From: Beth Havlik

To: Aagenes, Jason D.

Cc: George Pruchnofski; Erik Boleman

Subject: RE: Dick Cordes SO2 analysis estimate, contingency, and downtime costs

Date: Friday, June 29, 2007 7:06:34 PM

Attachments: AACE Cost Estimate Classification.pdf

We’ll talk more next week, but I wanted to provide an update on the issues we discussed relative to the

BART.

1. Comment on Dick’s cost estimate

This is the same info Dick had given me. But it’s only the input cost information. He verbally said

those were the main changes, but implied there might be other differences. For comparison, I plugged

those input numbers into the Utac spreadsheet, and the resulting cost per ton for Line 2 is $2839.

That is pretty close to the number Dick reported. So that may be the extent of Dick’s calculation.

The OAQPS Manual states that the purpose for providing default values is to allow facilities to prepare

an estimate even if site-specific information is unavailable or time-consuming to collect. It

acknowledges that site-specific costs will “improve the accuracy of the analysis.” 

[1]

OAQPS Manual,

page 2-31.

So Dick’s approach of using a so-called “common value” is not valid and actually degrades the

accuracy of the estimate.

2. Contingency:

The contingency value Barr used, 3% of purchased equipment, was based on long-standing agency

guidance for all BACT projects. They have been fairly insistent about not using different contingencies

from project to project so that BACT choices are made on a level playing field. Since this is BART, not

BACT, we may be able to argue for more latitude. Nonetheless, I looked at what value the CUECost

program [Coal Utility Environmental Cost] uses, and it consistently uses a contingency of 20% of

installed cost. CUECost was developed for EPA for the power industry, and would seem to be good

support for a higher number than 3 to 5%. I agree we can use Utac’s 30% contingency as a sitespecific number based on your experience, but I think it’s worth citing the EPA for the 20% used in

CUECost, as well as the AACE. See also I attached the AACE Recommended Practice for Cost

Estimate Classification that also provides support.

3. Downtime cost

I spoke to our Hibbing engineers who have installed retrofit set scrubbers in the taconite industry

(Harry Larson and Cliff Rasch). The shortest construction time-frame would be 8 to 9 months, and this

was for a fast-track project. So at a million per day that’s at least 24 million dollars.

4. Water treatment

I haven’t made progress on this, but I did verify that some of our water people will be working on next

week and should be able to help us develop a cost estimate for water treatment. Stay tuned on this

issue.

Beth T. Havlik, P.E.

Vice President, Barr Engineering

4700 West 77th Street

Minneapolis, MN 55435-4803

800/632-2277 toll free

952/832-2640 phone

952/832-2601 fax

734/255-0269 mobile 
aac.e 

I nternat1ona/ 

AACE internationai Recommended Praciice No. 17R-97 

COST ESTIMATE CLASSIFICATION SYSTEM 

Acknowledgment: 

Peter Christensen CCE, Primary Author 

Larry R. Dysert CCC, Primary Author 

Jennifer Bates CCE 

Dorothy J. Burton 

Robert C. Creese PE CCE 

John K. Hollmann PE CCE 

Copyright 2003 AACE, Inc. 

Kenneth K. Humphreys PE CCE 

Donald F. McDonald JR. PE CCE 

C. Arthur Miller 

Bernard A. Pietlock CCC 

Wesley R. Querns CCE 

Don L. Short II 

AACE International Recommended Practices 
aace, lnternatwna 

\• ---.. ••• <} 

~~ 1"o..,_CoST~ 

Recommended Practice No. 17R-97 

Cost Estimate Classification System 

August 12, 1997 

As a recommended practice of AACE International, the Cost Estimate Classification System provides 

guidelines for applying the general principles of estimate classification to asset project cost estimates. 

Asset project cost estimates typically involve estimates for capital investment, and exclude operating and 

life-cycle evaluations. The Cost Estimate Classification System maps the phases and stages of asset cost 

estimating together with a generic maturity and quality matrix that can be applied across a wide variety of 

industries. 

