic or factored to deterministic methods withincrease in the level of project definition.

Factored estimating techniques are proven to be reliable methods in the preparation of conceptualestimates (Class 5 or 4 based on block flow diagrams (BFDs) or process flow diagrams (PFDs)) duringthe feasibility stage in the process industries, and generally involves simple or complex modeling (orfactoring) based on inferred or statistical relationships between costs and other, usually design related,parameters. The process industry being equipment-centric and process equipment being the cost driverserves as the key independent variable in applicable cost estimating relationships.

This recommended practice outlines the common methodologies, techniques and data used to preparefactored capital cost estimates in the process industries using estimating techniques such as: capacityfactored estimates (CFE), equipment factored estimates (EFE), and parametric cost estimates. However,it does not cover the development of cost data and cost estimating relationships used in the estimatingprocess.

All data presented in this document is only for illustrative purposes to demonstrate principles. Althoughthe data has been derived from industry sources, it is not intended to be used for commercial purposes.The user of this document should use current data derived from other commercial data subscriptionservices or their own project data.

CAPACITY FACTORED ESTIMATES (CFE)

Capacity factored estimates are used to provide a relatively quick and sufficiently accurate means ofdetermining whether a proposed project should be continued or to decide between alternative designs orplant sizes. This early screening method is often used to estimate the cost of battery-limit processfacilities, but can also be applied to individual equipment items. The cost of a new plant is derived fromthe cost of a similar plant of a known capacity with a similar production route (such as both are batchprocesses), but not necessarily the same end products. It relies on the nonlinear relationship betweencapacity and cost as per equation 1:

CostB/Cost A = (CapB/Cap A)r

where Cost A and CostB are the costs of the two similar plants, Cap A and CapB are the capacities of thetwo plants and r is the exponent, or proration factor.

(equation 1)

The value of the exponent typically lies between 0.5 and 0.85, depending on the type of plant and mustbe analyzed carefully for its applicability to each estimating situation. It is also the slope of the logarithmiccurve that reflects the change in the cost plotted against the change in capacity. It can be determined byplotting cost estimates for several different operating capacities where the slope of the best line throughthe points is r, which can also be calculated from two points as per equation 2:

r = ln(CapB/Cap A)/ln(CostB/Cost A)(equation 2)

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The curves are typically drawn from the data points of the known costs of completed plants. With anexponent less than 1, scales of economy are achieved wherein as plant capacity increases by apercentage (say, by 20 percent), the costs to build the larger plant increases by less than 20 percent.With more than two points, r is calculated by a least-squares regression analysis. A plot of the ratios onlog-log scale produces a straight line for values of r from 0.2 to 1.1.

This methodology of using capacity factors is also sometimes referred to as the “scale of operations”method or the “six-tenths factor” method because of the reliance on an exponent of 0.6 if no otherinformation is available. With an exponent of 0.6, doubling the capacity of a plant increases costs byapproximately 50 percent, and tripling the capacity of a plant increases costs by approximately 100percent. In reality, as plant capacity increases, the exponent tends to increase as per figure 1. Thecapacity factor exponent between plants A and B may have a value of 0.6, between plants B and C avalue of 0.65, and between C and D, the exponent may have risen to 0.72. As plant capacity increases tothe limits of existing technology, the exponent approaches a value of one where it becomes aseconomical to build two plants of a smaller size, rather than one large plant.

Figure 1 – The capacity factored relationships shown here are logarithmic. Exponents differacross capacity ranges.

Usually companies should have indigenous capacity factors for several chemical process plants that mustbe updated with regular studies. However, the above factors should be used with caution regarding theirapplicability to any particular situation.

If the capacity factor used in the estimating algorithm is relatively close to the actual value, and if the plantbeing estimated is relatively close in size to the similar plant of known cost, then the potential error from aCFE is certainly well within the level of accuracy that would be expected from a stochastic method. Table1 shows the typical capacity factors for some process plants. However, differences in scope, location, andtime should be accounted for where each of these adjustments also adds additional uncertainty andpotential error to the estimate. If the new plant is triple the size of an existing plant and the actual capacityfactor is 0.80 instead of the assumed 0.70, one will have underestimated the cost of the new plant by only10 percent. Similarly, for the same three-fold scale-up in plant size, if the capacity factor should be 0.60

instead of the assumed 0.70, one will have overestimated the plant cost by only 12 percent. The capacity-increase multiplier is CapB/Cap A and in the base, r is 0.7. The error occurs as r varies from 0.7. Further,table 2 shows percent error when 0.7 is the factor used for the estimate instead of the actual factor.

The CFE method should be used prudently. Making sure the new and existing known plants are near-duplicates, include the risk in case of dissimilar process and size. Apply location and escalationadjustments to normalize costs and use the capacity factor algorithm to adjust for plant size. In addition,apply appropriate cost indices to accommodate the inflationary impact of time and adjustments for

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location. Finally, add any additional costs that are required for the new plant, but were not included in theknown plant.

COST INDICES

A cost index relates the costs of specific items at various dates to a specific time in the past and is usefulto adjust costs for inflation over time. Chemical Engineering (CE) publishes several useful cost indiceseach month such as the CE Plant Cost Index and the Marshall & Swift Equipment Cost Index. The CECost Index provides values for several plant-related costs including various types of equipment,buildings, construction labor and engineering fees. These values relate costs of complete plants overtime, using the 1957–1959 timeframe as the base period (value = 100). The Marshall & Swift indicesprovide equipment cost index values arranged in accordance to the process industry in which the unit isused, using 1926 as the base period.

To use either of these indices to adjust for cost escalation, multiply the un-escalated cost by the ratio ofthe index values for the years in question. For example, to determine the cost of a new chlorine plant inFebruary 2001 using capacity factored estimates where the cost of a similar chlorine plant built in 1994

was $25M, first the cost of the 1994 must be normalized for 2001. The CE index value for 1994 is 368.1.The February 2001 value is 395.1. The escalated cost of the chlorine plant is therefore: $25M x(395.1/368.1) = $25M x 1.073 = $26.8M.

