Development and testing of condensate assisted pre-cooling unit for improved indoor air quality in a computer laboratory

Development and testing of condensate assisted pre-cooling unit for improved indoor air quality in a computer laboratory

Building and Environment 163 (2019) 106321 Contents lists available at ScienceDirect Building and Environment journal homepage:

5MB Sizes 0 Downloads 9 Views

Building and Environment 163 (2019) 106321

Contents lists available at ScienceDirect

Building and Environment journal homepage:

Development and testing of condensate assisted pre-cooling unit for improved indoor air quality in a computer laboratory


Dhamodharan Palanisamy, Bakthavatsalam Kannappan Ayalur* Department of Energy and Environment, National Institute of Technology, Tiruchirappalli, India



Keywords: Indoor air quality (IAQ) CO2 concentration Pre-cooling unit (PCU) Phase change material (PCM) Air change per hour (ACH) and SOLIDWORKS flow simulation

Ventilation of fresh air into an enclosed air-conditioned environment is often not sufficient leading to poor Indoor Air Quality (IAQ). This paper analyses the effective utilization of Air Conditioning (AC) condensate for improving the IAQ. A 33 TR cooling capacity is considered for the experimental study wherein the AC condensate recovered is 12.5 L/hr with an average temperature of 16 ± 0.5 °C. The pre-cooling unit (PCU) is designed for effective utilization of AC condensate. Fresh outdoor air is pre-cooled in PCU through Air to Water heat exchanger (AWhx) and Thermal Energy Storage (TES) before being admitted into the air conditioned space. Base case is one where no fresh air is admitted into the laboratory except through infiltration. In the base case, the numerical results of indoor CO2 concentration were compared with the experimental measurements. The error range was 0.5%–8%. In the modified case, 170 m3/hr of fresh pre-cooled air is admitted through PCU at 28 ± 0.3 °C. PCU helps in reducing the CO2 concentration inside the lab by 5%–6%. Further, the CO2 concentration is reduced by 42% up to 3 m from PCU and maintained below 1000 ppm for 1 h. The flow simulation was executed with a curtained control volume of 44 m3 and the IAQ parameters were analyzed with six occupants. The CO2 concentration was maintained within 1000 ppm most of the time and the air velocity was also within the range of ASHRAE standard.

1. Introduction India is a hot humid country and the highest ambient temperature recorded being 51 °C at Phalodi in May 2016 [1]. It becomes essential to use air conditioners to provide thermal comfort in the living space. The primary function of an air conditioner is to provide acceptable indoor environment and thermal comfort [2,3]. Fresh air availability inside the air conditioned space is only through infiltration which is roughly 7–12% of ventilation requirement. CO2 concentration increases due to a continuous re-circulation of air inside the room [4]. Thus, the air quality in such built environment often does not meet the international standards of ventilation. The permissible CO2 concentration in occupied space for healthy and hygienic environment is 1000 ppm as per ASHRAE standard [5,6]. In reality, the CO2 concentration is in the range of 1500–1800 ppm due to continuous recirculation of room air. Many authors have investigated the effect of ventilation rate and indoor CO2 concentrations on metabolic rate, cognitive function, learning skills and involvement levels in the classrooms [7–10]. The level of CO2 concentration mainly depends on ventilation rate and number of occupants [11]. The occupancy is categorized as transit or fixed, based on the nature of working environment and its usage. The


ventilation rates in different schools with different air conditioning capacities have been investigated and found that the CO2 level could increase to 4000 ppm during classroom occupancy periods [12]. Silvia Vilcekova et al. [13] found that the average indoor CO2 concentration was more than 1000 ppm in an air conditioned school environment. Natural ventilation and continuous monitoring of CO2 inside the classroom were recommended to improve the IAQ [13,14]. In an enclosed air conditioned space, the only means of supplying fresh air is by window opening. L.A. Wallace et al. [15], discussed the air change rates in an occupied air-conditioned house and concluded that automatically operated window along with exhaust fan enhances ventilation and maintains CO2 level below 1000 ppm. The outdoor environment condition also plays a significant role in the case of ventilation through opening of window [16]. Literature survey clearly shows that increasing fresh air ventilation and incorporation of CO2 sensors (Demand Control Ventilation) could be appropriate solutions for lowering the CO2 concentration. However, both these measures lead to rising energy consumption and energy cost. Therefore, a new method of pre-cooling ambient fresh air and admission to the conditioned space is needed to improve the IAQ. The condensate from the AC is a potential source of chilled energy.

Corresponding author. E-mail address: [email protected] (B.K. Ayalur). Received 15 April 2019; Received in revised form 26 July 2019; Accepted 31 July 2019 Available online 01 August 2019 0360-1323/ © 2019 Elsevier Ltd. All rights reserved.

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur






Air Conditioning Air Change per Hour Air Handling Unit American Society of Heating, Refrigerating, and Air Conditioning Engineers Air Water heat exchanger Carbon Dioxide Coconut Oil Heating, Ventilation and Air Conditioning Indoor Air Quality Metabolic rate

National Accreditation Board for Testing and Calibration Laboratories Non Dispersive Infrared parts per million Phase Change Material Pre-Cooling Unit Relative Humidity Semi Implicit Method for Pressure Linked Equations Thermal Energy Storage Ton of Refrigeration Latent Heat of Fusion (Melting) Latent Heat of Fusion (Solidification)

applications [26,27]. Based on available condensate energy source, the list of phase change material is identified and tabulated in Table 1. One of the critical issues is the choice of appropriate PCM. The selected PCM must be eco-friendly and should not harm occupants’ health. Also, the cost of PCM must be affordable [28]. Among the different organic PCMs, Bio-PCMs are highly stable even with airborne contamination. In this study, Coconut Oil (C.O.) is used as phase change material since its phase transition temperature of 22.5 °C, is very close to human comfort temperature [29]. Validation of experimental results is essential for reliability on measurements of indoor air quality parameters such as indoor CO2 concentration, air temperature and relative humidity. A few studies have been reported on modeling and simulation of indoor air temperature and CO2 concentration in the working environment [30–37]. Simulations of exhaled CO2 concentration was studied using Computational Fluid Dynamics (CFD) at 1.4 m height (Case I) and 1.6 m height (Case II) from the floor level. It was found that the simulated temperature and simulated CO2 concentration of both cases were in accordance with experimental measurements [31]. The office room prototype has been modelled and it examines the influence of occupant behavior on indoor temperature distributions with and without the addition of window shading device [37]. Other studies have been considered for IAQ modeling and simulation with a room dimension of 5.5 m × 20 m × 7.7 m. The studies concluded that the simulated indoor air quality parameters were within the range of the ASHRAE standard with an air change rate of 10–14 [33]. In this study, SOLIDWORKS flow simulation software is used for simulation of indoor air quality parameters. Sampling of ambient air in the computer laboratory has shown a CO2 concentration of 1600 ppm, which is much higher than the ASHRAE standard. The CO2 concentration was observed to cross the permissible limit of 1000 ppm under the following situations: (a) circulation of return air through a common plenum that leads to the CO2 concentration exceeding the permissible limit 3 h after turning on the AC. This happened even when the number of occupants was only 20 in the lab (b) One lab was fully occupied (i.e. around 40 people). leading to an increase in the CO2 concentration in the other three labs. After an hour, the CO2 level crossed 1000 ppm in all the four labs, which clearly