This guideline and its addenda have been developed in a way that: 

• provides common understanding of the concepts involved with classifying project cost estimates, 

regardless of the type of enterprise or industry the estimates relate to; 

• fully defines and correlates the major characteristics used in classifying cost estimates so that 

enterprises may unambiguously determine how their practices compare to the guidelines; 

• uses degree of project definition as the primary characteristic to categorize estimate classes; and 

• reflects generally-accepted practices in the cost engineering profession. 

An intent of the guidelines is to improve communication among all of the stakeholders involved with 

preparing, evaluating, and using project cost estimates. The various parties that use project 

cost estimates often misinterpret the quality and value of the information available to prepare cost 

estimates, the various methods employed during the estimating process, the accuracy level expected 

from estimates, and the level of risk associated with estimates. 

This classification guideline is intended to help those involved with project estimates to avoid 

misinterpretation of the various classes of cost estimates and to avoid their misapplication and 

misrepresentation. Improving communications about estimate classifications reduces business costs and 

project cycle times by avoiding inappropriate business and financial decisions, actions, delays, or 

disputes caused by misunderstandings of cost estimates and what they are expected to represent. 

This document is intended to provide a guideline, not a standard. It is understood that each enterprise 

may have its own project and estimating processes and terminology, and may classify estimates in 

particular ways. This guideline provides a generic and generally-acceptable classification system that can 

be used as a basis to compare against. If an enterprise or organization has not yet formally documented 

its own estimate classification scheme, then this guideline may provide an acceptable starting point. 

An AACE International guideline for cost estimate classification for the process industries was 

developed in the late 1960s or early 1970s, and a simplified version was adopted as an ANSI 

Standard Z94.0 in 1972. Those guidelines and standards enjoy reasonably broad acceptance within the 

engineering and construction communities and within the process industries. This 

recommended practice guide and its addenda improves upon these standards by: 

1. providing a classification method applicable across all industries; and 

2. unambiguously identifying, cross-referencing, benchmarking, and empirically evaluating the multiple 

characteristics related to the class of cost estimate. 

This guideline is intended to provide a generic methodology for the classification of project cost 

estimates in any industry, and will be supplemented with addenda that will provide extensions and 

additional detail for specific industries. 

Copyright2003AACE, Inc. AACE International Recommended Practices 
Cost Estimate Classification System 2 of6 

aace 1-------------------------------------------- lntern••w•• August 12, 1997 

There are numerous characteristics that can be used to categorize cost estimate types. The most 

significant of these are degree of project definition, end usage of the estimate, estimating methodology, 

and the effort and time needed to prepare the estimate. The "primary" characteristic used in this guideline 

to define the classification category is the degree of project definition. The other characteristics are 

"secondary." 

Categorizing cost estimates by degree of project definition is in keeping with the AACE International 

philosophy of Total Cost Management, which is a quality-driven process applied during the entire project 

. life cycle. The discrete levels of project definition used for classifying estimates correspond to the typical 

phases and gates of evaluation, authorization, and execution often used by project stakeholders during a 

project life cycle. 

Five cost estimate classes have been established. While the level of project definition is a continuous 

spectrum, it was determined from benchmarking industry practices that three to five discrete categories 

are commonly used. Five categories are established in this guideline as it is easier to simplify by 

combining categories than it is to arbitrarily split a standard. 

The estimate class designations are labeled Class 1, 2, 3, 4, and 5. A Class 5 estimate is based upon 

the lowest level of project definition, and a Class 1 estimate is closest to full project definition and 

maturity. This arbitrary "countdown" approach considers that estimating is a process whereby successive 

estimates are prepared until a final estimate closes the process. 

Primary 

Characteristic 

LEVEL OF 

PROJECT 

DEFINITION ESTIMATE Expressed as % CLASS complete definiti 

Class 5 O%to'2% 

Class 4 1% to--15% 

Class 3 1D%-t0:4o% 

Class 2 3d%'to70% 

Class 1 50% to 100% 

END USAGE 

Typical purpose 

of of estimate 

n 

Screening or 

Feasibility 

Concept Study or 

Feasibility 

Budget, 

Authorization, or 

Control 

Control or Bid/ 

Tender 

Check Estimate or 

Bidffender 

Secondary Characteristic 

EXPECTED • PREPARATION 

METHODOLOGY ACCURACY EFFORT 

Typical estimating RANGE Typical _Ctegi"E7e 

method Typical +/- range :of effortielative 

relative to best tO: least cost 

index of 1 [a] iiidex,oO [bJ 

Sto'chastic or 4to20 Judgment 

Primarily 3 to 12 StOchastiC 

Mixed, I)Ut 

Prirri~rily 2to6 

Sto(:hastic 

P,rimarily 1 to 3 ~ter:ministic 

Deterministic 10 to 100 

Notes: [a] If the range index value of "1" represents +1 0/-5%, then an index value of 10 represents +100/-50%. 