Product Factor Acrolynitrile 0.60Butadiene 0.68Chlorine 0.45Ethanol 0.73Ethylene Oxide 0.78Hydrochloric Acid 0.68Hydrogen Peroxide 0.75Methanol 0.60

Nitric Acid 0.60Phenol 0.75Polymerization 0.58Polypropylene 0.70Polyvinyl Chloride 0.60Sulfuric Acid 0.65Styrene 0.60Thermal Cracking 0.70Urea 0.70Vinyl Acetate 0.65Vinyl Chloride 0.80

Table 1 – Capacity Factors for Process Plants[8]

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ActualExponent

Capacity-Increase Multiplier (CapB/Cap A)1.5 2 2.5 3 3.5 4 4.5 5

0.20 23% 41% 58% 73% 88% 100% 113% 124%0.25 20% 36% 51% 64% 75% 87% 97% 106%

0.30 18% 32% 44% 55% 64% 74% 83% 91%0.35 16% 28% 38% 47% 55% 63% 70% 76%0.40 13% 23% 32% 39% 46% 52% 57% 63%0.45 11% 18% 26% 32% 36% 41% 46% 50%0.50 9% 15% 20% 25% 28% 32% 35% 38%0.55 6% 11% 15% 18% 21% 23% 25% 28%0.60 4% 7% 10% 12% 13% 15% 16% 18%0.65 2% 3% 5% 6% 6% 7% 8% 8%0.70 0% 0% 0% 0% 0% 0% 0% 0%0.75 -2% -4% -5% -5% -6% -7% -7% -8%0.80 -4% -7% -9% -10% -12% -13% -14% -15%0.85 -6% -10% -13% -15% -17% -19% -20% -21%0.90 -8% -13% -17% -20% -22% -24% -26% -28%0.95 -10% -16% -21% -24% -27% -29% -31% -33%1.00 -11% -19% -24% -28% -31% -34% -36% -38%1.05 -13% -22% -28% -32% -36% -39% -41% -43%1.10 -15% -24% -31% -36% -40% -43% -45% -47%1.15 -16% -27% -34% -39% -43% -46% -49% -52%1.20 -18% -30% -37% -42% -47% -50% -53% -55%

Table 2 – % Error when factor r = 0.7 is used for estimate instead of actual exponent

Discrepancies are found in previously published factors due to variations in plant definition, scope, sizeand other factors such as:

• Some of the data in the original sources covered a smaller range than what is now standard.• Changes in processes and technology.• Changes in regulations for environmental control and safety that was not required in earlier plants.

Exponents tend to be higher if the process involves equipment designed for high pressure or isconstructed of expensive alloys. As r approaches 1, cost becomes a linear function of capacity — that is,doubling the capacity doubles the cost. The value of r may also approach 1 if product lines will beduplicated rather than enlarged. Whereas a small plant may require only one reactor, a much larger plantmay need two or more operating in parallel.

Large capacity extrapolations must be done carefully because the maximum size of single-train processplants may be restricted by the equipment's design and fabrication limitations. For example, single-trainmethanol synthesis plants are now constrained mainly by the size of centrifugal compressors. Costs mustalso be scaled down carefully from very large to very small plants because, in many cases the equipment

cost does not scale down but rather remains about the same regardless of plant capacity.

Despite these shortcomings, the r factor method represents a fast, easy and reliable way of arriving atcost estimates at the predesigned stage. It is helpful for looking at the effect of plant size on profitabilitywhen doing discounted cash-flow rate-of-return and payback-period calculations, and it is very useful formaking an economic sensitivity analysis involving a large number of variables.

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PROCESS Direct Costs ALL SOLID Process FLUID & SOLID Process (*) ALL FLUID Process

Mat’l Labor Total TC% Mat’l Labor Total TC% Mat’l Labor Total TC%Purchased Equipment 1.000 N/A 1.00 26% 1.000 N/A 1.00 24% 1.000 N/A 1.00 20%Equipment Setting 0.014 0.024 0.04 1% 0.014 0.024 0.04 1% 0.014 0.024 0.04 1%Site Development 0.016 0.029 0.05 1% 0.016 0.029 0.05 1% 0.016 0.029 0.05 1%

Concrete 0.038 0.054 0.09 2% 0.031 0.059 0.09 2% 0.028 0.052 0.08 2%Structural Steel 0.106 0.050 0.16 4% 0.103 0.040 0.14 3% 0.100 0.030 0.13 3%Buildings 0.016 0.006 0.02 1% 0.016 0.006 0.02 1% 0.016 0.006 0.02 0%Piping 0.200 0.160 0.36 9% 0.307 0.242 0.55 13% 0.520 0.450 0.97 19%Instrumentation & Controls 0.100 0.200 0.30 8% 0.100 0.215 0.32 7% 0.140 0.280 0.42 8%Electrical 0.109 0.086 0.20 5% 0.109 0.086 0.20 5% 0.088 0.072 0.16 3%Insulation 0.020 0.004 0.02 1% 0.030 0.004 0.03 1% 0.060 0.012 0.07 1%Painting 0.009 0.060 0.07 2% 0.009 0.060 0.07 2% 0.008 0.050 0.06 1%

Direct Costs = 1.63 0.67 2.30 59% 1.74 0.77 2.50 59% 1.99 1.01 3.00 59% PROCESS Indirect CostsLabor Indirects & Field Costs 0.160 0.392 0.55 14% 0.176 0.424 0.60 14% 0.220 0.500 0.72 14%Contractor Engineering & Fee 0.015 0.703 0.72 18% 0.016 0.759 0.78 18% 0.020 0.890 0.91 18%Owner Engineering & Oversight 0.080 0.242 0.32 8% 0.082 0.267 0.35 8% 0.085 0.330 0.42 8%

Total PROCESS Direct and Indirect = 1.88 2.01 3.89 100% 2.01 2.22 4.22 100% 2.32 2.73 5.04 100%

Excludes OSBL (non-process infrastructure), excludes land acquisition, excludes contingency, and assumes at-grade installations(*) = Most reliable data

Assumed material equipment cost (MEC) factor for bulks and direct field labor (DFL) = 1.5Labor is based on 1.0 labor productivity factor (LPF) @ $20.00 W2 rate + 91% for field indirects = $38.14 all in hourly composite labor rate

Table 3 – “Original” Lang factors (multipliers) of delivered equipment cost for capitalized costsand % of total installed costs to construct large scale capacity US Gulf Coast process plants.

