APPENDIX 8-F. COMMERCIAL PACKAGED BOILER LIFETIME 

DETERMINATION

TABLE OF CONTENTS

8-F.1 INTRODUCTION ..................................................................................................8-F-1

8-F.2 LIFETIME LITERATURE REVIEW....................................................................8-F-1

8-F.3 METHODOLOGY .................................................................................................8-F-4

8-F.3.2 Assumptions............................................................................................................8-F-6

8-F-i
APPENDIX 8-F. COMMERCIAL PACKAGED BOILER LIFETIME 

DETERMINATION

8-F.1 INTRODUCTION

The U.S. Department of Energy (DOE) defines the term “lifetime” as the age when a 

product is retired from service. DOE notes that a large percentage of boiler equipment now on 

the market were not available 10+ years ago and therefore comprehensive lifetime data is not yet 

available. Also, there is an ongoing evolution of the boiler component lifetime, which has not yet 

been thoroughly assessed.

1

DOE uses national survey data, published studies, and projections based on manufacturer 

shipment data to calculate the distribution of boiler lifetimes. DOE assumes that the lifetime of a 

boiler is the same at different efficiency levels. DOE assumes a median lifetime value of 24

years both for gas and oil-fired boilers, based on its review of boiler equipment literature. See 

Section 8-F.2 for sources found in the literature review.

8-F.2 LIFETIME LITERATURE REVIEW

To capture variances in boiler lifetimes, DOE performs a literature review. Many sources 

provide average lifetimes or range of lifetimes for one or multiple groups of boilers. For 

example, some sources provide average lifetimes for gas boilers and oil boilers, while others

provide average lifetimes for boilers by heat exchanger materials. Still other sources do not 

specify any boiler characteristics, but provided an average lifeteime across all boilers. All the 

sources DOE researched are shown below in Table 8-F.2.1 that lists a given equipment lifetime 

(or range of lifetimes) and the characteristics of the boiler(s) to which it applies. DOE determines

the overall average equipment lifetime across all the sources researched and uses that as the 

mean boiler lifetime in the lifetime distribution.

Table 8-F.2.1 Commercial Boilers: Product Lifetime Estimates and Sources

Boiler Characteristics

Typical 

Lifetime 

(years)

Reference

Condensing boilers 10 Navigant Consulting (2014), Manufacturer 

52

Steel core boiler - heat exchanger 

boilers 10-15 CDW Engineering (n.d.)3

Condensing boilers 10-15 Navigant Consulting (2014), Manufacturer 

12

Condensing boilers 10-15 PM Engineer Magazine (2012)4

Boilers, unspecified 10-15 Navigant Consulting (2014), Manufacturer 

5&72

8-F-1
Steel water-tube, forced draft, hot 

water 10-33 ASHRAE (2014)5

Steel water-tube, natural draft, hot 

water boilers 11-23 ASHRAE (2014)5

Boilers, unspecified 12-14 VHK (2007)6

Copper Gas Boilers 14-16 Consortium for Energy Efficiency (1998)7

Steel boilers (MTHW/LTHW) 15 Chartered Institution of Building Service 

Engineers (n.d.)8

Condensing boilers 15 Chartered Institution of Building Service 

Engineers (n.d.)8

Floor standing Gas Boilers 15-20 VHK (2007)6

Copper core boiler - heat exchanger 

boilers 15-20 CDW Engineering (n.d.)3

Oil Boilers 15-25 VHK (2007)6

Boilers 17 VHK (2007)6

Steam boilers 18-20 Mazurkiewicz (1999)9

Larger commercial, copper tube 

boilers 19 PM Engineer Magazine (2002)10

Light commercial, copper tube boilers 19 PM Engineer Magazine (2002)10

Boilers, unspecified 19.5-20 GDS Associates (2007)11

Boilers, unspecified

Average 

= 20; 