The quantity of condensate water depends on outdoor air humidity, number of occupants and cooling capacity. The collected condensate quantity is around 0.37–1.13 L/hr for one TR capacity [17]. Room air conditioners like split and window units could give only a small amount of condensate. Hence, the potential to recover energy from the condensate in such systems is negligible and water is egressed to outdoors. Theatres, malls, and hospitals are equipped with air conditioners of cooling capacity of more than hundred TR. The condensate generation in such systems is large which could be used as a potential cooling energy source [18]. Literature shows that, the AC condensate could be used for many applications such as gardening, makeup water for cooling tower, pre-cooling of outdoor air, roof dust cleaning, spray cooling and condenser cooling, and even for drinking purpose after necessary treatment [17–23]. Condensate recovered from 100 kW capacity was utilized for improving the performance of AHU and cooling tower in hot and humid climates of Singapore [21]. A reduction in power consumption of 10.9% was reported with an average recovered condensate temperature of 11.8 °C. After the energy recovery, the condensate water was circulated to cooling tower, which turn in helped in reducing the supply of makeup water by 50%. Research gap identified through the survey found that, so far, a few studies have been conducted in the field of energy recovery from air conditioning condensate for energy savings and water sustainability, but very few are focused on IAQ. Therefore, AC condensate obtained in high capacity plants is a promising energy source and it could be used for improving IAQ. In order to maintain the CO2 concentration below the permissible limit of 1000 ppm, the supply of fresh air into conditioned space is required. However, allowing fresh outdoor air into the air conditioned space leads to reduced thermal comfort and increase in energy consumption [16,24,25]. Outdoor air is warmer and hence it is desirable to pre-cool it before supplying into air conditioned space. AC condensate is a potential energy source for pre-cooling the outdoor air. However, the higher outdoor temperature poses a challenge for recovering the energy from the AC condensate. The volumetric flow rate of condensate is also low; therefore, latent heat storage systems could be an option for storing the energy content in the AC condensate. Phase Change Material (PCM) is an option to recover the energy content in the form of latent heat and provide free cooling in building Table 1 List of available PCMs along with cost and health hazard. Name of PCMs

Melting Point (°C)

Latent Heat (kJ/kg)

Safety and Hazards to the occupants

Cost (INR/Lit)


Acetophenone Hexadecane Benzyl benzoate Tert-Butyl alcohol 4-Methylacetophenone Butyl Stearate Dimethyl sabacate Coconut oil

20.5 18.1 21 23–26 22–24 19 21 22–24

140 236 96 18 NA 140 120–135 70–100

Skin irritation Aspiration hazard Skin irritation, Hazardous to the aquatic environment Skin irritation, Flammable liquids Skin irritation Skin irritation Corrosive, Eye and Skin irritation No hazards

1250 17500 1020 700 2800 4950 2310 480

[36–38] [27] [39] [40]


[41] [42] [28,43]

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

The Air Water heat exchanger (AWhx) operates in batch mode and provides sensible cooling to fresh air, followed by transfer of latent heat to fresh air from the charged PCM. The setup was used for testing two different cases viz., (a) Base case and (b) Modified case. The base case is one, where no fresh air is admitted other than infiltration. In the modified case, fresh air (170 m3/h) is pre-cooled by AC condensate through PCU before being admitted into the conditioned space. The basis for admitting 170 m3/hr of fresh pre-cooled air is drawn from ASHRAE standard- 62.1 which insists 15 cfm/person [38]. In this study, the six occupants require 90 cfm rounded off to the nearest standard size of 100 cfm (i.e.170 m3/hr). Experimental runs were executed for these two cases while assessing the improvement of IAQ. Using SOLIDWORKS flow simulation, the impact of fresh air admission on distribution of CO2 concentration and temperature was also studied.

indicates the need for improving the IAQ in the conditioned space, preferably without an increase in energy consumption. The objective is to improve the IAQ through effective utilization of AC condensate by incorporating a Pre-Cooling Unit (PCU). The PCU pre-cools the outdoor ambient air using AC condensate. Recovery of the condensate and pre-cooling of the outdoor air are performed by two units namely (a) Air Water heat exchanger (AWhx) and (b) Thermal Energy Storage (TES) units. The performance of PCU was studied at different condensate temperatures and results are reported. Numerical analysis was done to evaluate the indoor CO2 concentration and was compared with experimental results. Fresh air ventilation along with a curtain arrangement is modelled using SOLIDWORKS flow simulation for analysis of IAQ parameters. 2. Methods

2.3. Sampling and analysis 2.1. Description of the case building Indoor air quality was experimentally evaluated in the computer laboratories. Testo 480 and IAQ probe were used for measuring the CO2 concentration, RH and dry bulb temperature inside the conditioned space, and the data were recorded for every second. Non-Dispersive Infrared (NDIR) sensors measured the CO2 concentrations with an accuracy of 50 ppm ± 2%. Temperature sensor calibrated in NABL accredited laboratory had an error and uncertainty of 0.4 °C and 0.18 °C respectively. The position and the number of sampling points were fixed as per guidelines for better indoor air quality [5], i.e. location of the sampling probe is kept at 1.2 m above the floor. Probe was positioned 0.6 m away from the occupant's nose to avoid any measurement error [39]. The measurements were taken from 8.30 a.m. to 5.30 p.m. Computer Lab 1 was considered for IAQ assessment with and without PCU unit. Details of instrument accuracy and error associated with experimental measurement of parameters such as temperature, relative humidity, CO2 concentration and air velocity are given in Table 2.

Studies were conducted in the computer laboratory (Fig. 1) at the National Institute of Technology (NIT), Tiruchirappalli, Tamilnadu, India. The centralized unit is divided into two units, each with a capacity of 16.5 TR, and runs 24/7 dedicated to four labs viz., Computer Lab I, Computer Lab II, Computer Lab III and Computer Lab IV hosting around 200 students. The AHU unit is 3.7 m × 6.6 m x 5.3 m together with a rectangular channel of 0.29 m × 0.32 m on the north side wall to supply fresh air to the AHU room. 2.2. Methodology The approach is to provide a healthy air conditioning environment in terms of fresh air ventilation and acceptable limit of CO2 concentration. The improvement of IAQ using pre-cooling of air through AC condensate aided with PCM is explained through flow chart (Fig. 2). The flow chart consists of three subsections (i) Assessment of IAQ and energy consumption (ii) PCM selection and its characterization (iii) Testing of PCU. IAQ assessment was carried out for both existing and modified system using sampling and analysis techniques. Section (ii) involves identifying a suitable PCM which has a capability to store its latent heat with an available source of condensate. In this study, Coconut Oil (C.O.) was characterized and subsequently used as latent heat storage material, with suitable encapsulation. C.O. was procured from Sri Krishnan Oil Mill, Tiruchirappalli with purity of 99%. The PCU consists of both sensible and latent storage units within a single system.