Happel[28]

estimated purchase cost for all pieces of equipment (material), labor needed for installationusing factors for each class of equipment, extra material and labor for piping, insulation etc. from ratiosrelative to sum of material and added installed cost of special equipment, overhead, engineering fees,and contingency. A number of items given in table 4 below are prorated from the sum of key accounts G.Material listing in the second column refers to delivered cost to the plant site ready for erection. The laboritems in the adjoining column are the direct labor involved in erecting each of the items noted. Whenmaterial items A through F are made of expensive material such as stainless steel, the labor percentage

will be much lower than shown in table 4 which is based on carbon steel items in material column.

Item Material Labor Vessels A 10% of ATowers, field fabricated B 30 to 35% of BTowers, prefabricated C 10 to 15% of CExchangers D 10% of DPumps, compressors and other machinery E 10% of EInstruments F 10 to 15% of FKey accounts (Sum of A to F) G

Table 4 – Happel’s Method: Table 1

Item Material Labor Key accounts (Sum of A to F) GInsulation H = 5 to 10% of G 150% of HPiping I = 40 to 50% of G 100% of IFoundations J = 3 to 5% of G 150% of JBuildings K = 4% of G 70% of KStructures L = 4% of G 20% of LFireproofing M = 0.5 to 1% of G 500 to 800% of MElectrical N = 3 to 6% of G 150% of NPainting and cleanup O = 0.5 to 1% of G 500 to 800% of OSum of Material and Labor P


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Sum of material and labor PInstalled cost of special equipment QSubtotal R = P+QOverheads S = 30% of R

Total erected cost T = R+SEngineering fee U = 10% of TContingency fee V = 10% of TTotal investment W = T+U+V


It presents difficulties in piping estimation as it is time-consuming to detail the piping sufficiently toestimate it directly. If a percentage of 40 to 50% on key equipment for piping material is employed assuggested above, errors may result in the estimates of plants having a large proportion of investment inmachinery, compressors or other relatively expensive equipment. The use of “exotic” pipe material suchas Teflon or stainless will also naturally completely upset calculations made on the basis of a simplepercentage. A good check can be made on piping material by noting that valves will constitute 40% oftotal. Another item that must be considered carefully is the allowance for profit and fees to theengineering contractor. Prices are fixed by supply and demand rather than arbitrary percentages like

those noted above, so that equipment companies with a considerable backlog of orders may be able toenjoy greater profits. Another important factor to bear in mind when estimating construction costs frompublished data or company records is that these costs are not constant like the physical properties ofchemical compounds. It is necessary to correct them by the use of some type of construction index,especially when all information has not been obtained at the same time. In addition tables 4, 5, and 6above do not cover OSBL items so these should be included separately in the estimate.

Hand[24]

advanced the above approaches by applying individual factors to major equipment categories. Ata 50% error range for the quantity and for the cost of each category, the error range for each elementwould be 70.7%. But when the elements are added up, the error range of the sum (representing totalinstalled cost) is only 39.8%.

Hackney[25,26] developed an equipment ratio method with factors for labor and materials applied to not

only major equipment but also auxiliary equipment, to installation, and to various crafts, such as piping,electrical and building. The auxiliary equipment cost is usually estimated as a percent of the majorequipment; the costs of installation and craft activities are taken as percentages of the major and auxiliaryequipment summed. A checklist was included for numerically estimating the certainty with which theindividual aspects of the project are known. Examples include the amounts, physical forms and allowableimpurities in the raw materials and products and the extent to which the process design has beenreviewed. The sum of the individual ratings is an indication of how accurate the estimate is. In spite of itsmore detailed attention to uncertainty and accuracy, it does not lend itself to direct transfer to a moredetailed budget estimate. It is preferable to employ methods that can successively ”advance” to the moredetailed estimates.

Guthrie[27] developed a module method that applied the Hackney approach to individual equipmentaccounts. It used individual material factors for various crafts but one overall labor factor. The total plant

cost is the sum of the individual equipment modules, costs of linking the modules and indirect costs. Thelatter, including design engineering, project management and contractor's profit, can account for about 10to 30% of the total plant cost, depending on site topography, the economic climate of the area, the time ofyear (i.e., the weather) and the nature of the bidding process itself. The modules can also serve tomonitor costs during construction and to control the scheduling of labor since the factors are replacedwith material and labor prices and the latter are translated into labor hours. Because of the extensivesumming involved, the accuracy of this method is high. Assume for instance, that the technique is beingused for a definitive estimate and that each quantity factor and cost factor for the pump module has anaccuracy of 5%. Summing the individual pump-installation elements brings the total accuracy for the

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module into the range of 3%, and when all the modules in the cost estimate for the plant are summed, theaccuracy of the plant estimate will improve to 2% or less.

The completion of any construction project yields cost data that can be valuable for future cost estimatesprovided that these data are not time-indexed over an unreasonably large number of years. Cost data on

major pieces of equipment are readily available from computerized services whose databases are derivedfrom equipment vendor and vessel fabricator information. It is often possible to get better accuracy on thefactors for equipment installation by basing the installation outlays on the equipment size or designavailable from the flow sheets for the plant. It often reveals circumstances affecting the installation costthat are masked by the cost figures alone. The article “Sharpen Your Cost Estimating Skills” by Larry R.Dysert

[6], is a good source of process equipment factors. This document shows equipment factors forprocess equipment range from 2.4 for columns to 3.4 for pumps and motors, based upon the rawequipment costs.

