Cooling coil design improvement for HVAC energy savings and comfort enhancement

Transcription

1 Cooling coil design improvement for HVAC energy savings and comfort enhancement Vahid Vakiloroaya Keywords: Cooling coil, Design Optimization, Energy Saving, Comfort enhancement Abstract. In designing an energy-efficient HVAC system, several factors being to play an important role. Among several others, the performance of cooling coil which is embodied through its configuration, directly influence the performance of HVAC systems and should be considered to be crucial. This paper investigates and recommends design improvements of cooling coil geometry contributes for a central cooling system by using a simulation-optimisation approach. An actual central cooling plant of a commercial building in the hot and dry climate condition is used for experimentation and data collection. An algorithm was created in a transient system simulation program to predict the best design. Available experimental results were compared to predicted results to validate the model. Then different models of several new designs for cooling coil were constructed to evaluate the potential of design improvements. Afterwards, the computer model was used to predict how changes in cooling coil geometry would affect the building environment conditions and the energy consumption of the HVAC components. 1 Introduction The increasing consumption of energy in buildings on heating, ventilating and airconditioning (HVAC) systems has initiated a great deal of research aiming at energy savings. With the consolidation of the demand for human comfort, HVAC systems have become an unavoidable asset, accounting for almost 50% energy consumed in building and around 10-20% of total energy consumption in developed countries (Perez-Lombard et al. 2008) Because building cooling load varies with the time of the day, an HVAC system must be complemented with an optimum design scheme to reduce the energy consumption by keeping the process variables to their required set-

2 point efficiently in order to maintain comfort under any load conditions. One of the effective ways of achieving energy efficiency is to design cooling coil configurations properly that has motivated us to propose a design procedure which significantly lead to an overall reduction in HVAC energy consumption. Therefore, it is not surprising that the design of energy efficient HVAC components is receiving a lot of attention. (Jabardo et al. 2006) presented results from an investigation carried out with commercial air coils of 12.7 mm of tube diameter. They tested coils with different fin pitch and tube rows in order to determine their effect over the thermal performance. (Sekhar and Tan 2009) investigated the performance of an oversized coil at different conditions during the operation stage.the results showed that the humidifying performance of the oversized coil at the reduced loads during normal operation can be considerably enhanced by changing the effective surface area of the coil through a simple mainpulation of the effective number of rows. (Cai et al. 2004) derived a model for a cooling coil based on energyconservation and heat transfer principles. Catalogue fittings of published coil data and experiments on a centralized HVAC pilot plant were conducted and the results showed that the model can achieve good and accurate estimation over the entire operating ranges and thus the model can be used to handle real time information.however, no work has been mentioned to optimize the cooling coil geometry by using combined simulation of building dynamic behaviour with a detailed operational data of a real tested central HVAC system. The objective of this paper is to minimize the energy consumption of building cooling system by using the design improvements of cooling coil geometry contributes while satisfying human comfort and system dynamics. For this purpose, a realworld commercial building, located in a hot and dry climate region, together with its central cooling plant (CCP) is used for experimentation and data collection. The existing central cooling plant was tested continuously to obtain the operation parameters of system components under different conditions. In order to take into account the nonlinear, time varying and building-dependent dynamics of the CCP, a transient simulation software package, TRNSYS 16, is used to predict the CCP energy usage. The cooling coil model was developed and coded within the TRNSYS environment.on the basis of the TRNSYS codes and using the real test data, a simulation module for the central cooling plant is developed and embedded in the software. An optimization algorithm which uses an iterative redesign procedureis developed and implemented in the cooling coil module in order to calculate and select its optimum configuration.the simulation results are compared with the monitored data in order to analyze the performance and feasibility of the proposed method. To show the effect of proposed approach, the comfort condition index, predicted mean vote (PMV), is studied. 2 Methodology 2.1 Cooling Coil Model The central cooling plant which is installed in the building consist of one water cooled chiller, one cooling tower, one air handling unit (AHU), two chilled water

