Predicting Condensate Collection From Hvac Air Handling ...


Recent water shortages in the southeastern US and elsewhere have

increased the perceived value of fresh water beyond its financial cost.

Local, interstate, and international disputes over water rights are

likely to further this trend. The increasing concern for water resources

within ASHRAE is reflected in two new Standards that address water use

in buildings: Standards 189.1-2010 (Standard for the Design of High

Performance Green Buildings Except Low-Rise Residential Buildings) and

191P (Standard for the Efficient Use of Water in Building, Site and

Mechanical Systems). Both standards provide requirements for water using

systems, addressing efficient usage as well as water reclamation and


Condensate collection from air handling units (AHUs) is one method

of water reuse that has been successfully incorporated in new buildings

and is even required in new construction in some locations. (1)

Condensate can most easily be routed directly to a cooling tower sump,

but with storage (and usually in conceit with a rainwater collection

system) can be used for irrigation or ornamental purposes; with further

processing it can be used for indoor applications such as toilet

flushing or even potable water.

Tom Lawrence is a Public Service Associate in the Faculty of

Engineering at the University of Georgia, Jason Perry is a Research

Engineer in the Faculty of Engineering Outreach Service at the

University of Georgia and Peter Dempsey is an engineering undergraduate

student at the University of Georgia

While incorporating condensate collection into new buildings can be

relatively straightforward, retrofitting existing buildings can be more

complicated. Since existing buildings comprise approximately 98% of the

building stock (the other 2% being new construction), they represent a

significantly greater immediate benefit to society in terms of energy or

water consumption savings. It is thus worthwhile to study and facilitate

condensate collection retrofits in existing buildings.

It is easy to estimate the cost to install a condensate collection

system, but it is more difficult to calculate the financial payback due

to water savings. Dire water shortages in some areas may make the

financial question moot, but for now in the majority of cases it is

likely that some financial justification will be necessary.

Currently, prediction methods and tools are not widely available

for evaluating whether condensate collection is worthwhile. Guz (2005)

suggests a rule of thumb of 0.1 to 0.3 gallons (0.4--1.1 L) of

condensate per ton of air conditioning, per hour of operation, but this

only applies to San Antonio. A simple online calculator (no longer

available) created by Wilcut and Fry determined the steady-state

condensate production rate for a given set of conditions, but was not

useful for predicting condensate over a season of varying weather.

Painter (2009) developed a prediction model for dedicated outdoor air

handling units with enthalpy wheel energy recovery, in which he used the

expected difference in humidity ratio on the entering and leaving sides

of a cooling coil. He developed the model to predict condensate

production in three locations in Texas using annual daily average

temperature and humidity data.

With this paper we present our methodology for predicting the

amount of water collected from an air handling unit, and we describe an

attempt to validate and refine the model with empirical measurements

taken throughout the 2009 cooling season in Athens, Georgia. The model

is designed to be adapted to any location for which hourly data are



The system used in this study was the second condensate collection

retrofit installed at the University of Georgia (UGA), and has been in

operation since February 2009. (see Lawrence, et al., 2010). It is

comprised of a stainless steel collection basin measuring about 2 ft

(600 mm) square and 9 in (230 mm) deep, a 1/6 HP (125 W) sump pump with

an external diaphragm switch, and an analog totalizing water meter with

1/10th gallon (0.38 L) resolution. The system is equipped with a check

valve above the pump to prevent backflow into the basin.

The basin was installed so that it could intercept the original

path of the condensate drain pipe without changing the slope or the

dimensions of the U-trap at the drain outlet from the AHU. An emergency

overflow pipe is connected near the top of the basin and leads to the

existing floor drain, so that the original drain path would be completed

in the case of a pump failure.

The meter was installed at eye level in the vertical section of

pipe above the pump so that it would always be measuring full pipe flow.

This was a lesson learned from the first installation at UGA in which

the meter was installed horizontally in a section of pipe with

essentially open-channel flow, raising concerns that the meter might be

fooled into reporting more water than is really flowing through it.