Equipment costs must be estimated to gauge a project's economic viability, to evaluate alternativeinvestment opportunities, to choose from among several process designs the one likely to be the mostprofitable, to plan capital appropriations, to budget and control expenditures or a competitive bid forbuilding a new plant or revamping an existing one. Shop fabricated costs including freight derived from

cost curves is suitable for making study estimates of total plant costs and is more than adequate formaking order-of magnitude ones. Since costs are changing and costs obtained from one source are likelynot to agree with those acquired from another, costs derived from the related graphs should not beconsidered incontestable but rather should be adjusted in light of cost data from other sources accordingto one's judgment and experience.

A good source of process equipment costs is DOE/NETL-2002/1169, “Process Equipment CostEstimation”

[10] report:

Cooling tower purchased equipment cost range from $4,000 for a 150 gal/min unit to $100,000 for a6,000 gal/min. The cooling tower would consist of a factory assembled cooling tower including fans,drivers and basins.The design basis would be:• Temperature Range: 15 °F• Approach Gradient: 10 °F• Wet Bulb Temperature: 75 °F

Air cooler purchased equipment cost range from $11,000 for a 100 sq/ft to $120,000 for a 10,000 sq/ft ofbare tube area. The air cooler would consist of variety of plenum chambers, louver arrangements, fintypes (or bare tubes), sizes, materials, free-standing or rack mounted, multiple bays and multiple serviceswithin a single bay.The design basis would be:• Tube Material: A214• Tube Length: 6 – 60 Feet• Number of Bays: 1 – 3• Power/ Fan: 2 – 25 HP• Bay Width: 4 – 12 Feet• Design Pressure: 150 psig• Inlet Temperature: 300 °F• Tube Diameter: 1 Inch• Plenum Type: Transition shaped• Louver Type: Face louvers only• Fin Type: L-footed tension wound aluminum

Furnace/process heater purchased equipment cost range from $100,000 for 2 Million BTU/hour to$5,000,000 for 500 Million BTU/hour of heat duty. The furnace heater would consist of gas or oil-fired

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vertical cylindrical type for low heat duty range moderate temperature with long contact time. Walls of thefurnace are refractory lined.The design basis would be:• Tube Material: A214• Design Pressure: 500 psig

• Design Temperature: 750 °F

Rotary pump purchased equipment cost range from $2,000 for 10 gal/min to $10,000 for 800 gal/min ofcapacity. The rotary pump would consist of rotary (sliding vanes) pump including motor driver.The design basis would be:• Material: Cast Iron• Temperature: 68 °F• Power: 25 – 20 HP• Speed: 1800 RPM• Liquid Specific Gravity: 1• Efficiency: 82%

Single stage centrifugal pump purchased equipment cost range from $3,000 for 100 gal/min to $600,000

for 10,000 gal/min of capacity. The single stage centrifugal pumps would consist for process or generalservice when flow/head conditions exceed general service, split casing not a cartridge or barrel andincludes standard motor driver.The design basis would be:• Material: Carbon Steel• Design Temperature: 120 °F• Design Pressure: 150 psig• Liquid Specific Gravity: 1• Efficiency: 500 GPM = 82%• Driver Type: Standard motor• Seal Type: Single mechanical seal

Reciprocating pump (duplex) purchased equipment cost range from $4,000 for 2 HP to $30,000 for 100

HP driver power. Reciprocating pump (triplex) purchased equipment cost range from $8,000 for 2 HP to$80,000 for 100 HP driver power. The reciprocating pump would consist of duplex with steam driverhaving Triplex (plunger) with pump motor driver.The design basis would be:• Material: Carbon Steel• Design Temperature: 68 °F• Liquid Specific Gravity: 1• Efficiency: 82%

The direct field cost (DFC) factor is an uplift applied to the free on board (FOB) cost of the equipment andranges between 2.4 - 4.3 (with instrument) and 2 - 3.5(without instrument) for different equipment.

Guthrie introduced a module costing method as a type of EFE where the main relation is as per equation

3:

CBM = CPFBM (equation 3)

For other items the related relations are shown below:

DirectLabor CL = αL(CP + CM) = (1 + αM)αLCP Freight CFIT = αFIT(CP+CM) = (1 + αM)αFITCP

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The pressure correction factor (FP) is described in equation 9:

log10(FP) = C1 + C2 log10(P) + C3log10(P)2

The coefficients K1, K2, K3, C1, C2, C3 are given for different equipment.

(equation 9)

By totaling the above module cost for equipments, the total module cost can be obtained. To calculate thetotal plant cost one needs to add the auxiliary services and contingency costs, so 15 percent of themodule cost is considered for contingency,

3 percent for contractors, and 35 percent for auxiliary services.

Finally, the cost of a grass root plant can be calculated through equation 10:

CGR=1.18 CBM,i0 +0.35 CBM,i where CGR = grass roots cost

(equation 10)

The auxiliary services and utilities do not depend on the pressure or material of the battery limit andusually its cost is 35 percent of the module cost, at a base case of (CBM,i).

The capital cost, which includes all the capital, needed to ready a plant for startup is derived from:

• Direct project expenses include equipment FOB cost (CP), material (CM) required for installation,and labor (CL) to install that equipment and material.

• Indirect project expenses include freight, insurance, and taxes (CFIT), construction overhead (CO)and contractor engineering expenses (CE).

• Contingency and fees includes contractor fees (CFEE) and overall contingency (CCONT).• Auxiliary facilities includes site development (CSITE), auxiliary buildings (C AUX) and off sites and

utilities (COFF).

TOTAL CAPITAL INVESTMENT COST BREAKDOWN

Total bare-module cost equipment CFE Total bare-module cost machinery CPM Total bare-module cost spares CSPARE Total bare-module cost storage tanks CSTORAGE Total bare-module cost initial catalyst CCATAL __________Sums to total bare module investment CTBM Cost of site preparation CSITE Cost of service facilities (auxiliary buildings) C AUX Cost of utility plant and related facilities COFF __________Sums to cost of direct permanent investment CDPI

Cost of contingencies and contractors fees CCONT __________Sums to total depreciable capital CTDC Cost of land CLAND Cost of royalties CROYALTY Cost of plant startup CSTART __________Sums to total permanent investment CTPI Working capital CWC __________Sums to total capital investment CTCI

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CSITE = (0.10 - 0.20) CTBM FOR GRASS ROOTS, (0.04 - 0.06) CTBM FOR INTEGRATED COMPLEX C AUX = (0.1)CTBM FOR HOUSED OR INSIDE

Indirect on labor is based on U.S. Gulf Coast (USGC) as the suggested choice which is 115% to 180% ofdirect labor cost. All other locations are compared with the USGC to establish their indirect percentages.