2.4. Experimental setup The experimental assembly (Fig. 3) consisted of an insulated condensate tank, axial fan, radiator (AWhx), pump, spiral copper tube coil filled with PCM, aluminum duct along with necessary measuring sensors and instruments. The flow chart (Fig. 4) depicts the working of PCM assisted PCU system. The condensate tank (25 L) is made of HDPE with a diameter of 0.245 m and a height 0.6 m, insulated with nitrile rubber, in which the condensate water is collected at an average

Fig. 1. Test building for Indoor Air Quality Assessment. 3

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

Fig. 2. Methodology followed for improving Indoor Air Quality.

pump with 2 m head, 20 W is used for pumping the water to the radiator with a flow rate of 30 mL/s. Since the condensate is collected in the outdoor, there is a need for storing and recovering the available chillness continuously through PCM. PCM is filled in spiral copper coil

temperature of 16 ± 0.5 °C. The cold condensate water from the tank passes to TES tank by gravity. The TES tank is made of aluminum sheet with a dimension of 0.4 × 0.65 m2. The TES tank was insulated with nitrile rubber (k = 0.03 Wm−1. K−1) with a thickness of 13 mm. A

Table 2 List of instruments including with error details used for experimental measurement. Parameters

IAQ probe

Anemometer probe

Power Energy Logger

Temperature (°C)

RH (%)

CO2 (ppm)

Indoor air velocity (m/s)








−20 to 70

0 to 100

0 to 10,000

0 to 20

V = 100–1000 V I = 200 mA to 10,000A


± 0.45

± (1.0%) for 0 to 90 %RH ± (1.4%) for 90 to 100 %RH

± (50 ppm CO2 + 2% of reading) for 0 to +5000 ppm CO2 ± (100 ppm CO2 +3% of reading) 5.001 to +10,000 ppm CO2

± (0.03 m/s + 4% of reading)

± 0.5% to ± 0.005% Pnom

Percentage Error (%)







Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

rectangular axial fan of 9-inch diameter is mounted in front of AWhx to provide the inlet air with a velocity of 2.5 m/s. Fan, AWhx and TES unit along with PCM are enclosed within an insulated aluminum duct. A 6inch hole was punched on the wooden window board to allow precooled fresh air into conditioned space. The entire setup was supported tightly with stainless steel frame mounted on a concrete base. PCU was designed based on the available energy content in the AC condensate and the selected PCM. This system was installed on the western wall of Computer Lab I, to facilitate condensate collection closer to the PCU. Drain pipe was well insulated and the increase in condensate temperature was restricted to 0.6 °C to 1.1 °C over a distance of 2.4 m from the condensate source point. The water flow rate and the mass of PCM were estimated after analyzing the mass and energy balance across the PCU. The detailed view of PCU along with experimental measurement is shown in Fig. 5. Two set of experiments were conducted viz., the base case and the modified case. In the base case, the conditioned space was maintained with actual room air change rate of 2.77 ACH and no external fresh air was admitted. The variation in the CO2 concentration corresponding to the number of occupants was studied in the base case. In the modified case, planned air changes were carried out through PCU based on the number of occupants and CO2 concentration and the variations were studied.

Fig. 3. Model of Pre-Cooling Unit (PCU) for improved IAQ.

tube with a dimension of 9.52 mm OD and 2.2 m length with a clamp holder and set within the TES tank. The AC condensate is used for charging (solidification) the PCM. Subsequently, the latent heat is used for cooling the ventilation air which is admitted into the room through discharging of PCM (melting). After extraction of the chillness from the condensate, the water is drained out from the TES tank and subsequently refilled with fresh condensate for charging the next batch. Entire operation is intermittent depending on the timeline when CO2 concentration exceeds 1000 ppm. As of now, this is not exactly Demand Control Ventilation (DCV). In order to cool the admitted outdoor air temperature, it is passed through the AWhx and TES tank. The

2.4.1. Condensate assisted pre cooling unit (PCU) PCU consists of Air Water heat exchanger (AWhx) and a Phase Change Material (PCM) used for storing the available energy in the AC condensate. Condensate quantity was monitored for different load capacities such as 11 TR, 16.5 TR 22 TR and 33 TR and the number of occupants was also monitored on a daily basis (Table 3). It was found to vary from 9.36 to 21.1 L/hr. The condensate drain pipe was well insulated and the water temperature was found to be 16 ± 0.5 °C throughout the day. The condensate collected was stored in an insulated tank in order to avoid heat loss. This work studies the effect of a

Fig. 4. Working of PCM assisted PCU system. 5

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

Fig. 5. Experimental setup of Pre-Cooling Unit (PCU) for improved IAQ.

for energy recovery from AC condensate. 0.6 kg of C.O. was filled in a spiral copper coil with the help of a peristaltic pump. The spiral copper coil tube was of diameter 9.52 mm with 12 turns. Initial priming was done to remove the air gap inside the coil. Data logger was used for continuous monitoring of phase transfer phenomena with thermocouples inserted at different locations of the copper coil. Both the ends were completely sealed to avoid leakage. The freezing characteristics (Fig. 7) of C.O. were studied using AC condensate, with the temperature maintained at 16 ± 0.5 °C. In the experiment, the freezing temperature was 22.3 °C and the charging time was observed to be 2 h. Subsequent to charging, the discharge characteristics were studied in the PCU (Fig. 8). Air was passed over the spiral copper coil with a velocity of 2.5 m/s. Melting temperature of C.O. was recorded by a k type thermocouple and melting was found to be at 23.8 °C. Due to the inherent properties of C.O., the discharging rate was relatively much higher than expected. It clearly shows that C.O. has higher heat absorption capability in convection [29]. The maximum drop of air temperature was 2.1 °C observed for 15 min after that PCM starts melting. The complete discharge time of PCM was observed as 80 min.

Table 3 Condensate water quantity with respect to cooling capacity and number of occupants. Cooling Capacity (TR)

Number of occupants

Collected Condensate water (liters/hr)


30 60 100 30 60 100 60 100 60 100

9.36 12.4 13.5 10.1 13.2 16.5 13.9 18.2 15.6 21.1


22 33

PCM based PCU with AC condensate on improving IAQ. 2.4.2. Characterization studies of PCM (coconut oil) The phase change behavior of C.O., including melting temperature and enthalphy of fusion were measured using DSC analysis. Before conducting the test, the instrument was calibrated with the standard procedure as specified in the user manual. Single furnace DSC (DSC 6000) with heat flux measurement techniques was used for maintaining thermal balancing between the reference pan and the sample pan. The sample (5 mg C.O.) was cooled from room temperature to −10 °C and heated to 50 °C with a scanning heat rate of 0.5 °C/min under N2 atmosphere. DSC curve of the sample is given in Fig. 6. The analysis shows the phase transition behavior of solidification and melting. During the exothermic reaction, the freezing peak is observed at 19.77 °C. This is followed by melting, which starts at 22.35 °C (onset) and proceeds with a distinct endothermic peak at 22.42 °C. After that, heat flow reduces, reaching complete melting at 24.73 °C. The results showed that latent heat of fusion during the solidification (ΔH)solid and melting (ΔH)melt is 95.59 J/g and 131.10 J/g, respectively. The thermal conductivity of C.O. and condensate water was estimated using a KD2 pro Thermal properties meter. KS-1, 6 cm sensor with a measurement range of 0.02–2.0 W/mK was used for measurement. C.O. and condensate water with a quantity of 50 ml are prepared by standard operating procedure and taken for the measurement. The thermal conductivity of C.O. and condensate water was found to be 0.142 W/mK and 0.651 W/mK respectively. Thermogravimetric analysis was used for studying the thermal stability and the rate of decomposition of C.O. Mass reduction was observed to be only 0.61% up to a temperature of 100 °C.