After the meter, the pipe runs up and over the AHU, through the

penthouse wall, across the roof at a slope of 1/4 inch per foot (21 mm

per m), and joins a pipe from another condensate collection system

before dropping down an exterior wall to the sump of the building's

cooling tower.

Measurement Equipment and Methods

Dataloggers were used measure the air temperature and relative

humidity at the outdoor air intake and in the fan section of the AHU. We

employed a combination temperature and relative humidity datalogger,

which has a RH measurement accuracy of [ or -] 3.5% from 25% to 85%

over the range of 59[degrees] to 113[degrees]F (15[degrees] to

45[degrees]C) and [ or -] 5% from 25% to 95% over the range of

41[degrees] to 131[degrees]F (5[degrees] to 55[degrees]C). The logger

temperature accuracy is [ or -] 0.72[degrees]F from 32[degrees] to

104[degrees]F ([ or -] 0.4[degrees]C from 0[degrees] to 40[degrees]C).

We recorded fan speed during the study using fan motor current as

proxy data for fan speed and hence airflow. A current transformer (CT)

rated for 50 amperes was placed on one leg of the three phase motor

circuit and connected it to a datalogger. The CT and datalogger

combination is rated as being accurate to [ or -] 2.25 A.

We synchronized and programmed the three dataloggers to record

every five minutes, and we downloaded data from them about once a month.

Airflow Baseline

From our initial observations of the supple air fan operation, the

fan current draw (as indicated on the variable frequency drive display)

tended to be within a fairly narrow range, but did vary some. We

obtained the baseline airflow of the AHU by conducting pitot tube traverses across the supply air ductwork leading from the AHU, and

recorded the current to the fan. The baseline airflow measurements were

made when the fan was in normal operation and the current draw near the

'nominal' level. We next also conducted pitot tube traverses

with the fan speed manually set near the upper end and then again near

the lower end of what our data loggers had recorded as being the fan

normal operating range. From the pitot tube traverses we then calculated

the supply airflow at the 'baseline' current, which was used

as the primary reference point for determining the airflow for all data

points during the cooling season.

Water Meter Validation

To ensure that the water meter was not affected by turbulence due

to its proximity to the pump, a calibration procedure was performed

using a graduated one gallon (3.8 L) container. One gallon at a time was

added to the system's collection basin until the pump was triggered

and the water pumped out. This process was repeated approximately 15

times, with a recording taken from the meter each time. There was no

significant error between the measured input of water and the cumulative

meter reading at the end of the process.


General method for computing condensation

For simplicity, consider the process of a unit conditioning 100%

outdoor air (such as with a dedicated outdoor air system or DOAS). The

psychrometric chart shown in Figure 1 represents a path of outdoor air

as it passes across the cooling coil for the 0.4% cooling design

condition in Athens, Georgia. Assuming a supply air condition of

55[degrees] F (12.8[degrees] C) and 85% relative humidity (wet bulb

T=52.5[degrees] F or 11.4[degrees] C), the humidity ratio changes across

the coil from 0.0141 to 0.0078 lb/[lb.sub.air] (kg/[kg.sub.air]). The

difference in absolute humidity ([omega]) between the incoming outdoor

air and supply air leaving the unit represents the amount of

condensation that occurs. Thus, for every pound (or kg) of air supplied

by the unit, 0.0141 - 0.0078 or 0.0063 pounds (kg) of water are



The total amount of condensate expected is determined by the

equation below:

Condensate collected = Airflow x density x 60 [min/hr] x

[DELTA][omega] (1)

Assuming for example 1,000 cfm (472 l/s) of outdoor air is being

conditioned, the total amount of condensate expected would be.

Condensate = 1,000 [[ft.sup.3]/min] x [lb/13.133[ft.sup.3]] x 60

[min/hr] x (0.0141 - 0.0078) [[lb.sub.water]/[lb.sub.dry air]] = 28.8

[lb/hr] (13.1 kg/hr)

This is approximately 3.5 gallons (13.1 liters) per hour at the

cooling design condition.