A typical make-up for all indirect on labor is shown below:

Proposed RangesField Supervision & Field Office Expenses 25.0% to 41.0%Temporary Facilities & Structures(Includes Temporary Support Systems & Utilities)

9.0% to 18.0%

Construction Equipment & Tools 20.0% to 35.0%Construction Consumables & Small Tools 9.0% to 15.0%Statutory Burdens & Benefits 40.0% to 50.0%Misc. Overhead & Indirects 2.5% to 6.0%Profit/Fees for Construction Management 1.5% to 2.5%Mobilization/Demobilization 4.0% to 6.5%Scaffolding 4.0% to 6.0%

Total 115% to 180%

For international locations the field indirect and overheads (FIOH) percentage is identified through localcontacts or personal visits or through contacts with joint venture partners or from published informationfrom different sources. FIOH refers to a contractor’s construction costs necessary to support the directwork and is a function of the project’s planned duration of need, as extended by a definable estimatedrate per hour, together with an estimated cost associated with site mobilization/transport and finaldemobilization, relative size of project, type of project (grassroots or retrofit), local labor and constructionpractices, site specific location and conditions (such as extremely remote site requiring daily transport ofworkers to/from jobsite or special allowances for seasonal weather conditions). To compare the indirectcosts from different contractors, the multipliers should be on a similar basis and include field supervisionand indirect support staff, travel/relocation/subsistence, field per diems and relocation, temporary facilities

and structures, temporary support systems and utilities, construction equipment and tools, safety and firstaid, field office furnishings and supplies, communications, construction consumables, insurance/taxes,statutory payroll burdens and benefits, miscellaneous overhead and indirects (home office overheads,home office equipment, computers, purchasing services), and profit/fees. Statutory burdens shouldinclude social security, medical insurance, unemployment benefits, worker’s compensation insurance,general liability insurance, health and welfare, pension, education fund, industry fund, vacation, etc.

Temporary construction and consumables (TC&C) are the material, labor, and subcontract costsassociated with establishing and operating a temporary infrastructure to support construction work.Examples of TC&Cs include: temporary facilities (such as trailers and temporary buildings, field offices,furniture for temporary buildings, field shops including shop machinery, field warehouses, and workercamps, temporary roads, and fencing), scaffolding materials and labor, site clean-up, temporary utilitycosts, fuel, gas, welding rods, protective clothing and personal protective equipment, etc.

Field supervision/field office costs are the material, labor, and subcontract costs associated withsupervising the construction work. Examples of these costs include: wages, salaries, benefits, relocationcosts, travel expenses for assigned and local field staff (such as construction managers, superintendents,area supervisors, craft supervisors, warehouse supervisors, field project controls, trainers, fieldbuyers/expediters, safety officers, etc.), and ongoing expenses for a field office such as personalcomputers, telephone, fax machines, copiers, etc.

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Construction equipment/tools are material, labor, and subcontract costs necessary for providing tools andmachines to support the construction work. Examples include: cranes, trucks, welding machines, jackingequipment, small tools, rigging devices, etc.

The contractor engineering is based on total equipment items:

• Small projects: 650 to 950 work-hours per equipment item• Grassroots projects: 1,100 to 1,550 work-hours per equipment item• Retrofits: 30% to 45% of all direct costs (included in direct cost is equipment, material, and labor)

The individual item count includes all numbered equipment, any numbered spares, and the individuallynumbered pieces of equipment on a packaged unit.

A secondary check for grassroots projects for contractor engineering will be a cost range of 12% to 25%of all direct costs or 8% to 14% of total project costs. The owner engineering cost is estimated as 10% to12% of all direct costs or 25% to 45% of contractor engineering.

The contingency amount will vary based on the type of unit under consideration:• Well established process design (previously built): 5% to 10%

• Well established process designs, debottleneck type: 20% to 35%• Any OSBL unit: 25% to 40%• Brand new process design (never built before): 15% to 30%• DCS implementation, any unit: 10% to 15%.

The escalation for equipment, materials, and construction activities is based on the most currentconstruction cost index. The freight cost for a typical project is 2% to 6% of equipment cost. For overseaslocations, the freight cost varies from 8% to 18% of equipment cost, depending upon the country underconsideration. The spare parts (capital spares only) for US installations are 4% to 8% of equipment costs.The percentages are higher for overseas locations (8% to 12% of equipment cost) but should be lookedat on an individual basis.

PARAMETRIC COST ESTIMATES

Parametric cost estimates are used to estimate equipment cost and finally the total plant cost at anacceptable error percentage when there is little technical data about equipment and other capital costitems or engineering deliverables for submission to equipment manufacturers. It involves development ofparametric model based on data on equipment costs from specified time duration. Then, using statisticalmethods, the models coefficients are obtained and their accuracy and estimation capabilities are studied.The best reference for reliable cost data is the completed projects of an organization. Applying this data,using regression methods and statistical tests, a final model is proposed.

A parametric model is a mathematical representation of cost relationships that provide a logical andpredictable correlation between the physical or functional characteristics of a plant and its resultant cost.Capacity and equipment-factored estimates are simple parametric models. Sophisticated parametricmodels involve several independent variables or cost drivers.

The first step in developing a parametric model is to establish its scope. This includes defining the enduse, physical characteristics, critical components and cost drivers of the model taking into considerationthe type of process to be covered, the type of costs to be estimated (such as TIC and TFC) and theaccuracy range.

The model should be based on actual costs from completed projects and reflect the company’sengineering practices and technology. It should use key design parameters that can be defined withreasonable accuracy early in the project scope development and provide the capability for the estimator

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regression equations, test results and a discussion on how the data was adjusted or normalized for use inthe data analysis stage. Any assumptions and allowances designed into the cost model should bedocumented, as should any exclusion. The range of applicable input values and the limitations of themodel’s algorithms should also be noted. Write a user manual to show the steps involved in preparing anestimate using the cost model and to describe the required inputs to the cost model.