3. Result and discussion As discussed in an earlier section, this study involves assessment of IAQ in terms of CO2 concentration in the base case and the modified

2.4.3. Charging and discharging studies of coconut oil Experimental work was conducted to ascertain the potential of C.O.

Fig. 6. Differential scanning calorimetry crystallization curves of coconut oil. 6

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

where H & W are Height (m) & Weight (kg) of the occupant. Height and weight of the occupants were collected during the study. In this study, the estimation of ventilation rates using CO2 as a tracer gas are based on a fully mixed mass balance model as described by Stuart Batterman [40]. (3)

VdCdt = E + QCR − QC

V = Room volume (m3); C = CO2 concentration in the room (mg/m3); CR = CO2 concentration in outdoor air (mg/m3); Q = flow rate of outdoor air (m3/hr). E = CO2 emission rate of indoor sources (mg/hr); E is calculated as n GP, where n = number of occupants in the laboratory, and GP = CO2 generation rate per person (L/hr). Occupancy rate inside the laboratories is found to vary randomly, and therefore the numerical study is considered to be transient. Hence, the transient mass balance method was used to determine the indoor CO2 concentration, where occupancy and CO2 concentration were observed at an interval of 1 h. Transient mass balance equation is obtained by taking an appropriate numerical solution of Equation (3).

Fig. 7. Freezing characteristics of coconut oil.

⎛ 6 × 10 4nt Gr Ct + 1 = ⎜ ⎜ Q 1 − exp −Q VΔt ⎝


( )}

⎞ −Q ⎞ + CR ⎟ + (Ct − CR)exp ⎛ ⎝ VΔt ⎠ ⎟ ⎠


where, Ct = observed CO2 concentration (ppm) at time t; nt = number of occupants observed at time t; Gr = average CO2 emission rate (L/min/person) Q = replacement air flow rate (m3/hr); V = volume of the space (m3); CR = replacement air CO2 concentration (ppm); Δt = time interval between observation of CO2 and occupancy (hr) and Ct+1 = Observed CO2 concentration after the time intervals of Δt. From equation (4), total CO2 generation rate inside the laboratories was found under transient condition, and the results fit with the experimental values with a highest error percentage of 8%. During the measurements, it was observed that the CO2 concentration crossed 1000 ppm in 3 h after the AC was turned on, with 35–40 occupants. There are two contributing factors for the rise in CO2 concentration inside the labs viz., the total number of occupants and air change per hour. The entire unit was designed based on constant air volume method with common ducts serving all the four labs. The return air CO2 concentration corresponding to the total occupants hosted is presented in Fig. 9 where it was observed that return air CO2 concentration crossed the permissible limit with 40 occupants. This is due to insufficient fresh air change inside the conditioned space. In the afternoon, an increase in the number of occupants resulted in rise of CO2 concentration. Table 4 shows the numerical and experimental indoor CO2 concentrations in the four laboratories. Initially, the number of occupants gradually increases every hour. It can be observed that the CO2 concentration exceeded the 1000 ppm limit at around 11.30 a.m. and continued to be above 1000 ppm during lunch interval (12.30 p.m.–1.30 p.m.). In the afternoon, there was a continuous increase in CO2 concentration due to a rise in the number of occupants. The measured CO2 concentration was validated with estimated values using numerical equation under transient occupancy. The minimum and the maximum error percentage was 0.5% and 8% respectively across all four labs. Chi-square test indicates that the experimental CO2 (ppm) and the numerical CO2 (ppm) results compared well with the goodness of fit i.e. 90% for Computer Lab I&II.

Fig. 8. Melting characteristics of coconut oil.

case. Numerical and experimental analysis of CO2 concentration inside the air-conditioned labs were carried out and the results are discussed below. 3.1. Experimental and numerical analysis of CO2 concentration in base case 3.1.1. Numerical analysis of CO2 concentration in base case The CO2 concentration is mainly dependent on two factors, namely the number of occupants and the metabolic activity. CO2 generation rate (Gr) inside the laboratory was calculated based on equation (1), stated in ASHRAE Fundamentals Handbook and ASTM D6245 [40]. Based on the occupants physical parameters (weight and height), standard metabolic release rate for sitting and typing activity is assumed as 1.5 [41], and by substituting all the values in equation (1), CO2 generation rate was evaluated.

Gr =

(60)*(0.83)*(0.00276)*S *MET {(0.23)*(0.83) + (0.77)}


where Gr - CO2 generation rate (L/min/person), MET - Metabolic Rate and S is DuBois surface area (m2) which is estimated by the equation.

S = 0.20247H 0.725W 0.425

(2) 7

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

Table 5 Details of room dimensions and occupants in the Computer Lab I. Region


Computer Lab Length (m) Width (m) Height (m) Room Volume (m3)

11.58 6.07 2.48 174.32

Working Table Table height (m) Number of table

0.75 14

Occupants Number of Occupants Occupant Breathing Zone from ground level (m)

42 1.1

Two months of consecutive measurements and recordings show that the CO2 concentration exceeds 1000 ppm at around 11.00 a.m. and 3.00 p.m. respectively. Based on the observations, it can be inferred that in order to reduce the indoor CO2 level, fresh outdoor air supply is required at least twice a day, especially, around 11.00 p.m. and 3.00 p.m. The outdoor air was pre-cooled by the PCU installed in one of the windows of Computer Lab I.

Fig. 9. Return air CO2 concentration inside the AHU.

3.1.2. Indoor air quality assessment in computer lab I (base case) CO2 concentration, air velocity, indoor temperature and humidity were investigated in Computer Lab I for both base and modified case. The details of Computer Lab 1 are listed in Table 5. The Computer Lab I houses 42 computer systems spread over 7 rows. CO2 concentration was noted during base case and modified case operation close to each computer totalling to 42 points. I In the base case, the CO2 concentration crossed 1000 ppm between 11.00 a.m. and 11.30 a.m. with 12 occupants. Subsequently, it rose continuously and stabilized around 1600 ppm–1700 ppm towards evening. Data reliability test was done using SPSS in order to estimate the internal consistency of measured CO2 concentration in Computer Lab I during the period 10.30 a.m.–11.30 a.m. The data of CO2 concentration measured on four different working days was taken for the reliability test. The Cronbach's Alpha (α) value obtained from this test was 0.986 which is greater than 0.8. Hence, the measured data values were reliable for an occupancy of 12 members during the period 10.30 a.m.–11.30 a.m. Air velocity is an important factor for maintaining thermal comfort. The air velocity should be maintained in winter and summer conditions at 0.15 m/s and 0.25 m/s respectively, according to ASHRAE standard62.1. However, the average air velocity in the Computer Lab I was between 0.02 and 0.09 m/s during the class hours. Mostly, air infiltration and fresh air admission happens due to the intermittent opening of the doors/windows. The average air infiltration rate within Computer Lab I was found to be 0.104 kg/hr with an effective leakage area of 121 cm2 using the equation reported by A. Bhatia [42]. Air velocity was also observed to be insufficient in the conditioning space.