[b] If the cost index value of "1" represents 0.005% of project costs, then an index value of 100 represents 0.5%. 

Figure 1 - Generic Cost Estimate Classification Matrix 

Copyright 2003 AACE, Inc. AACE International Recommended Practices 
Cost Estimate Classification System 3 of6 

aace 1--------------------------------------------- lntematiOna August 12, 1997 

The following are brief discussions of the various estimate characteristics used in the estimate 

classification matrix. For the secondary characteristics, the overall trend of how each characteristic varies 

with the degree of project definition (the primary characteristic) is provided. 

Level of Project Definition (Primary Characteristic) 

This characteristic is based upon percent complete of project definition (roughly corresponding to 

percent complete of engineering). The level of project definition defines maturity or the extent and types 

of input information available to the estimating process. Such inputs include project scope definition, 

requirements documents, specifications, project plans, drawings, calculations, learnings from past 

projects, reconnaissance data, and other information that must be developed to define the project. Each 

industry will have a typical set of deliverables that are used to support the type of estimates used in that 

industry. The set of deliverables becomes more definitive and complete as the level of project definition 

(i.e., project engineering) progresses. 

End Usage (Secondary Characteristic) 

The various classes (or phases) of cost estimates prepared for a project typically have different end 

uses or purposes. As the level of project definition increases, the end usage of an estimate 

typically progresses from strategic evaluation and feasibility studies to funding authorization and budgets 

to project control purposes. 

Estimating Methodology (Secondary Characteristic) 

Estimating methodologies fall into two broad categories: stochastic and deterministic. In stochastic 

methods, the independent variable(s) used in the cost estimating algorithms are generally something 

other than a direct measure of the units of the item being estimated. The cost estimating relationships 

used in stochastic methods often are somewhat subject to conjecture. With deterministic methods, the 

independent variable(s) are more or less a definitive measure of the item being estimated. A deterministic 

methodology is not subject to significant conjecture. As the level of project definition increases, the 

estimating methodology tends to progress from stochastic to deterministic methods. 

Expected Accuracy Range (Secondary Characteristic) 

Estimate accuracy range is in indication of the degree to which the final cost outcome for a given 

project will vary from the estimated cost. Accuracy is traditionally expressed as a +/- percentage range 

around the point estimate after application of contingency, with a stated level of confidence that the actual 

cost outcome would fall within this range (+/- measures are a useful simplification, given that actual cost 

outcomes have different frequency distributions for different types of projects). As the level of project 

definition increases, the expected accuracy of the estimate tends to improve, as indicated by a tighter+/-

range. 

Note that in figure 1, the values in the accuracy range column do not represent + or- percentages, 

but instead represent an index value relative to a best range index value of 1. If, for a particular industry, 

a Class 1 estimate has an accuracy range of +1 0/-5 percent, then a Class 5 estimate in that same 

industry may have an accuracy range of +100/-50 percent. 

Effort to Prepare Estimate (Secondary Characteristic) 

The level of effort needed to prepare a given estimate is an indication of the cost, time, and resources 

required. The cost measure of that effort is typically expressed as a percentage of the total project costs 

for a given project size. As the level of project definition increases, the amount of effort to prepare an 

estimate increases, as does its cost relative to the total project cost. The effort to develop the project 

deliverables is not included in the effort metrics; they only cover the cost to prepare the cost estimate 

itself. 