Induced-draft cooling towers are typically used in process plants to provide a recycle cooling-water loop.These units are generally prefabricated and installed on a subcontract or turnkey basis by the vendor.Key design parameters that appear to affect the costs of cooling towers are the cooling range, thetemperature approach and the water flow rate. The cooling range is the temperature difference betweenthe water entering the cooling tower and the water leaving it. The approach is the difference in the coldwater leaving the tower and the wet-bulb temperature of the ambient air.

CoolingRange, °F

Temperature Approach, °F

Flow Rate, gal/min Actual Cost, $ Predicted Cost, $ % Error

30 15 50,000 1,040,200 1,014,000 -2.5%30 15 40,000 787,100 843,000 7.1%40 15 50,000 1,129,550 1,173,000 3.8%

40 20 50,000 868,200 830,000 -4.4%25 10 30,000 926,400 914,000 -1.3%35 8 35,000 1,332,400 1,314,000 -1.4%

Table 7 – Actual Costs versus Predicted Costs with Parametric Equation

Table 7 provides the actual costs and design parameters of six recently completed units whose costshave been normalized (adjusted for location and time) to a Northeast US, year-2000 timeframe [6]. Thesedata are the input to a series of regression analyses that are run to determine an accurate algorithm forestimating costs. Using a computer spreadsheet, the cost estimation algorithm was developed as perequation 13:

Predicted Cost = $86,600 + $84,500(Cooling Range, °F)0.65 – $68,600(Approach, °F) +

+ $76,700(Flow Rate, 1,000 gal/min)

0.7

(equation 13)

The above equation demonstrates that the cooling range and flow rates affect cost in a nonlinear fashion,while the approach affects cost in a linear manner. Increasing the approach will result in a less costlycooling tower, since it increases the efficiency of the heat transfer-taking place. These are reasonableassumptions. The regression analysis resulted in an R

2 value of 0.96, which indicates that the equation is

a “good fit” for explaining the variability in the data. The percentage of error varies from –4.4 percent to7.1 percent. The estimating algorithm developed from regression analysis, can be used to develop costversus design parameters that can be represented graphically.

This information can then be used to prepare estimates for future cooling towers. It is fairly easy todevelop a spreadsheet model that will accept the design parameters as input variables, and calculate thecosts based on the parametric estimating algorithm.

To derive the models, one needs to suppose that a linear relationship exists between the cost of theequipment and its key parameters as per equation 14:

ln(CE) = A + Bln(KP) + Cln(KP)2

Where CE is equipment cost and KP is a key parameter. The models for other equipment are given inTable 8 calculated using the linear regression method along with the coefficients.

(equation 14)

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.Figure 2 – Graph developed from regression data for tower cost that can be used for futurecooling towers.

Equipment Proposed Models Parameter Ranges %AAD Coefficients

Pressure Vessels (Carbon Steel) CE = exp[A1 + B1ln(W) + C1ln(W)^2] 180 < W < 621,0002 < P < 20

21% A1 = -1.731737B1 = 0.5598C1 = 0.024773

Pressure Vessels (Stainless Steel) CE = exp[A2 + B2ln(W)] 168 < W < 108,8492 < P < 5

27.6% A2 = -2.788577B2 = 0.94935

Atmospheric Storage Tanks(Carbon Steel)

CE = exp[A3 + B3ln(W)] 2,800 < W < 1,540,000 4.2% A3 = -4.619487B3 = 0.9892

Separation Tower (Carbon Steel) CE = exp[A4 + B4ln(W) + C4ln(W)^2] 5,360 < W < 178,0003.5 < P < 30

12.8% A4 = 13.271536B4 = -2.253712C4 = 0.154118

Separation Tower (Stainless Steel) CE = exp[A5 + B5ln(W) + C5(L/D)] 6,400 < W < 39,0001.4 < (L/D) < 21.3

3.5 < P < 37

37% A5 = -2.484312B5 = 0.964302C5 = 0.04109

Shell and Tube Heat Exchangers –BEU Type (Carbon Steel)

CE = exp[A6 + B6ln(W)] 4,400 < W < 77,4007 < P < 85

3.2% A6 = -2.910474B6 = 1.016550

Oil Injected Screw Compressor CE = exp[A7 + B7WP + C7WP^0.5] 7 < WP < 3157 < P < 85

9.2% A7 = 2.193159320B7 = -0.01059287C7 = 0.450875824

Where: W(weight, kg), P(operating pressure, bar), L(length, m), D(diameter, m), CE(equipment cost, Millions Iranian Rials),

WP(power, kW). Note: The above costs are related to year 2004 in the Iranian market. Table 8 – Obtained models for some equipment

The parametric models for the above equipment were prepared using a provided data bank including thecost and some specifications of equipment. Because of limitations, both in the number of projects and inthe type of equipment, the defined models are in specified limited domains. To increase these domains,additional cost data in broader ranges are needed.

To increase these domains, additional cost data in broader ranges are needed. The achieved results canbe used as initial data to develop more complete models.

In the above table, the obtained models are shown, as well as the applicable ranges and absolute

average deviation percentages, which are listed as %AAD. The %AAD can be defined as per equation15:

%AAD = 100x 1n ABSY − YY

where Y is the estimated value and Y i is the cost value from data bank and n is the number of data.(equation 15)

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For example, the cost of a BEU type heat exchanger (carbon steel) with a weight of 10,000 kg can becalculated as:

CE = exp(-2.910474 + 1.01655 ln(10000)) = 631.15 MRls = $71253.60

(For exchange rate in 2004 use: 8900 Rials = 1 $)

The confidence interval method also provides a means of quantifying uncertainty. For each coefficient (Bi)is as per equation 16:

Bi = B ± tSE

where B is estimated coefficients, t is t-student from the distribution table and depends on degree offreedom and statistical significance. SE is the standard error for coefficients.