3.2. Assessment of IAQ in computer lab I with PCU (modified case) IAQ assessment was conducted in June, where the outside air temperature and the CO2 concentration were found to be 31 ± 0.5 °C and 390 ± 2 ppm respectively. Initially, the system was run as the base case (PCU unit was not connected), and CO2 concentration was found to saturate at 1500–1600 ppm around 11.30 a.m. After that, the PCU unit was started with admission of pre-cooled air with a CO2 concentration of 392 ppm. In this study, AWhx and 0.6 Kg of PCM were used to precool the fresh air. 170 m3/h of fresh pre-cooled air was admitted based on ASHRAE standard- 62.1, for people related pollutants which insists 15 cfm/person. In this study, the six occupants requires 90 cfm which was rounded off to the nearest standard size of 100 cfm (i.e.170 m3/hr). Fig. 10 shows the average drop in air temperature when the condensate temperature inside the PCU is 16 ± 0.5 °C. It clearly shows that air temperature reduction of 3.5–4 °C is achieved by both AWhx and PCM unit. The final air temperature after the PCU was observed at 27 ± 0.2 °C, which is circulated into the conditioned space. With admission of fresh pre-cooled air (170 m3/hr) through PCU, the minimum CO2 concentration attained was 420 ppm and maximum being 1580 ppm. Average reduction of CO2 concentration (centre of the lab) was observed to be 5%. This is due to inadequate supply of fresh air with respect to the actual volume of room air. However, measurements

Table 4 Numerical and experimental indoor CO2 concentrations in the four laboratories. Time

8.30a.m. 9.30a.m. 10.30a.m. 11.30a.m. 12.30p.m. 1.30p.m. 2.30p.m. 3.30p.m. 4.30p.m. 5.30p.m.

Computer lab I

Computer lab II

Computer lab III

Computer lab IV

Number of Occupants

Num CO2 Cons.

Exp CO2 Cons.

Number of Occupants

Num CO2 Cons.

Exp CO2 Cons.

Number of Occupants

Num CO2 Cons.

Exp CO2 Cons.

Number of Occupants

Num CO2 Cons.

Exp CO2 Cons.

1 3 6 11 14 10 21 27 24 15

469 528 746 1014 1195 1298 1585 1880 1989 1747

499 553 757 1003 1204 1230 1636 1894 2030 1761

1 5 13 20 18 17 18 20 18 13

472 575 871 1161 1261 1431 1680 1918 2013 1780

505 597 880 1165 1294 1315 1651 1908 2020 1739

1 5 13 20 18 17 18 20 18 13

475 570 755 971 1136 1282 1584 1825 1877 1764

510 597 825 1056 1223 1240 1623 1878 1999 1750

1 11 14 18 19 24 27 31 32 14

481 610 802 1029 1201 1389 1614 1911 1970 1754

510 626 885 1094 1294 1410 1725 1962 2048 1725


Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

A 44 m3 controlled room was built in SOLIDWORKS and simulation was done using SOLIDWORKS flow simulation. The controlled room of dimension (44 m3) 3 m length, 6.1 m width and 2.4 m height was considered for analysis (Fig. 12). The curtain is located at 3 m from the front wall. The structured quadrilateral mesh was given as global mesh in the computational domain. Mesh refinement was specified in the computational domain in order to avoid the sharp edges and increase the mesh quality. For a different level of refinement, the obtained cell counts grid elements of the computational domain with sizes of 8245301, 14503032, 17918705 and 94562613 were used for carring out grid independency test for a simulation duration of 5 min. It is found that the grid element size of 8245301 and 94562613 has less than 5% error. In order to reduce the computational time, the intermediate grid size of 17918705 cells was chosen for the simulation study. Pressure based solver and SIMPLE algorithm was employed to calculate the flow parameters in the fluid region using the time intervals of n and (n+1) [43]. The modified k-ε two-equation turbulence model with a damping function [47] was considered to predict heat transfer and fluid flow characteristics. This model coupled to a unique Two-Scale wall function treatment which corrects wall shear stress and heat flux from the fluid to the wall. In SOLIDWORKS flow simulation, the goals option was used for convergence control. In this study, global and point goal options were given in order to calculate the physical parameters in the entire computational domain and the user-specified points (point parameters). In this study, the obtained convergence criterion was 10−4 for each time step of 0.01s, which corresponds to 10998 iteration and simulation time of 142 s. The heat loads considered in the controlled room were (i) occupants (60 W/m2/person) and (ii) computers (18.7 W/m2/computer) as per ASHRAE standard 55, 2017 [48,49]. Fresh air ventilation of 15 cfm per person (ASHRAE standard for people related pollutants) was considered for IAQ analysis [50]. SOLIDWORKS flow simulation offers tracer gas studies with appropriate source input of mass or volume fraction, mass flow rate and volumetric mass flow rate. In this study, CO2 volume fraction was given as input to evaluate the indoor CO2 distribution. In the base case, for creating the simulation model, it is assumed that total infiltration quantity (0.104 m3/hr) into the controlled space is through PCU, whose outlet pipe diameter is 0.152 m (Cross section area of 0.0181 m2), which is equivalent to a flow velocity of 0.0015 m/s. Further, in the modified case, fresh air velocity was considered at 2.5 m/s. The air temperature after the PCU was always maintained at

Fig. 10. Air temperature difference across the pre-cooling unit with condensate temperature of 16 ± 0.5 °C.

indicate that in about 10% of the total space, closer to the PCU CO2 was is less than 1000 ppm. The pre-cooled fresh air from PCU has an effect on the first two rows. After that, the air is bypassed by the return air duct and enter to AHU. Hence, the temperature and CO2 concentration were noted in the first two rows of the computer Lab I. Reduction in CO2 concentration was observed, confirming admission of fresh outdoor air through PCU. The CO2 concentration dropped to below 1000 ppm within a duration of less than 6 min. It was run for a total duration of 15 min and the CO2 concentration stabilized at around 760 ppm up to 3 m from PCU which covers the first two rows. The occupants reported better comfort as a result of removal of stale air, in the first two rows. The CO2 concentration with and without planned fresh air change is shown in Fig. 11. However, with supply of 170 m3/hr of fresh precooled air from PCU, the energy consumption increased by 0.5 kWh, whereas without PCU, fresh air admitted at ambient temperature of 32 °C into the air conditioned space would result in an increase in energy consumption by 1.8 kWh. Hence, in order to provide a better environment for occupants with the help of PCU, the seating position was scheduled in the first two rows. In the subsequent simulation studies, a curtain was introduced in the existing room which separates the first two rows from others creating a controlled volume (44 m3). An attempt was made to study the reduction in CO2 concentration, an increase in indoor air temperature and air velocity in the controlled space with PCU. 3.3. Simulation analysis of controlled room size with curtain 3.3.1. SOLIDWORKS flow simulation SOLIDWORKS flow simulation software is an integration of modeling and analysis tool used for computing fluid flows and heat transfer analysis with a proven CFD technology [43]. It includes three processors, namely pre-processor (specifying data for the calculation), coprocessor (monitoring and controlling the calculations) and post-processor (viewing the results) used for computation. Recently researchers are using SOLIDWORKS flow simulation as it helps analyze a complex domain with the minimum iteration time. As of now, SOLIDWORKS flow simulation was being used in heat transfer studies and models were validated with experimental work [44–46]. However, in the present study attempt is made to investigate IAQ parameters inside airconditioned built environment with SOLIDWORKS flow simulation.