For the condensate prediction study described in this paper, a

spreadsheet model was developed which computed an estimate of the

condensate collection rate expected during each five-minute data logging time period through the course of the cooling season. The model uses the

following data inputs:

* Outdoor air temperature and relative humidity (from data logger)

* Supply air temperature and relative humidity (from data logger)

* Outdoor air and supply air humidity ratio (computed from recorded

data set)

* Supply fan input current (from data logger)

* Air handling unit supply airflow rate at 'baseline'

flow and specific current input values (measured using pitot tube

traverses at 'normal operating' fan speed and other points by

manually adjusting fan speed at the VFD controller)

Spreadsheet model logic

The following computational steps are performed by the spreadsheet

model for each of the five-minute data recording periods.

1. Compute the differential between outdoor and supply air humidity

ratio. ([DELTA][omega])

2. Estimate the supply airflow rate for this period, assumed to be

a function of the cube root of the current.

Supply Flow This Period = [([Current this period/Current at

baseline flow]).sup.[1/3]] x Baseline flow (2)

Baseline current = 33.9 Amps (one leg of 3 phase circuit);

Baseline flow = 19,128 cfm

3. Multiply the supply volumetric airflow by density and 60 min/hr

to get supply air mass flow rate ([m.sub.air]) in lbm/hr.

4. Compute the predicted condensate collection rate. Condensate

flow = [m.sub.air] x [DELTA][omega] The result is the predicted

condensate collected in lb/hr.

5. Convert the predicted condensate mass flow rate into a volume

flow rate, gallons/hr and gallons/min (gpm).

6. Determine the predicted total condensate produced for this data

logging time period Condensate produced = Volume flow (gpm) x 5 min

The total condensate produced is summed up between each field meter

reading period, which was typically done on a daily basis during each

weekday. For each period between field meter readings, the total

predicted condensate is compared to the actual measured amount.


Baseline model evaluation

We made a total of 108 water meter readings during the 2009 cooling

season. Data were recorded from April 1 through the end of September. By

April 1 the installed water meter had already recorded some condensate

collected but our data logging equipment was not installed and validated

until then. A second AHU in this mechanical room was retrofitted with a

condensate collection system in early October, and the output pipe from

this new unit connected into the collection basin used for our study

AHU, therefore we had to stop data recording on September 29. Since this

was getting near the end of the condensate collection and cooling

season, it was not felt to be a significant problem.

Readings were made nearly every day during the normal workweek

(Monday through Friday; the building is locked to outsiders on weekends

and holidays). The predicted condensation results were computed using

the supply fan airflow as measured in the spring and using the installed

data logger measurements for outdoor air and supply air conditions and

these compared to the actual condensate collected using the installed

totalizing flow meter.

Using the prediction method described earlier and the recorded

measurements from the dataloggers, the condensate quantities predicted

by the model were consistently lower than the actual quantities measured

throughout the cooling season. The total predicted condensate for this

air handling unit during the cooling season was 134,021 gallons (507,325

liters), but the actual amount collected was 171,793 gallons (650,307

liters), for a net under-prediction error of 28%. Figure 2 shows the

results for each data reading period in terms of average condensate flow

rate (gallons per minute) during the recording period, which makes for

an easier comparison than just the total predicted or actually

collected, as the time lengths between readings varied.


To check how consistent this error was through the cooling season

and if it depended on the weather conditions, Figure 3 is provided which

shows a scatter diagram of the ratio of actual to predicted total

condensate collected versus the average outdoor air humidity ratio.

Although the predicted values seem to be a little closer to the actual

during periods of higher ambient humidity, this is not a strong trend

and does not appear to provide any insight to the analysis.


Evaluation of potential sources of error - Supply airflow rate

The amount of predicted condensate during any given period is a

direct linear function of the supply airflow rate used in the

calculation, since the condensate collection predicted is based on the

humidity ratio of mass of water per mass of dry air (lb/lb or kg/kg).

Thus, if the actual flow were say 10% higher than used in the

calculation, there would be an underestimation of condensate collection

by 10% even if all other data were perfectly known and the equation was

perfectly accurate and applicable to this situation.