(equation 16)

The confidence interval was determined at 95 percent statistical significance for coefficients of the six firstmodels and 90 percent statistical significance for the last model for a compressor.

Table 9 shows the related confidence intervals and standard errors for coefficients in the proposedmodels. Since none of the intervals straddle zero, then none of the coefficients are zero, and therefore,they are acceptable.

The goodness of fit is explained by R-square in regression. R 2 = 1 is a perfect score. R2 = 0.99 is a verygood score that shows the goodness of fit.

Equipment Coefficients t StandardError

Confidence Interval R2

Pressure Vessels (Carbon Steel) B1 = 0.5598C1 = 0.024773

1.981.98

0.1352540.007028

0.292005 < B1 < 0.8275950.0108576 < C1 < 0.0386884

0.990.99

Pressure Vessels (Stainless Steel) B2 = 0.94935 2.074 0.035066 0.876623 < B2 < 1.022077 0.98 Atmospheric Storage Tanks (Carbon Steel) B3 = 0.9892 2.074 0.007508 0.97362 < B3 < 1.00477 0.99Separation Tower (Carbon Steel) B4 = -2.253712

C4 = 0.154118

2.179

2.179

0.696337

0.032736

-3.77103 < B4 < -0.73639

0.082786 < C4 < 0.22545

0.99

0.99Separation Tower (Stainless Steel) B5 = 0.964302

C5 = 0.041092.7762.776

0.0116740.001323

0.640231 < B5 < 1.2883720.0004363 < C5 < 0.0077816

0.990.99

Shell and Tube Heat Exchangers – BEUType (Carbon Steel)

B6 = 1.016550 2.131 0.006779 1.002104 < B6 < 1.030996 0.99

Oil Injected Screw Compressor B7 = -0.01059287C7 = 0.450875824

1.6971.697

0.0009390.025139

-0.00624 < B7 < -0.008990.0024 < C7 < 0.087

0.990.99

Table 9 – Confidence Intervals

Cost estimation accuracy by parametric models in the feasibility study stages ranges between 20 to 50percent (upper limit) and -15 to -30 percent (lower limit). These models can be accepted with accuracyranges between ±3 percent to ±37 percent. The obtained models are related to a specific year. Becauseof inflation, they must be re-evaluated for use in following years.

ACCURACY OF FACTORED ESTIMATE

There are different kinds of cost estimates prepared in the conceptual arena depending on their purposeor the amount of time and information available with an accuracy of plus or minus X %, implying that thetrue value lies between (100 + X)% and (100 - X)%. However, that range is biased, because the largestpossible positive deviation theoretically approaches infinity whereas the largest possible negativedeviation is only 100%. So, a value of (100 - X) is a more significant departure from X than is the value(100 + X).

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In line with this logic, the listing of cost estimates classes sanctioned by AACE[1] typically uses rangeswith the positive deviation being larger than the negative:

PrimaryCharacteristic

Secondary Characteristic

ESTIMATECLASS

DEGREE OFPROJECT

DEFINITIONExpressed as % ofcomplete definition

END USAGETypical purpose of

estimate

METHODOLOGYTypical estimating method

EXPECTEDACCURACY RANGETypical variation in low and

high ranges[a]

Class 5 0% to 2%Concept

screening

Capacity factored,parametric models,

judgment, or analogy

L: -20% to -50%H: +30% to +100%

Class 4 1% to 15%Study orfeasibility

Equipment factored orparametric models

L: -15% to -30%H: +20% to +50%

Class 3 10% to 40%Budget

authorization or

control

Semi-detailed unit costswith assembly level line

items

L: -10% to -20%H: +10% to +30%

Class 2 30% to 70%Control orbid/tender

Detailed unit cost withforced detailed take-off

L: -5% to -15%H: +5% to +20%

Class 1 70% to 100%Check estimate

or bid/tenderDetailed unit cost with

detailed take-offL: -3% to -10%H: +3% to +15%

Notes: [a] The state of process technology and availability of applicable reference cost data affect the range markedly.The +/- value represents typical percentage variation of actual costs from the cost estimate after application ofcontingency (typically at a 50% level of confidence) for given scope.

Table 1 – Cost Estimate Classification Matrix for Process Industries[1]

It is important to understand how uncertainties propagate in cost estimates involving the four arithmeticmanipulations (being the sum of multiplicative products or requiring subtraction and division during itscalculation) since the values of the quantities, unit costs and other numbers being thus manipulatedtypically are uncertain.

Consider an estimate to be a summation of elements with each element being the product of twovariables or factors: a) Quantity Factor: the number of units - individual pieces as reactors, areas assurfaces to be insulated, volumes as cubic meters of concrete to be poured or other units that enumeratethe entity being priced, and b) Cost Factor: the corresponding unit cost. When two or more independentvariables A and B are multiplied together, any inaccuracies in the individual variables are amplified in theirproduct:

(A ± a)(B ± b) = AB ± (A2b

2 + B

2a

2)1/2

If a is a symmetric accuracy range for A, and b is a symmetric accuracy range for B

For instance, consider a cost estimate element consisting of a tank. Its required volume A is expected tobe 21,000 gal with an uncertainty of ± 20%, and its anticipated unit capital cost B is $2/gal with anuncertainty of ± 30%. Thus, a equals (21,000)(0.20) or 4,200, and b is (2)(0.30) or $0.60. Then theirproduct P becomes: P = (21,000)(2.00) ± (21,0002 x 0.602 + 22 x 4,2002)1/2 = 42,000 ± 15,143, or ± 36.1%between the percent ranges corresponding to the product of two independent variables, each having itsown accuracy range. The range of the product is at the intersection of the row and column appropriate forthe two variables. The ± 20% quantity factor would be accurate enough for budgeting purposes under theaforementioned conventional listing, and the ± 30% cost factor would qualify for study or factoredestimates, but their product qualifies only for use as a conventional order-of-magnitude or conceptual

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estimate. If the quantity and cost factors each were instead ± 50% accurate, their product would be ±70.7%, unacceptable even for order-of-magnitude purposes.

