ASHRAE Journal - May 2012 - 20

From reviewing plots of the correlations developed during this study, it became apparent that the cities in very dry regions that have a low amount of condensate were influencing the results such that the correlations were not as accurate as they could be for cities that have a higher amount of condensate expected. Therefore, for the final step in this regression study, the regression was performed with the nine cities removed that have very low amounts of condensate expected. This improved the overall correlation equation fit for the remaining cities, and further discussion on this issue is given in the results section.

Parameter Annual Average Dew Point Cooling Degree Days (Based on calculation of daily average temperature difference from a defined point 65°F) 1% Cooling Design Point – Dry Bulb Temperature 1% Cooling Design Point – Mean Coincident Wet Bulb Temperature 1% Cooling Design Point – Humidity Ratio 0.4% Dehumidification Design Point – Dew Point 0.4% Dehumidification Design Point – Humidity Ratio 0.4% Dehumidification Design Point – Mean Coincident Dry Bulb 0.4% Evaporation Design Point – Wet Bulb 0.4% Evaporation Design Point – Mean Coincident Dry Bulb Total Annual Rainfall Total Rainfall During Cooling Season, Defined as April 1 Through Oct. 31

Abbreviation Avg. DP CDD DB MCWB HR – Cooling DP – Dehumd HR – Dehumid MCDB– Dehumid WB – Evap MCDB – Evap Total Rainfall

Units °F

°F °F lbw/lba °F lbw/lba °F °F °F inches

Economic Analysis

This study determined the simple payApril–Oct Rain inches back period for a condensate collection system expected to capture a low, me- Table 1: Weather data parameters used in correlation study. dium and high amount of condensate. The amount collected is a function of the amount of incom- sump into a small collection basin and then pumps that water ing outdoor air, since that is the primary source of additional to a simple method of reuse, such as back into the basin of a moisture (ignoring minor additions from the occupants and chiller cooling tower. A fairly robust and simple system for other activities such as cooking). For the purposes of this installation has been verified by implementation in approxistudy, we considered typical low, medium and high amounts mately 10 retrofit applications the past few years, and further of incoming outdoor ventilation air, which were assumed to details on this design are contained in Reference 6. These probe 1,000, 5,000 and 20,000 cfm (500, 2500 and 10 000 L/s), vide the basis for the condensate system cost in this study. respectively. The outdoor airflow rates are values assumed A key to the economic viability of condensate collection is for one individual air-handling unit, or a situation where two the water rate charged by the local water utility. Specific and or more units are located in close enough proximity to effec- reliable cost data for the combined water/sewer rates could tively have their condensate flow combined. A high level of not be identified for two of the 47 cities used in this study, and outdoor airflow could be the result of either a large volume these were left out of the economic evaluations (but not the air-handling unit, or a unit with smaller supply air capacity regression correlation study). but with a large fraction of the total flow being outdoor air, such as with a dedicated outdoor air supply unit. The eco- Results nomic analysis results presented in this paper were results Condensate Prediction and Correlation Studies from the “high” level of outdoor airflow and based on the asThe condensate prediction correlation study results are sumption that this ventilation airflow occurred continuously summarized in Table 2. This evaluation process worked to24 hours per day. ward one key goal of this study: that being a derivation of a This analysis did not account for the effect of including a simple to use correlation equation of the amount of condenventilation energy recovery system that includes latent en- sate collected annually for a steady unit value of incoming ergy recovery. Thus, the amount of condensate and resulting outdoor airflow. economic benefits would need to be adjusted to account for The “p-value” statistics were used to help guide the decithe latent recovery effectiveness of the unit. Similarly, adjust- sions on which parameters to select for reducing the number ments should be made for systems that have variable outdoor of parameters in the next regression step. This process resulted air intake. in determining that the three best parameters are the average The cost of a condensate collection system will vary de- dew point (Avg. DP), cooling degree days (CDD), and the pending on such items as: whether storage was planned and if April through October cooling season rainfall. The correlathat storage system also included other water sources such as tion equation is given below, and the resulting predictions are rainwater; the intended reuse(s) for the water; and how much plotted in Figure 2. associated processing of the water is required before reuse. For the purposes of this comparative study, we assumed a Condensate (gal/cfm) = 0.4777 × Avg. DP + 0.00204 (1) simple system that drains the air-handling unit condensate CDD + 0.32596 × [April Through Oct Rainfall] – 22.50
20 ASHRAE Journal ashrae.org May 2012

ASHRAE Journal - May 2012

Table of Contents for the Digital Edition of ASHRAE Journal - May 2012