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Research Article Open access CC BY 4.0

Modelling the Condensed Water Discharge Rate in an Air Conditional System in South West, Nigeria

O. S. Bamisaye, P. K. Oke

Current Journal of Applied Science and Technology · pp. 1–13 · Published 18 Jan 2019

10.9734/CJAST/2019/46188

Abstract

Aims: This work is aimed at developing an empirical model for predicting condensed water discharge rate in an air conditional system, most especially in Nigerian offices.   Study Design: Quantitative study. Relevant data on condensate discharge rate was collected. Place and Duration of Study: An office located within the School of Engineering and Engineering Technology Building of the Federal University of Technology, Akure, Ondo State, Nigeria, between November 2015 to April 2016. Methodology: The method used consists of data collection and readings such as condensate volume, dry bulb temperature, relative humidity, sensible heat ratio, and dew point temperatures. A split type air-conditioning unit with a cooling capacity of 2500 W, using refrigerant and rated air flow rate of 400 m3/hr was used to determine the amount of condensate rate. Results: The result of six-month data collected showed that a total of 528 L of condensed water was collected at the split type air conditional unit. The highest condensate discharge rate of 1.07 L/hr was recorded on 6th and 7th April 2016. The coefficient of determination, R2, obtained for first, second and third order multiple linear regression model were 0.964, 0.9793, and 0.9803 respectively. The developed multiple linear regression model was used to compare the experimental and predicted values of the condensate from the air conditional unit used for the study. Conclusion: The developed model offers ease of prediction and forecasting of the amount of condensate discharge rate. This study confirms that relative humidity, sensible heat ratio, dry bulb temperature, and dew point temperature are the most significant factors contributing to an increase in condensate discharge rate.

Air condition regression model reclaimed water forecasting modern building.

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