Copyright 2003 AACE, Inc. AACE International Recommended Practices 
Cost Estimate Classification System 4 of6 

aace 1--------------------------------------------- lnf•rn•f10"• August 12, 1997 

:-~-' ·''""<>'' ·;:;'"';,'';y ';"J"· '' 

<<-' >'1-1>;::~+>>"Y' ,,,, , ':,···::: :>::iC:<T' 

There are a myriad of complex relationships that may be exhibited among the estimate characteristics 

within the estimate classifications. The overall trend of how the secondary characteristics vary with the 

level of project definition was provided above. This section explores those trends in more detail. Typically, 

there are commonalties in the secondary characteristics between one estimate and the next, but in any 

given situation there may be wide variations in usage, methodology, accuracy, and effort. 

The level of project definition is the "driver" of the other characteristics. Typically, all of the secondary 

characteristics have the level of project definition as a primary determinant. While the other characteristics 

are important to categorization, they lack complete consensus. For example, one estimator's "bid" might 

be another's "budget." Characteristics such as "accuracy" and "methodology" can vary markedly from 

one industry to another, and even from estimator to estimator within a given industry. 

Level of Project Definition 

Each project (or industry grouping) will have a typical set of deliverables that are used to support a 

given class of estimate. The availability of these deliverables is directly related to the level of project 

definition achieved. The variations in the deliverables required for an estimate are too broad to cover in 

detail here; however, it is important to understand what drives the variations. Each industry group tends to 

focus on a defining project element that "drives" the estimate maturity level. For instance, chemical 

industry projects are "process equipment-centric"-i.e., the level of project definition and subsequent 

estimate maturity level is significantly determined by how well the equipment is defined. Architectural 

projects tend to be "structure-centric," software projects tend to be "function-centric," and so on. 

Understanding these drivers puts the differences that may appear in the more detailed industry addenda 

into perspective. 

End Usage 

While there are common end usages of an estimate among different stakeholders, usage is often 

relative to the stakeholder's identity. For instance, an owner company may use a given class 

of estimate to support project funding, while a contractor may use the same class of estimate to support a 

contract bid or tender. It is not at all uncommon to find stakeholders categorizing their estimates by 

usage-related headings such as "budget," "study," or "bid." Depending on the stakeholder's perspective 

and needs, it is important to understand that these may actually be all the same class of estimate (based 

on the primary characteristic of level of project definition achieved). 

Estimating Methodology 

As stated previously, estimating methodologies fall into two broad categories: stochastic and 

deterministic. These broad categories encompass scores of individual methodologies. Stochastic 

methods often involve simple or complex modeling based on inferred or statistical relationships between 

costs and programmatic and/or technical parameters. Deterministic methods tend to be straightforward 

counts or measures of units of items multiplied by known unit costs or factors. It is important to realize 

that any combination of methods may be found in any given class of estimate. For example, if a 

stochastic method is known to be suitably accurate, it may be used in place of a deterministic method 

even when there is sufficient input information based on the level of project definition to support a 

deterministic method. This may be due to the lower level of effort required to prepare an estimate using 

stochastic methods. 

Expected Accuracy Range 

The accuracy range of an estimate is dependent upon a number of characteristics of the estimate 

input information and the estimating process. The extent and the maturity of the input information as 

measured by percentage completion (and related to level of project definition) is a highly-important 

determinant of accuracy. However, there are factors besides the available input information that also 

greatly affect estimate accuracy measures. Primary among these are the state of technology in the 

project and the quality of reference cost estimating data. 

Copyright 2003 AACE, Inc. AACE International Recommended Practices 
Cost Estimate Classification System 5of6 

m~r.~.,-------------------------------------------------------------------- August 12, 1997 

State of technology-technology varies considerably between industries, and thus affects estimate 

accuracy. The state of technology used here refers primarily to the programmatic or technical uniqueness 

and complexity of the project. Procedurally, having "full extent and maturity" in the estimate basis 

deliverables is deceptive if the deliverables are based upon assumptions regarding uncertain technology. 

For a "first-of-a-kind" project there is a lower level of confidence that the execution of the project will be 

successful (all else being equal). There is generally a higher confidence for projects that repeat past 

practices. Projects for which research and development are still under way at the time that the estimate is 

prepared are particularly subject to low accuracy expectations. The state of technology may have an 

order of magnitude (1 0 to 1) effect on the accuracy range. 