Fig. 11. CO2 concentration with and without planned fresh air change in the first two rows. 9

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

Fig. 12. Layout of controlled room with a curtain in which the simulation was performed.

28 ± 0.3 °C, which is circulated to the conditioned space. Two supply diffusers are considered for the supply of 950 m3/hr air at an average temperature of 23.2 °C. The average supply diffuser concentration was maintained in the range of 1003 ppm–1020 ppm throughout the simulation. Two return diffusers operating at atmospheric pressure were used as a return air duct. The simulation running time was considered as 600 s. The indoor air quality parameters were observed at seven different points (S1–S7) located at a height of 1.2 m from the floor, shown in Fig. 12. The detailed boundary condition of the controlled room is given in Table 6.

average measured indoor air temperature for the base case and the modified case was 24.8 °C and 25.6 °C respectively. Simulated temperatures were validated with experimentation and the variation was found to be 5.2% and 6.5% for base case and modified case respectively. In base case, the measured air velocity was varying from 0.04 to 0.08 m/s in each points of S1 to S7. In the modified case, the air velocity was observed to be quite high at S2 (0.8 m/s) and S5 (0.5 m/s) due to fresh air impingement. However, the average air velocity was observed to be 0.22 m/s for modified case which is close to the ASHRAE standard of 0.25 m/s.

3.3.2. Simulation model validation The temperature and the velocity distributions for the base and modified case are illustrated in Fig. 13 (a) and Fig. 13(b). The indoor air temperature was almost the same except at point S5 where fresh air is admitted. The simulated average indoor air temperature for the base and the modified case were 23.8 °C and 24.32 °C respectively. Similarly, the average room air velocity was in the range of 0.11 m/s to 0.28 m/s for the base and the modified case. S1, S2, S3, S4, S5, S6 and S7 were the measuring points from the front wall where the fresh air supply duct is placed. The middle point (S4) denotes the pathway that separates the desks on the right and left sides of the room. In the base case, the simulated CO2 concentration was in the range of 1400 ppm–1550 ppm for the first and second rows. The simulated CO2 concentration for the modified case was in the range of 400 ppm and 900 ppm in the first two rows. No predominate change in relative humidity was observed in the simulation results for the base and modified case. The physical curtain arrangement was made inside Computer Lab I and experiments were carried out for base and modified case in order to validate the simulation model. PCU system was operated for 600 s and measurement were taken at seven points (S1 to S7) as mentioned in Fig. 12. The indoor CO2 concentration and air temperature obtained from the simulation was compared with the experimental measurements and presented in Fig. 14 (a) and Fig. 14(b). It can be observed that the minimum and maximum error percentage between simulated and measured CO2 concentration was 1.5% and 8.2% respectively. The

Table 6 Boundary condition of controlled room size with curtain. Solver

Pressure based transient


Modified k-ɛ

Initial conditions: Air temperature = 25 °C Air humidity = 50% Air velocity = 0 m/s (air infiltration was assumed to be zero) CO2 mass fraction = 0.001573 Wall conditions: Adiabatic Boundary Conditions at Supply diffuser: Inlet air temperature = 23.2 °C Inlet air RH = 48% Inlet air velocity = 3.6 m/s Inlet air CO2 mass fraction at 10.30 a.m. = 0.001573 Inlet air CO2 mass fraction at 11.30 a.m. = 0.001778


Base case

Modified case

Boundary Conditions: - Air infiltration is considered Air infiltration velocity = 0.0015 m/s Temperature of infiltration air = 32 °C RH of infiltration air = 50% CO2 mass fraction of infiltration air = 0.0005714

Boundary Conditions: -Fresh air admission is considered Air velocity = 2.5 m/s Air temperature = 28.3 °C Air RH = 60% CO2 mass fraction = 0.0005714

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

Fig. 13a. Temperature and velocity distribution of base case, simulation time 600 s.

and malls, are often designed with minimal fresh air ventilation and the CO2 level often exceeds the permissible limit (1000 ppm). In such buildings, this system can be mounted in fresh air ducts to pre-cool the admitted fresh air depending on demand. This apart, the use of Variable Refrigerant Volume (VRV) systems has risen sharply for medium cooling capacity (up to 70 TR) in buildings, and the provision for fresh air ventilation is not considered in VRV systems. In such cases, the installation of condensate assisted PCU can improve IAQ with considerable energy savings. The collected condensate quantity of 12.5 L/hr at 16 ± 0.5 °C and 0.6 Kg of PCM is more than adequate to pre-cool 170 m3/hr of fresh air, with continuous running for 15 min. Based on this, the quantity of PCM and the fresh air requirement can be estimated

Simulation and experimental studies confirmed that supplying 170 m3/hr fresh air could maintain the CO2 concentration for a controlled volume of 44 m3. It could provide a good indoor environment for six occupants seated in the first two rows. Based on the number of occupants, the curtain could be positioned for effective fresh air ventilation. Total energy consumption increased by 0.5 kWh due to the admission of fresh air through PCU at 28 ± 0.3 °C. However, during the running of PCU, the occupant thermal comfort is not greatly changed. Multiple PCUs can be installed at different places that could help to provide better indoor air quality in terms of CO2 accumulation, depending on other factors such as availability of condensation etc. High occupancy buildings, especially theatres, shopping complexes 11

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

Fig. 13b. Temperature and velocity distribution of modified case, simulation time 600 s.

for the design of PCU systems for large buildings. 4. Conclusion

AC condensate has potential to pre-cool the ambient air admitted into a conditioned space which in turn improves the indoor air quality in an energy efficient manner. The following conclusions were drawn on the basis of experiments and simulation results.

• Indoor CO •

2 concentration evaluated numerically for the four laboratories compared with experimental measurements with an error percentage of 0.5%–8%. Indoor air quality was found to be poor in terms of CO2 concentration which is higher than the permissible limit of ASHRAE standard 62.1. Pre-cooling unit (PCU) was designed, fabricated and installed in the Computer Lab I for improving indoor air quality. With 15 min


operation of PCU, the indoor CO2 concentration fell less than 1000 ppm within 6 min and stabilized at around 760 ppm up to a distance 3 m from PCU. The average temperature reduction of the pre-cooled ambient air was 3.75 °C, 2.99 °C and 2.13 °C at a distance of 1, 2 and 3 feet from the PCU respectively. Experimental results indicate that the admission of pre-cooled ambient air through PCU provides better IAQ up to two rows (3 m from PCU) inside the laboratories, considering the fact that the admitted fresh air quantity was only 5% of actual room volume. This was further confirmed by a simulation study of a reduced control room. In the controlled room simulation, CO2 concentration for the base case was in the range of 1400 ppm–1550 ppm in row one and two respectively. In the modified case with the admission of outdoor air, the CO2 concentration dropped to 400 ppm and 900 ppm for the first two rows.