Low = 

17; High 

= 24

Appliance Magazine (2009)12, Appliance 

Magazine (2010)13

Copper boilers 20 Navigant Consulting (2014), Manufacturer 

12

Boilers, unspecified 20 Navigant Consulting (2014), Manufacturer 

82

Boilers, unspecified 20-25 Control Engineering (1999)14

Boilers, unspecified 20-25 Rochester District Heating Cooperative 

(2008)15

New cast iron core boiler – heat 

exchanger boilers 20-25 CDW Engineering (n.d.)3

Oil Boilers 20-25 VHK (2007)6

Boilers, unspecified 20-35 American Boiler Manufacturers 

Association (2010)16

Steel tube boilers 20 - 40 CDW Engineering (n.d.)3

Light commercial, steel boilers 22 PM Engineer Magazine (2002)10

Steel water-tube hot water boilers 24 Menlo Park Fire District (n.d.)17

Steel water tube hot water boilers 24 Building Owners and Managers 

Association International (n.d.)18

Condensing boilers 25 Navigant Consulting (2014), Manufacturer

8-F-2
42

Steel fire-tube, forced draft, hot water 25 ASHRAE (2014)5

Larger commercial, steel 25 PM Engineer Magazine (2002)10

Boilers, unspecified 25 Efficiency Vermont (2013)19

Water tube boilers (MTHW/LTHW) 25 Chartered Institution of Building Service 

Engineers (n.d.)8

Cast iron sectional boilers 

(MTHW/LTHW) 25 Chartered Institution of Building Service Engineers (n.d.)8

Steel fire-tube hot water boilers 25 Building Owners and Managers 

Association International (n.d.)18

Steel fire tube steam boilers 25 Building Owners and Managers 

Association International (n.d.)18

Boilers, unspecified 25 Cleaver Brooks (2012)20

Steel fire-tube hot water boilers 25 Menlo Park Fire District (n.d.)17

Light commercial, cast iron boilers 26 PM Engineer Magazine (2002)10

Boilers 25 Burnham Commercial (n.d.)21

Steel water-tube steam boilers 25 Menlo Park Fire District (n.d.)17

Hot water boilers 25-30 Mazurkiewicz (1999)9

Steel Gas Boilers 25-30 Consortium for Energy Efficiency (1998)7

Boilers, unspecified 25-35 Environmental Defense Fund (2009)22

Larger commercial, cast iron boilers 26 PM Engineer Magazine (2002)10

Light commercial, stainless steel 

boilers 27 PM Engineer Magazine (2002)10

Steel water tube steam boilers 28 Building Owners and Managers 

Association International (n.d.)18

Larger commercial, stainless steel 

boilers 28 PM Engineer Magazine (2002)10

Steel fire-tube steam boilers 28 Menlo Park Fire District (n.d.)17

Steel boilers 30 Navigant Consulting (2014), Manufacturer 

12

Cast iron boilers 30 Navigant Consulting (2014), Manufacturer 

12

Cast iron steam boilers 30 Menlo Park Fire District (n.d.)17

Cast iron boilers 30 NoesisEnergy (n.d.)23

Steam boilers 30 Ceils (2011)24

Cast iron steam boilers 30 Building Owners and Managers

Association International (n.d.)18

8-F-3
Boilers, unspecified 30 McDonough (2008)25

Boilers, unspecified 30 Navigant Consulting (2014), Manufacturer 

3&42

Boilers, unspecified 30-40 Piper (2011)26

Old cast iron core boiler – heat 

exchanger boilers 30 - 50 CDW Engineering (n.d.)3

Cast iron hot water boilers 35 Building Owners and Managers 

Association International (n.d.)18

Cast iron boilers 35+ Weil-McLain (n.d.)27

Cast iron hot water boilers 35 Menlo Park Fire District (n.d.)17

Boilers, unspecified 40-60 Navigant Consulting (2014), Manufacturer 

6

2

8-F.3 METHODOLOGY

DOE’s lifetime methods are based on the "Using national survey data to estimate 

lifetimes of residential appliances" paper.