Division has the same effect as multiplication, increasing the range of inaccuracy whereby the product orquotient is less accurate than the more uncertain of the two factors involved.

When two or more independent variables are added, any inaccuracies in the individual variables aredecreased in their sums. The expression for two numbers A and B having symmetric accuracy ranges aand b is:

(A ± a) + (B ± b) = A + B ± (a2 + b2)1/2

Consider, for instance, summing the costs of 10-in. and 8-in. flanges, respectively costing $120 with anaccuracy of 10% and $80 with an accuracy of ± 10%. Then a = (120)(0.10) = $12, and b = (80)(0.20) =$16, and their sum S becomes: S = (120 + 80) ± (122 + 162)1/2 = 200 ± 20 = 200 ± 10%

The expected error range of the total will be less than the error in either of the individual numbers or, atmost, equal to the lower of them.

This decrease in accuracies is not limited to the summation of two variables. The inaccuracies of theseven cost-estimating elements such as list of process equipment that is needed for a distillation unitbecome far less significant when the associated costs are summed. This demonstrates that the moredetail in which we define the scope of our project, the more accurate our estimate becomes.

In subtraction, the same formula is used as for addition. The expected absolute range is the same aswhen adding, but the percentage range is much greater. Consider again the two flanges mentionedabove and take the difference D in their costs: D = (120 - 80) ± (122 + 162)1/2 = 40 ± 20, or ± 50%

These uncertainty-propagation rules have significant implications for the accuracies that we can expectfrom any given estimating method.

CONCLUSION

Factored cost estimation is proposed as sample methods to organizations and engineering companies toderive their own cost relations by referring to their past project cost archives. When deciding uponpotential investment opportunities, management must employ a cost screening process that requiresvarious estimates to support key decision points. At each of these points, the level of engineering andtechnical information needed to prepare the estimate will change. Accordingly, the techniques usedprepare the estimates will vary depending upon the information available at the time of preparation, theend use of the estimate, and its desired accuracy. The challenge for the engineer is to know what isneeded to prepare these estimates, and to ensure they are well documented, consistent, reliable,accurate and supportive of the decision-making process.

REFERENCES

1. AACE International Recommended Practice No. 18R-97, Cost Estimate Classification System – As Applied in Engineering, Procurement, and Construction for the Process Industries, AACEInternational, Morgantown, WV, (latest revision)

2. Black, Dr. J. H., “Application of Parametric Estimating to Cost Engineering”, 1984 AACETransactions, AACE International, 1984

3. Mohammed Reza Shabani and Reza Behradi Yekta, “ Chemical Processes Equipment CostEstimation Using Parametric Models”, AACE International, May 2006

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4. Chilton, C. H., “Six Tenths Factor Applies to Complete Plant Costs”, Chemical Engineering, April1950

5. Dysert, L. R., “Developing a Parametric Model for Estimating Process Control Costs”, 1999 AACETransactions, AACE International, 1999

6. Dysert, L. R., “Sharpen Your Cost Estimating Skills”, Cost Engineering, Vol. 45, No.6, AACE

International , Morgantown, WV, 20037. Guthrie, K. M., “Data and Techniques for Preliminary Capital Cost Estimating”, Chemical Engineering,

March 19698. Guthrie, K.M., Capital and Operating Costs for 54 Chemical Processes, Chem. Eng., June 1970.9. Mohammed Reza Shabani and Reza Behradi Yekta, “ Suitable Method for Capital cost estimation in

Chemical Process Industries”, AACE International, May 200610. Loh, H.P., Jennifer Lyons, and Charles W. White III, Process Equipment Cost Estimation Final

Report, DOE/NETL-2002/1169, U.S. Department of Energy/National Energy Technology Laboratory,January 2002

11. Hand, W. E., “Estimating Capital Costs from Process Flow Sheets”, Cost Engineer’s Notebook, AACEInternational, January 1964

12. Lang, H. J., “Cost Relationships in Preliminary Cost Estimation,” Chemical Engineering, October 194713. Lang, H. J., “Simplified Approach to Preliminary Cost Estimates,” Chemical Engineering, June 1948

14. Miller, C. A., “New Cost Factors Give Quick Accurate Estimates,” Chemical Engineering, September196515. Miller, C. A., “Capital Cost Estimating – A Science Rather than an Art,” Cost Engineer’s Notebook,

AACE International, 197816. NASA, Parametric Cost Estimating Handbook17. Nishimura, M., “Composite-Factored Estimating”, 1995 AACE Transactions, AACE International,

199518. Remer, D. and L. Chai, “Estimate Costs of Scaled-Up Process Plants”, Chemical Engineering, April

199019. Gustav Enyedy, “How Accurate is Your Estimate”, Chemical Engineering20. Rodl, Dr. R. H. and Dr. P. Prinzing and D. Aichert, “Cost Estimating for Chemical Plants”, 1985 AACE

Transactions, AACE International, 198521. Rose, A., “An Organized Approach to Parametric Estimating”, Transactions of the Seventh

International Cost Engineering Congress, 198222. Williams Jr., R., “Six-Tenths Factor Aids in Approximating Costs,” Chemical Engineering, December

194723. Lang, H. J., Engineering approach to preliminary cost estimates, Chemical Engineering, September

1947, pp. 130-133.24. Hand, W. E., From Flow sheet to Cost Estimate, Petroleum Refiner, September 1958, pp. 331-334.25. Hackney, J. W., ``Control and Management of Capital Projects,'' Wiley, New York, 1965.26. Hackney, J.W., Estimating methods for process industry capital costs, Chemical Engineering, April 4,

1960, pp. 119-134.27. Guthrie, K. M., ``Process Plant Estimating Evaluation and Control,'' Craftsman, Saline Beach, Calif,

1974.28. Happel, J. and D.G. Jordan, Chemical Process Economics, 2

nd Ed., Marcel Dekker, New York, NY,

1975

CONTRIBUTORS

Rashmi Prasad (Author)Kul B. Uppal, PE CEP

A. Larry Aaron, CCE CEP PSPPeter R Bredehoeft Jr., CEPLarry R. Dysert, CCC CEPJames D. Whiteside II, PE

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