Quality of reference cost estimating data-accuracy is also dependent on the quality of reference cost 

data and history. It is possible to have a project with "common practice" in technology, but with little cost 

history available concerning projects using that technology. In addition, the estimating process typically 

employs a number of factors to adjust for market conditions, project location, environmental 

considerations, and other estimate-specific conditions that are often uncertain and difficult to assess. The 

accuracy of the estimate will be better when verified empirical data and statistics are employed as a basis 

for the estimating process, rather than assumptions. 

In summary, estimate accuracy will generally be correlated with estimate classification (and therefore 

the level of project definition), all else being equal. However, specific accuracy ranges will typically vary 

by industry. Also, the accuracy of any given estimate is not fixed or determined by its classification 

category. Significant variations in accuracy from estimate to estimate are possible if any of the 

determinants of accuracy, such as technology, quality of reference cost data, quality of the estimating 

process, and skill and knowledge of the estimator vary. Accuracy is also not necessarily determined by 

the methodology used or the effort expended. Estimate accuracy must be evaluated on an estimate-byestimate basis, usually in conjunction with some form of risk analysis process. 

Effort to Prepare Estimate 

The effort to prepare an estimate is usually determined by the extent of the input information 

available. The effort will normally increase as the number and complexity of the project definition 

deliverables that are produced and assessed increase. However, with an efficient estimating methodology 

on repetitive projects, this relationship may be less defined. For instance, there are combination 

design/estimating tools in the process industries that can often automate much of the design and 

estimating process. These tools can often generate Class 3 deliverables and estimates from the most 

basic input parameters for repetitive-type projects. There may be similar tools in other industry groupings. 

It also should be noted that the estimate preparation costs as a percentage of total project costs will 

vary inversely with project size in a nonlinear fashion. For a given class of estimate, the preparation cost 

percentage will decrease as the total project costs increase. Also, at each class of estimate, the 

preparation costs in different industries will vary markedly. Metrics of estimate preparation costs normally 

exclude the effort to prepare the defining project deliverables. 

The five estimate classes are presented in figure 1 in relationship to the identified characteristics. 

Only the level of project definition determines the estimate class. The other four characteristics are 

secondary characteristics that are generally correlated with the level of project definition, as discussed 

above. 

This generic matrix and guideline provide a high-level estimate classification system that is 

non industry specific. Refer to subsequent addenda for further guidelines that will provide more detailed 

information for application in specific industries. These will provide additional information, such as input 

deliverable checklists, to allow meaningful categorization in that industry. 

Copyright 2003 AACE, Inc. AACE International Recommended Practices 
Cost Estimate Classification System 6 of6 

ln~~~al------------------------------------------------------------------------- August 12, 1997 

ANSI Standard Z94.2-1989. Industrial Engineering Terminology: Cost Engineering. 

Copyright 2003 AACE, Inc. AACE International Recommended Practices 
bhavlik@barr.com

From: Aagenes, Jason D. [mailto:JDAagenes@cleveland-cliffs.com] 

Sent: Friday, June 29, 2007 3:36 PM

To: Beth Havlik; George Pruchnofski

Cc: Paul Taylor; Erik Boleman

Subject: FW: DRAFT STS report

FYI.. re Dick Cordes SO2 analysis estimate...

From: Fenske, Mary Jean [mailto:Mary.Jean.Fenske@state.mn.us] 

Sent: Friday, June 29, 2007 12:08 PM

To: Aagenes, Jason D.

Cc: Jackson, Anne; Cordes, Richard

Subject: RE: DRAFT STS report

Jason,

After correcting your original submitttal for the double counting of the 15% scale up factor, Dick simply

used "common values" from the attached spreadsheet for his analysis of the SO2 control cost

estimates submitted in your BART analysis. This exercise was a check on the influence of the disparity

in some of the operating costs between Utac and the other facilties.

Please contact Dick or myself if you have questions.

Mary Jean

-----Original Message-----

From: Aagenes, Jason D. [mailto:JDAagenes@cleveland-cliffs.com] 

Sent: Friday, June 29, 2007 8:42 AM

To: Fenske, Mary Jean

Cc: Jackson, Anne

Subject: RE: DRAFT STS report

Mary Jean,

Could you forward me Dick Cordes’ spreadsheet (analysis and assumptions) on the MPCA SO2

removal cost/ton summary? I’d like to better understand the differences between MPCA’s, Barr’s and

STS’s cost estimating.