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

standard of 0.25 m/s. and experimental studies confirmed that supplying 170 m3/hr fresh air could maintain the CO2 concentration with a controlled volume of 44 m3. It could provide a good indoor environment for six occupants seated in the first two rows. The average increase in room temperature observed by experimentation was 0.6–0.8 °C with an admission of 170 m3/hr of fresh air. This translates to an increase in energy consumption of 0.5 kWh due to the admission of pre-cooled ambient air at 28 ± 0.3 °C for a duration of 1 h, whereas without PCU, fresh air admitted at an ambient temperature of 32 °C into the air conditioned space, results in an increase in energy consumption by 1.8 kWh.

• Simulation •

Acknowledgments The authors acknowledge the National Institute of Technology, Tiruchirappalli, Tamil Nadu, India, for the development of experimental setup. We express our sincere thanks to Indian patent office for publication of this invention in the patent office journal (201741039073 A, dated 11/01/2019). We would also like to thank the Computer Centre for permitting adjustment/closing of the AC dampers for simulating a room air conditioner and carrying out experiments on a live running AC plant. We also like to place on record the cooperation extended by the students in completing the IAQ survey.

Fig. 14a. Simulated and measured CO2 concentration for the base case and modified case.

References [1] G.J. van Oldenborgh, S. Philip, S. Kew, M. van Weele, P. Uhe, F. Otto, R. Singh, I. Pal, K. AchutaRao, Extreme heat in India and anthropogenic climate change, Nat. Hazards Earth Syst. Sci. Discuss. (2017) 1–23, [2] L.T. Wong, K.W. Mui, An energy performance assessment for indoor environmental quality (IEQ) acceptance in air-conditioned offices, Energy Convers. Manag. 50 (2009) 1362–1367, [3] L. Dias Pereira, D. Raimondo, S.P. Corgnati, M. Gameiro da Silva, Assessment of indoor air quality and thermal comfort in Portuguese secondary classrooms: methodology and results, Build. Environ. 81 (2014) 69–80, 1016/j.buildenv.2014.06.008. [4] K.D. Putra, E. Djunaedy, A. Bimaridi, M.R. Kirom, Assessment of outside air supply for split AC system Part B: experiment, Procedia Eng. 170 (2017) 255–260, https:// [5] ASHRAE, Apply More Advanced Ventilation Approaches, Indoor Air Quality Guide, ASHRAE, USA, 2009, pp. 164–165. [6] A. Persily, Indoor Carbon Dioxide Concentrations in Ventilation and Indoor Air Quality Standards vol 3, (2016). [7] D.A. Coley, R. Greeves, B.K. Saxby, The effect of low ventilation rates on the cognitive function of a primary school class, Int. J. Vent. 6 (2007) 107–112, https:// [8] A. Riham Jaber, M. Dejan, U. Marcella, The effect of indoor temperature and CO2 levels on cognitive performance of adult females in a university building in Saudi arabia, Energy Procedia 122 (2017) 451–456, 2017.07.378. [9] J.G. Allen, P. MacNaughton, U. Satish, S. Santanam, J. Vallarino, J.D. Spengler, Associations of cognitive function scores with carbon dioxide, ventilation, and volatile organic compound exposures in office workers: a controlled exposure study of green and conventional office environments, Environ. Health Perspect. 124 (2016) 805–812, [10] Y.T. Prairie, C.M. Duarte, Direct and indirect metabolic CO2 release by humanity, Biogeosci. Discuss. 3 (2006) 1781–1789, [11] T. Lu, A. Knuutila, M. Viljanen, X. Lu, A novel methodology for estimating space air change rates and occupant CO2generation rates from measurements in mechanically-ventilated buildings, Build. Environ. 45 (2010) 1161–1172, 10.1016/j.buildenv.2009.10.024. [12] D.J. Clements-Croome, H.B. Awbi, Z. Bakó-Biró, N. Kochhar, M. Williams, Ventilation rates in schools, Build. Environ. 43 (2008) 362–367, 1016/j.buildenv.2006.03.018. [13] S. Vilčeková, P. Kapalo, Ľ. Mečiarová, E.K. Burdová, V. Imreczeová, Investigation of indoor environment quality in classroom - case study, Procedia Eng. 190 (2017) 496–503, [14] M.O. Ng, M. Qu, P. Zheng, Z. Li, Y. Hang, CO2-based demand controlled ventilation under new ASHRAE Standard 62.1-2010: a case study for a gymnasium of an elementary school at West Lafayette, Indiana, Energy Build. 43 (2011) 3216–3225, [15] L.A. Wallace, S.J. Emmerich, C. Howard-Reed, Continuous measurements of air change rates in an occupied house for 1 year: the effect of temperature, wind, fans, and windows, J. Expo. Anal. Environ. Epidemiol. 12 (2002) 296–306, https://doi.

Fig. 14b. Simulated and measured air temperature for the base case and modified case.

• SOLIDWORKS flow simulation model for the controlled volume was

• •

analyzed wherein temperature and velocity distributions were computed. The average air velocity and the temperature distribution in the breathing zone were found to be 0.22 m/s and 23.9 °C, 0.16 m/s and 23.8 °C at 1.2 m and 3 m respectively from the PCU. In the actual experimental work, the velocity and the temperature distributions in the breathing zone were 0.05 m/s and 25.1 °C, 0.08 m/s and 24.7 °C at 1.2 m and 3 m respectively from the PCU. The simulated average indoor air temperature for the base and the modified case were 23.8 °C and 24.32 °C respectively. Similarly, the average room air velocity was in the range of 0.11 m/s to 0.28 m/s for the base and the modified case. Physical curtain arrangement was used for validating the simulation model. The minimum and maximum error percentage between simulated and measured CO2 concentration was found to be 2% and 6% for base case, and 1.5% and 8.2% for modified case. Similarly, the variation in measured and simulated temperatures was 5.2% for base case and 6.5% for modified case. In the modified case, the average air velocity was found to be 0.28 m/s in simulation and 0.22 m/s in the experiments, which are very close to ASHRAE 13