3 pumps and two condenser water pumps.in this section, a mathematical model is developed for the cooling coil of AHU in order to truly simulate the effects of its operation on the whole system performance: Qcc Fs Aa NrUoTm, (1) where Q cc is the cooling coil capacity, F s is the cooling coil core surface area parameter, A a is face area of the coil, N r is the number of rows in the cooling coil and U o and DT m are respectively the overall heat transfer coefficient based on outside surface area of the cooling coil and log-mean temperature difference of the cooling coil and are determined as: 1 Uo 1 A r f ho hi, (2) ( Tai Two ) ( Tao Twi ) Tm, (3) ( T ) ln ai Two ( Tao Twi ) where Q cc is the heat absorbed by the chilled water in the cooling coil tubes, A r is the ratio of outside surface area of the coil to inner surface of tubes, Ì s is the fin efficiency, h o and h i are respectively the heat transfer coefficient of the outer surface and inner surface of the cooling coil, T ai and T ao are respectivelly the air temperature entering and leaving the cooling coil and T wi and T wo are the water temperature entering and leaving the coil respectively. 2.2 Experimental Rig In order to obtain the system performance data under various operating, a real test data was conducted in one typical week in the summer. A desktop computer was interfaced with the CCP for monitoring the performance of system. Therefore a total 392 points of system power consumption and other variables were measured for each fifteen minutes period by the monitoring and data acquisition system. The building sensible and latent cooling load, are calculated from monitoring data. Indoor sensible loads are determine by assuming that they are exactly the same as the product of monitored zone air flow rate and the difference in the monitored temperature between the supply air and air zone. The building latent loads are calculated using the product of the fan air flow and the difference in supply and return humidity ratios. Both humidity ratios are determined through the monitored air temperature and relative humidity. Then these variables were stored and arranged in data base files in TRNSYS so that iteration can be performed automatically.

4 2.3 Optimization Algorithm The optimization problem is formulated through the determination of the optimum cooling coil configuration, objective function and constraints. The objective function is to determine of the overall power consumption of the whole system according with each cooling coil geometry selected by proposed algorithm. For the objective function, the hourly overall HVAC energy consumption P total,i is determined for each operation hour i, in response to the different cooling coil designs by using the developed TRNSYS model. Finally the summer energy consumption P total is summed up for all the operating hours. Consequently, the objective function can be explicitly established as follows: n n Ptotal Ptotal, i ( Pch, ipahu, i Pctf, i Pchp, i Pcwp, i ), (4) i1 i1 where N=2170 H ( ) for May, June, July, August and September (based on 14-h daily operation) and P total is the summer energy consumption of the CCP including energy usage of the chiller P ch,i, the AHU variable air volume (VAV) fan P ahu,i, the cooling tower fan P ctf,i, the chilled water pump P chp,i and the cooling tower pump P cwp,i. The ultimate goal is to optimize the cooling coil geometry in order to minimize the energy consumption of the CCP while satisfying human comfort, subject to system dynamics and other constraints. The parameters to be optimized are number of rows, number of tubes in a row, number of fins and coil dimension. An optimization algorithm is developed and implemented in cooling coil module in order to calculate and select its optimum configuration. The algorithm uses an iterative redesign procedure to solve the problem.in the iterative redesign procedure, a given design is evaluated in terms of the design requirements and if it is found to be unacceptable, the system is redesigned by varying the design parameters, keeping the conceptual design unchanged. This new design is again evaluated and the iterative process continued until a satisfactory design is obtained. The design procedure also depends on the operating conditions, which are monitored experimentally and used in the algorithm. In the cooling coil design problem, since the given requirements involve several parameters and thus many criteria for convergence, it is useful to focus on parameters that must be optimized (Vakiloroaya et al. 2011). These parameters may then be followed as iteration proceeds to stop the iteration when the desired results have been obtained.the design obtained at convergence must be evaluated to ensure that all the design requirements are satisfied. Redesign involves choosing different values of the design parameters in the problem. In general, there are two types of design requirements: physical limitation of parameters and interaction between components which are shown in Fig. 1 and listed as follows: Requirement 1. The cooling coil capacity Q cc must be more than the building cooling load Q b and less than the cooling capacity of the chiller Q ch : Qb Qcc Qch. (5)