This is illustrated by considering a case where the supply airflow

used in the condensate prediction calculation is arbitrarily increased

by 30%, representing a case where the actual airflow were 30% more than

determined by the field measurements. The resulting total condensate

predicted for this cooling season is 174,227 gallons (659,522 liters),

or an error compared to the actual measure of about 1%. The plot of

average condensate flow rate for each data recording period shows a

fairly good match as well (Figure 4). While this 30% extra fan flow

seems to be a possible explanation for the difference, it is not the

only potential contributor to the error measurement. The 30% error in

airflow measurement is also considered larger than what is generally

considered acceptable in practice (more on this later).


Evaluation of potential sources of error - Relative humidity


The accurate measurement of relative humidity has been an issue in

the past within the HVAC industry. For example, earlier versions of

humidity sensors used in economizer controllers had a propensity for

early failure, leading to bypassing of the economizer control and giving

a black eye to this concept for years.

The particular humidity sensors used for this study have a

manufacturer's stated accuracy of [ or -] 3.5% for the majority of

the temperature range that they were used to measure. Since the

calculation of condensate collection potential in this study involved a

differential of humidity ratio between the incoming outdoor air and the

supply air, there potentially could be anywhere between a -7% and 7%

error (double one sensor) even if the sensors used were within the

manufacturer's specifications.

Consider the extreme case of this and with the error in the proper

direction to bring the predicted condensate level closer to the actual

measured value. This would be if the case were that the actual relative

humidity differential between incoming outdoor air and the supply air

were 7% larger. A plot of the average condensate flow rates during each

data recording period is given in Figure 5. The predicted annual

condensate collected under this scenario would be 165,541 gallons

(626.643 liters), for a 4% error in the prediction. While this scenario

is possible, it relies on a 'best case' assumption of the

error in relative humidity measurements.


Other scenarios were also evaluated with an assumed larger relative

humidity differential of 1% and 3.5% as well. These results had

corresponding levels of improvement in the condensate prediction.

Evaluation of potential sources of error - One realistic


This scenario evaluated one case where the relative humidity and

supply fan flow rate error values would be within that expected for a

'typical' case. For this evaluation, we evaluated the

predicted condensate flow assuming the following errors in sensor

readings or measurements.

* Relative humidity - assumed a 3.5% higher difference between

outdoor air and supply air than we measured

* Supply airflow - assumed the supply air fan had a 15% higher flow


A 15% error in measurement of the supply fan airflow rate is a very

reasonable estimate. For example, the U.S. Green Building Council's

LEED-2009 program for IEQ Credit 1 considers a [ or -] 15% differential

in measured incoming outdoor airflow an acceptable value.

Figure 6 gives a comparison plot of the average condensate flow

rates during each data recording period. The results compare very

similarly to the actual measured values, and the total annual condensate

predicted is 170,428 gallons (645,142 liters), for an under-prediction

of only 1% compared to the actual measured value.


Evaluation of potential model simplifications - Assume constant

supply airflow rate

This final section evaluates the impact of using more simplified

model approaches. One of these is to use just an 'average'

value for the supply fan flow rate. Since we were not directly measuring

the supply flow, only the electrical current input to the fan, this will

have to be approximated. Fortunately, the fan speed did not vary

considerably during any given day or through the course of the cooling

season. In fact, our baseline airflow measurement taken in the early

spring was at a fan speed (based on current reading) considered very

representative for the entire cooling season. This flow rate was

measured at 19,128 cfm (9,027 liters/s), so this scenario evaluated the

predicted collection of condensate assuming the supply airflow was a

constant 19,128 cfm (9,027 liters/s). One other point to note is that

there is an error introduced from measuring the current as well; our

current transducers and datalogger together are accurate to [ or -]

2.25 A and we were measuring current in the range of about 30 to 40 A

The resulting predicted annual condensate was 136,532 gallons

(516,830 liters), essentially the same as the baseline case when a fan

speed (using measured current) correction was applied. In fact, this

number is slightly closer to the actual measured amount of condensate


Evaluation of potential model simplifications - Assume constant

supply air humidity ratio

Another possible simplification is to assume that the supply air

humidity ratio is constant, and thus there would be no need to measure

the supply air temperature and relative humidity. This is an important

simplification that makes estimation of any AHU for condensate

collection potential (retrofit or new installation) much easier.