Thank you,

Jason Aagenes

From: Fenske, Mary Jean [mailto:Mary.Jean.Fenske@state.mn.us] 

Sent: Friday, June 29, 2007 8:10 AM

To: Aagenes, Jason D.

Cc: Jackson, Anne

Subject: RE: DRAFT STS report

Thanks, Jason. I will pass these comments along to Hal for his thoughts and make the request that

he provide the justification for the retrofit multiplier used as well as the the % contingency.
Mary Jean

-----Original Message-----

From: Aagenes, Jason D. [mailto:JDAagenes@cleveland-cliffs.com] 

Sent: Thursday, June 28, 2007 4:09 PM

To: Fenske, Mary Jean

Cc: Skolasinski, Dave Z.; Roth, Todd D.

Subject: RE: DRAFT STS report

Importance: High

Mary Jean:

Two other quick comments on the STS report:

1) The 60% retrofit multiplier should continue to be used. It is a standard factor that is

referenced in EPA guidance documents for such projects.

2) I noticed that only a 5% contingency was applied to project. For a capital project of this scale

and at this preliminary cost estimating stage (pre-engineering), a 30% contingency is probably

a more appropriate value that should be used. Please let me know if you would like me to

provide documentation on the source of this guidance.

Regards,

Jason Aagenes

From: Aagenes, Jason D. 

Sent: Wednesday, June 27, 2007 4:18 PM

To: 'Fenske, Mary Jean'

Cc: Skolasinski, Dave Z.; Roth, Todd D.

Subject: RE: DRAFT STS report

Mary Jean,

Due to the quick review that was requested, please accept this informal email as United Taconite’s

comments on the draft Line 2 SOx Removal Cost Study that was prepared by STS. The following

items should be included in the review, if not please explain why they are omitted:

1) Since the proposed project is a major modification to the process and building, significant

process downtime would be anticipated. Please include the economic cost of lost production

on Line 2 during construction.

2) Verify that complete water treatment costs for removal of fluorides, sulfates, etc. are included

(capital, O&M) for blowdown water. I’m concerned that, over time, untreated blowdown water

reporting to the tailings basin would significantly deteriorate our process water quality.

3) Verify that proper sized process air fans are included in the design as well as increased

electrical demand. A critical component of maintaining production is ensuring that sufficient air

remains available to the grate/kiln. With an increased pressure drop expected for this type of

scrubber, the process air fans and motors may need to be upgraded.

Please call me if you have any questions or need additional information. I would also like to review the

final STS report as soon as it is completed.

Regards,

Jason Aagenes

Section Manager - Environmental Affairs

Cleveland-Cliffs United Taconite LLC

P.O. Box 180

Eveleth, MN 55734

Office (218) 744-7803

Mobile (218) 290 -5936

Fax (218) 744-7635

jdaagenes@cleveland-cliffs.com
From: Fenske, Mary Jean [mailto:Mary.Jean.Fenske@state.mn.us] 

Sent: Tuesday, June 26, 2007 1:11 PM

To: Aagenes, Jason D.

Cc: Seltz, John; Jackson, Anne; Taylor, Hal

Subject: DRAFT STS report

Jason,

Here is the draft report from Hal Taylor at STS. We are currently reviewing and assembling our

comments. If you see any issues with the assumptions used, please comment directly to the MPCA (by

tomorrow if possible).

You will receive a comment of the final report and you can provide more detailed comments on that

product as well, if you choose .

<<UTAC Cost Analysis SpreadsheetSO2 STSHWTDraft 5 30 07.xls>> <<STS UTAC cost

analysis.doc>> 

Thanks Jason.

Mary Jean

Mary Jean Fenske, P.E.

Minnesota Pollution Control Agency

520 Lafayette Road N.

St. Paul, MN 55155

(651) 297-5472

e-mail: mary.jean.fenske@pca.state.mn.us

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may contain information that is privileged or confidential and thereby exempt and protected

from unauthorized disclosure under applicable law. If the reader of this message is not the

intended recipient, or an employee or agent responsible for delivering the message to the

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[1]