5 Length Height Aspect Ratio = AR = No. of tubes per row = N = Height VTS Fig. 1. The geometry notation of a cooling coil Requirement 2.The supply air temperature T sup is restricted to avoid overcooling or becoming too humid inside the building as: 10 C Tsup 20 C. (6) Requirement 3. The cooling coil aspect ratio (AR) should be within its limitation: 1 AR 6. (7) Requirement 4.The comfort ranges for indoor air temperature T room and relative humidity RH during occupied periods are given respectively: 20 C T 27 room C, 40% RH 60%. (8) 2.4 Model Verification The simulation is run with a time interval of 15 minutes that is equal to the monitoring time step in the CCP real test process. In order to verify the appropriateness of using the estimation values obtained by the simulation, it is important to validate the accuracy of the models under various operational conditions.the integrated simulation tool was validated by comparing predicted and measured power consumption of the chiller for the first week of July during which the chiller operated continuously from 8 a.m. to 10 p.m. As Fig. 2 illustrates, the model predicts quite well the variation in the chiller electric demand over the operating periods.

6 Fig. 2. The geometry notation of a cooling coil 3 Study Description The simulation object is a real-world commercial building, located in a hot and dry climate region, together with its central cooling plant.the gross floor area of the building is 2500 square meters and the usable floor area is 1700 square meters. The building height is 3 meters. The building is compliant with the requirements of the ANSI/ASHRAEStandard Theinternal cooling loads are selected based on the method given in ASHRAE Fundamentals. The weather data that drives the simulation in the project is based on a Typical Meteorological Year (TMY). The chiller has two screw compressors each with a nominal capacity of 175 kw and uses refrigerant R407C. The chiller model included a subroutine to evaluate the thermodynamic properties of the R407C. The temperature of the supply and return chilled water are respectively designed at 7 C and 12 C. The chiller coefficient of performance (COP) at design condition is 3.4. The chiller can operate down to about 10% of its rated full load capacity via a modulating slide valve in the compressor. The design air flow rate and the electric power input of variable speed cooling tower fan at maximum air flow rate are m 3 /h and 1.5 kw respectively. The design air flow rate of the air handling unit with variable air volume fan is m 3 /h and its rated power input is 12.8 kw. The design water flow rate and electric power of each chilled water pump is 41m 3 /h and 1.7 kw respectively. The design water flow of each condenser water pump is 50 m 3 /h and their electric power is 2.3 kw. All circulator pumps operate at constant speed.the simulation information flow diagram for all mentioned components is shown in Fig. 3.

7 Fig.3. The simulation information flow diagram in TRNSYS work space 4 Results and Conclusio TRNSYS is run to obtain component-wise energy analysis and the indoor comfort conditions throughout the summer. The proposed algorithm determines the different configuration designs for the cooling coil which are shown in Table Energy Analysis Fig.4 shows the simulation results for energy consumption of each cooling coil geometry and compares it with monitored energy consumption of the central cooling plant in summer.according to the results, as the number of rows increase, the chiller and supply fan power increase. The influence of the number of rows on cooling tower fan is relatively very small. Meanwhile, a lower number of fins albeit resulting less plant energy consumption in the system reduces cooling coil capacity which causes a higher supply air temperature leaving the cooling coil. Therefore, the supply fan must work with higher air flow rate, in turn increase fan power consumption. The optimal configuration exists when the summation of power consumption for both the chiller and supply fan is minimal. As a result, considering the number of rows as the only parameter will consume more electrical energy in the whole system. Also results show that the reduction of the number of tubes in a row does contribute to lowering the cooling coil capacity while the system power saving increases. The influence of the number of tubes on the system power consumption is more than fins number and thus the balance between system power and cooling coil capacity can be occurred by increasing the fin number while number of tubes are decreased. Further benefits can be obtained when the reduction in tubes number is coupled with a reduced coil aspect ratio. This is evident from the simulation results that system power consumption drops from 3% to 8% when the tube number in a row is reduced from 52 to 34 while the fin numbers are increased from 8 to 14 to keep the cooling coil capacity.