For this scenario, we assumed that the supply air humidity ratio

was that if the supply air temperature and relative humidity were

56[degrees] F and 85%, respectively. This corresponds to a humidity

ratio of 0.008 [lb.sub.water]/[lb.sub.dryair]


The resulting predicted annual condensate was 133,288 gallons

(504,548 liters), again essentially the same as the baseline case using

measured temperature and relative humidity of the supply and the

corresponding humidity ratio for those conditions.

Other Sources of Error

Several other potential sources of error might exist, and one of

these could include the sensor locations. For example, the outdoor air

intake is shielded from direct sun, but there may still be some error

from that since the intake is on the south side of the building. The

supply air conditions were recorded with the data logger located in an

easy to access section of the fan chamber; it was assumed the air is

fairly well mixed but this may not have exactly been the case.


This study evaluated a model for predicting condensate collected

from an AHU that conditions 100% outdoor air. A number of cases and

possible scenarios for the impact of measurement error were studied to

see if these alone could account for the under-prediction of condensate

compared to the measured value during an entire cooling season. A

summary of all the evaluation scenarios is given in Table 1.

Even though the baseline model using measured values for key

parameter inputs such as outdoor and supply air temperature and relative

humidity and an estimation of the supply airflow based on fan current

draw underpredicted the condensate that would be collected, there are

several potential scenarios that could explain this simply by error

introduced by sensor (in)accuracy. One very real possibility was

discussed with an assumed 3.5% error in relative humidity (the

manufacturer's advertised accuracy) and 15% error in airflow.

Even if there were a 30% error in predicted condensate, this may be

acceptable if the only answer really desired was if one should install

or retrofit a condensation collection system or not. A 30% error in

estimated condensate would result in 30% error of the potential cost of

water savings or recovery, which may or may not be significant to the

decision maker.

We also determined that two simplifications could be made to the

prediction if one is only concerned with the total annual condensate

collected. For this AHU, a constant supply fan flow could be assumed.

This assumption may not apply to all AHUs across the board as it would

depend on the variation in fan speed expected and how wide that

variation is. The assumption of a constant supply air humidity ratio

also can be reasonably assumed, where this should be based on the

average supply air conditions expected.

Based on all these results, we conclude that our model for

estimating the condensate collection potential for any AHU with 100%

outdoor air is a relatively simple and valid approach. But what if the

AHU is not 100% outdoor air (as most are not)? We feel the model is

applicable there as well, with it being up to the engineer to determine

or estimate the incoming outdoor airflow and variation in flow to use.

The purpose of this study was to validate our approach for

estimating the potential application of condensate to a new or existing

AHU and the amount of water expected. Before this study, we have done

this using typical weather data (Marion and Urban 1995). Based on the

fairly successful results of this study, we can safely recommend

applying this model approach using 'typical' weather data to

predict condensate collection during a 'typical' cooling



Guz. K. 2005. "Condensate Water Recovery", ASHRAE Journal


Lawrence, T.M., J. Perry and P. Dempsey, 2010, "Making Every

Drop Count: Retrofitting Condensate Collection on HVAC Air Handling

Units", ASHRAE Journal 52(1):48-54.

Marion, W. and K. Urban. 1995. Users Manual for TMY2s. National

Renewable Energy Laboratory, Golden, Colorado

Painter, F. 2009. "Condensate Harvesting from Large Dedicated

Outside Air-Handling Units with Heat Recovery", ASHRAE Transactions

2009, 115(2):xxx

Wilson, A. 2008. "Alternative Water Sources: Supply-Side

Solutions for Green Buildings", Environmental Building News, May

2008. Available from:


December 2009].

T.M. Lawrence, Ph.D.


Jason Perry

Associate Member ASHRAE

Peter Dempsey

Student Member ASHRAE

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