Building and Environment 163 (2019) 106321

D. Palanisamy and B.K. Ayalur

buildenv.2013.11.001. [33] R.K. Jaafar, E.E. Khalil, T.M. Abou-Deif, Numerical investigations of indoor air quality inside Al-haram mosque in makkah, Procedia Eng. 205 (2017) 4179–4186, [34] N. Szczepanik-ścisło, Measurements and simulation of CO2 concentration in a bedroom of a passive house, Czas. Tech. 9 (2018) 163–179, 4467/2353737XCT.18.139.8978. [35] P. Batog, M. Badura, Dynamic of changes in carbon dioxide concentration in bedrooms, Procedia Eng. 57 (2013) 175–182, 04.025. [36] T. Van Hooff, B. Blocken, CFD evaluation of natural ventilation of indoor environments by the concentration decay method: CO2gas dispersion from a semienclosed stadium, Build. Environ. 61 (2013) 1–17, buildenv.2012.11.021. [37] B. Yan, E. Long, X. Meng, Y. Zhang, D. Hou, X. Du, Influence of user behavior on unsatisfactory indoor thermal environment, Energy Convers. Manag. 86 (2014) 1–7, [38] R.L. Hedrick, J.K. Mcfarland, M.G. Apte, D.C. Bixby, G. Brunner, M.P. Buttner, L.A. Damiano, R.A. Danks, F.J. Fisher, F.M. Gallo, J.R. Girman, E.P. Howard, R.L. Howard, W.M. Lawton, C.R. Magee, C.A. Marbery, J.P. Mcclendon, A.S. Muliawan, D.B. Meyers, L.J. Rogers, D.P. Rothstein, J.K. Smith, A.J. Spata, F. Myers, J.C. Peterson, Ashrae 62 (1) (2010) 2010. [39] A. Wang, Y. Zhang, Y. Sun, X. Wang, Experimental study of ventilation effectiveness and air velocity distribution in an aircraft cabin mockup, Build. Environ. 43 (2008) 337–343, [40] S. Batterman, Review and extension of CO2-based methods to determine ventilation rates with application to school classrooms, Int. J. Environ. Res. Public Health 14 (2017) 1–22, [41] M. Mansoubi, N. Pearson, S.A. Clemes, S.J.H. Biddle, D.H. Bodicoat, K. Tolfrey, C.L. Edwardson, T. Yates, Energy expenditure during common sitting and standing tasks: examining the 1.5 MET definition of sedentary behaviour, BMC Public Health 15 (2015), [42] I.A. Bhatia, HVAC Nat. Vent. Infiltration (2012) 253. [43] A. Sobachkin, G. Dumnov, Numerical Basis of CAD-Embedded CFD, vol 2013, NAFEMS World Congr, 2014, pp. 1–20. [44] E. Bellos, C. Tzivanidis, K.A. Antonopoulos, G. Gkinis, Thermal enhancement of solar parabolic trough collectors by using nanofluids and converging-diverging absorber tube, Renew. Energy 94 (2016) 213–222, renene.2016.03.062. [45] E. Bellos, D. Korres, C. Tzivanidis, K.A. Antonopoulos, Design, simulation and optimization of a compound parabolic collector, Sustain. Energy Technol. Assess. 16 (2016) 53–63, [46] A.A. Shrikant, R. Sivakumar, M. Vivekanandan, Comparison of shell and tube heat exchanger using theoretical methods , HTRI , ASPEN and SOLIDWORKS simulation softwares, Int. J. Eng. Res. Afr. 6 (2016) 99–107. [47] S.V. Patankar, PatankarNumericalHeatTransferandFluidFlow, (1980). [48] E.A. Arens, F.S. Bauman, J.R. Breckenridge, R.E. Chase, P.O. Fanger, R.R. Gonzalez, D. Int-hout, J. Klassen, E.A. Mccullough, L.W. Nelson, B.W. Olesen, G. Reeves, M.K. Anderson, L.W. Burgett, M.E. Dillon, D.L. Geistert, Thermal Environmental Conditions for Human Occupancy, (1995), p. 1995. [49] R. Parsons, ASHRAE Fundamentals Handbook, SI Edition, 2001, pp. 8–13. [50] D.L. Johnson, R.A. Lynch, E.L. Floyd, J. Wang, J.N. Bartels, Indoor air quality in classrooms: environmental measures and effective ventilation rate modeling in urban elementary schools, Build. Environ. 136 (2018) 185–197, 1016/j.buildenv.2018.03.040.

org/10.1038/sj.jea.7500229. [16] J. Gao, P. Wargocki, Y. Wang, Ventilation system type, classroom environmental quality and pupils' perceptions and symptoms, Build. Environ. 75 (2014) 46–57, [17] K. Siriwardhena, S. Ranathunga, Air-Conditioners Condensate Recovery, (2010). [18] A. Magrini, L. Cattani, M. Cartesegna, L. Magnani, Integrated systems for air conditioning and production of drinking water-preliminary considerations, Energy Procedia 75 (2015) 1659–1665, [19] N. Liu, Z. Li, The feasibility on the case that the air conditioning condensate water is used as the make-up water of cooling tower, Procedia Eng. 205 (2017) 3557–3562, [20] S. Algarni, C.A. Saleel, M.A. Mujeebu, Air-conditioning condensate recovery and applications—current developments and challenges ahead, Sustain. Cities Soc. 37 (2018) 263–274, [21] D. Licina, C. Sekhar, Energy and water conservation from air handling unit condensate in hot and humid climates, Energy Build. 45 (2012) 257–263, https://doi. org/10.1016/j.enbuild.2011.11.016. [22] L. Sisco, S. Monzer, N. Farajalla, I. Bashour, I.P. Saoud, Roof top gardens as a means to use recycled waste and A/C condensate and reduce temperature variation in buildings, Build. Environ. 117 (2017) 127–134, buildenv.2017.02.025. [23] N.I. Ibrahim, A.A. Al-Farayedhi, P. Gandhidasan, Experimental investigation of a vapor compression system with condenser air pre-cooling by condensate, Appl. Therm. Eng. 110 (2017) 1255–1263, 2016.09.042. [24] Y. Cheng, S. Zhang, C. Huan, M.O. Oladokun, Z. Lin, Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving, Build. Environ. 147 (2019) 11–22, [25] K. Hassouneh, A. Alshboul, A. Al-Salaymeh, Influence of windows on the energy balance of apartment buildings in Amman, Energy Convers. Manag. 51 (2010) 1583–1591, [26] M. Alam, J. Sanjayan, P.X.W. Zou, S. Ramakrishnan, J. Wilson, Evaluating the passive and free cooling application methods of phase change materials in residential buildings: a comparative study, Energy Build. 148 (2017) 238–256, [27] E. Osterman, V. Butala, U. Stritih, PCM thermal storage system for “free” heating and cooling of buildings, Energy Build. 106 (2015) 125–133, 1016/j.enbuild.2015.04.012. [28] K. Biswas, R. Abhari, Low-cost phase change material as an energy storage medium in building envelopes: experimental and numerical analyses, Energy Convers. Manag. 88 (2014) 1020–1031, [29] W.A. Putri, Z. Fahmi, I.M. Sutjahja, D. Kurnia, S. Wonorahardjo, Thermophysical parameters of coconut oil and its potential application as the thermal energy storage system in Indonesia, J. Phys. Conf. Ser. 739 (2016), [30] S. Silva, A. Monteiro, M.A. Russo, J. Valente, C. Alves, T. Nunes, C. Pio, A.I. Miranda, Modelling indoor air quality: validation and sensitivity, Air Qual. Atmos. Health 10 (2017) 643–652, [31] N. Mahyuddin, H.B. Awbi, Modelling the Distribution of Exhaled Co2 in an Environmental Chamber, (2012), pp. 1–6, 2010.02836.x. [32] A. Bulińska, Z. Popiołek, Z. Buliński, Experimentally validated CFD analysis on sampling region determination of average indoor carbon dioxide concentration in occupied space, Build. Environ. 72 (2014) 319–331,