28

A Weibull distribution is a probability distribution function commonly used to measure 

failure rates.29 Its form is similar to an exponential distribution, which would model a fixed 

failure rate, except that it allows for a failure rate that changes over time in a particular fashion. 

The cumulative distribution takes the form:

for x > θ and P(x) = 1 for x ≤ θ,

Eq. 8-F-1

Where:

P(x) = probability that the equipment is still in use at age x,

x = equipment age,

α = the scale parameter, which is the decay length in an exponential distribution,

β = the shape parameter, which determines the way in which the failure rate changes in time, and

θ = the delay parameter, which allows for a delay before any failures occur.

When β = 1, the failure rate is constant over time, and this distribution takes the form of a 

cumulative exponential distribution. For the case of equipment, β is commonly greater than 1, 

which results from a rising failure rate as the equipment ages. A plot of a Weibull distribution 

(DOE’s calculated boiler survival function) is shown as Figure 8-F.3.1.

e x

P x

β

α

θ 



 



 − −

( ) =

8-F-4
Figure 8-F.3.1 Lifetime Distribution for Commercial Boilers

Typically shipments data over multiple years and equipment stock from survey data over 

multiple years are used together to determine the input parameters needed to define the 

distribution. However, in the case of commercial boilers the available data was very limited. 

Shipments data was only available for one year (see Chapter 9). Additionally, while the Energy 

Information Administration’s (EIA) Commercial Building Energy Consumption Survey 

(CBECS) survey data is available across multiple years, CBECS specifies the presense of a 

boiler but not the number of boilers present each building.

30 Without the number of boilers per 

building, it is not possible to determine the equipment stock.

The input parameters needed to define the Weibull distribution have been determined 

based on the data that was available. As discussed above, the mean boiler lifetime is determined 

based on the average of all of the sources found in the literature review. The boiler lifetime mean 

value is determined to be 24.8 years. Because DOE assumes that all boilers have a warranty 

period of at least one year, the boiler lifetime low value is set to one year.

The American Society of Heating, Refrigerating and Air-Conditioning Engineers

(ASHRAE) Service Life and Maintenance Cost Database is used to determine the spread of

boiler lifetimes.5 There are 92 buildings with boilers in the database (not including buildings 

with electric boilers). From the age of the boilers in each building, DOE has determined that 82.2

percent of the buildings have boilers less than 35 years old. Therefore the high value has been set 

to 35 years and the percentile of high value to 82.2 percent.

The mean value, high value, low value, and percentile of high value were used to derive 

the α and β values for the Weibull Distribution.

8-F-5
8-F.3.2 Assumptions

DOE’s lifetime-calculation technique depends on several assumptions:

• Equipment lifetime can be modeled by a survival function. In particular, a Weibull 

distribution is an appropriate survival function.

• The equipment survival function does not change over time.

• The survival function is independent of other building factors (such as building size, 

region, etc.).

• The survival function is independent of boiler characteristics such as fuel type, heat 

exchanger material, and efficiency level.

• The age of equipment as reported in ASHRAE’s Service Life and Maintenance Cost 

Database is accurate.

• The Weibull delay parameter, θ, is limited to between 1 and 5 years.

Two of these assumptions are of particular importance. The first is the assumption that a 

Weibull distribution is the correct distribution to use for equipment retirement rates. This 

distribution is the standard distribution for use in lifetime analysis, but it is not guaranteed to 

reflect actual commercial consumer behavior.

DOE limits the delay parameter to between 1 and 5 years to reflect the range of common 

appliance warranties. A delay of less than 1 year would imply that some appliances fail or are 

replaced within their initial year of use, a period during which they are commonly covered by 

parts and labor warranties. A delay of greater than 5 years implies that no appliances are replaced 

for some length of time after the end of the longest standard warranty. Fits with θ > 5 also 

commonly show nonsensical behavior with sharp changes in commercial consumer behavior or 

appliance survival immediately following the “delay” period.

8-F-6
REFERENCES

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8-F-7
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8-F-8
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8-F-9