8 Table.1. Simulation results for different cooling coil configurations Actual Length High AR No. Of Row No. Of Tubes in a Row No. Of Fins The investigation of current coil geometry on CCP performance showed that by reducing the effective number of operating coil rows from a 6-row to a 4-row configuration, the cooling coil capacity is reduced to the building cooling load demand and thus the cooling coil efficiency is increased.however, the potential of energy saving for configuration 1, 2, 3, 4 and 5 in summer are respectively 8.1%, 9.3%, 3.2%, 6.4% and 4.8% which are significant enough to consider. The simulation is performed in the same conditions such as tube diameter, tube and fin material, tube spacing, and fin thickness for each case. 4.2 Thermal Comfort Thermal comfort is all about human satisfaction with a thermal environment. The design and calculation of air conditioning systems to control the thermal environment to achieve standard air quality and health inside a building should comply with the ASHRAE standard To predict the thermal comfort condition, an index called predicted mean vote (PMV) which indicates mean thermal sensation vote on a standard scale for a large group of people is used in this paper. PMV is defined by six thermal variables from human condition and indoor air, namely air temperature, air humidity, air velocity, mean radiant temperature, clothing insulation and human activity. The PMV index predicts the mean value of the votes on the seven point thermal sensation scale +3: hot, +2: warm, +1: slightly warm, 0: neutral, -1: slightly cool, -2: cool, -3: cold. According to ISO 7730 standard the values of PMV between -1 and 1 are in the range that 75% people are satisfied while between -0.5 and 0.5 is the range that 90% people will be satisfied. It is of interest to see how the resulting PMV ap-

9 pears with each control scenarios. The resulting PMV fluctuates between 0.15 and 0.94 for hottest day in July for configuration 2 as most effective configuration discussed in the last section. Fig.4. CCP energy consumption via different cooling coil configuration The PMV for all configurations is shown in Fig. 5. Therefore, all PMV responses lie in the acceptable range, i.e. -1< PMV <+1. Also according to the results, for the best cooling coil configuration design,42% of the votes are for PMV < 0.5 and 100% of the votes for PMV < 0.94 which means this design is able to save energy significantly while can maintain PMV values in a standard range. Fig.5. PMV comparision fot July

10 5 Conclusion In this paper, we have addressed the modeling and optimization problem of a cooling coil to target energy savings in a commercial building HVAC system. Simulation has been carried out to investigate the influence of cooling coil optimum design on energy demand and comfort conditions. The simulation modules were developed by using monitored data which were collected experimentally from the existing central cooling plant of the real-world commercial building located in a hot and dry climate region. An optimization algorithm which uses an iterative redesign procedure is developed and implemented on TRNSYS in order to determine and select the optimum configuration of the cooling coil. Results show that the new optimum design of the cooling coil offers energy saving potential up to 9.3% while maintaining the thermal comfort conditions in the building. References Perez-Lombard L, Ortiz J and Pout S (2008) A Review on Building Energy Consumption Information, Energy and Buildings, Vol. 40, pp Jabardo J.M.S, Bastos Zoghbi Filho J.R, Salamanca A (2006) Experimental Study of the Air Side Performance of Louver and Wave Fin-Tube Coils, Experimental Thermal and Fluid Science, Vol. 30, pp Sekhar S.C and Tan L.T (2009) Optimization of Cooling Coil Performance During Operation Stages for Improved Humidity Control, Energy and Buildings, Vol. 41, pp Cai W.J, Wang Y.W, Soh Y.C, Li S.J, Lu L, Xie L (2004) A Simplified Modeling of Cooling Coils for Control and Optimization of HVAC Systems, Energy Conversion and Management, Vol. 45, pp TRNSYS software, A Transient System Simulation Program, version 16,Wisconsis-Madison University. Available: < Vakiloroaya V, Zhu J.G and Ha Q.P (2011) Modeling and Optimization of Direct Expansion Air Conditioning Systems for Commercial Buildings Energy Savings, Int. Symposium on Automation androbotics in Construction, Seoul, Korea. American Society of Heating, Refrigerating and Air-Conditioning Inc, ANSI/ASHRAE Standard 140: Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program, (2007). American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc, ASHRAE Fundamentals Handbook, Atlanta, GA, (2005). American Society of Heating, Refrigerating and Air-Conditioning Inc, ASHRAE Standard 55: Thermal Environment Conditions for Human Occupancy, (2010). International Organization for Standardization, ISO Standard 7730: Moderate Thermal Environment, Determination of PMV and PPD Indices and Specifications of the Conditions for Thermal Comfort, Geneva, Switzerland, (2005).