A cooling tower combined with chilled ceiling: system optimisation

Transcription

1 A cooling tower combined with chilled ceiling: system optimisation Ala Hasan 1, Mika Vuolle 1, Kai Sirén 1, Riikka Holopainen 2 and Pekka Tuomaala 2 1 Laboratory for HVAC-technology, Helsinki University of Technology, Espoo, Finland kai.siren@tkk.fi 2 Building, Built Environment, Technical Research Centre of Finland, Espoo, Finland Abstract The goal of the work was to find out the best performance of a closed wet cooling tower system combined with chilled ceiling, serving a residential building. The research method was building simulation combined with optimisation. IDA-ICE was used for simulation together with GenOpt optimisation package. A closed wet cooling tower connected to chilled ceiling units was the system under consideration. This building-system combination was simulated using Helsinki 1979 reference year weather on an hourly basis. The highest yearly COP achieved was This was achieved using the cooling tower only. The tower combined with the storage tank or the optional chiller gave a slightly lower COP value. Keywords cooling towers; evaporative cooling; optimisation 1. Introduction A wet cooling tower is one application of evaporative cooling (EC) and can be used to replace the vapour compression system in cooling of buildings [1, 2]. The cooling tower can be combined with chilled ceiling or cooling beams. With such room units the cooling effect could be achieved by a relatively high cooling water supply temperature (about 18 20ºC) [3, 4]. Highest potential for this concept is in cool and dry climates, but warm and dry or maritime temperate climates offer a significant potential as well [5]. The technical concept must be dimensioned in a proper way. This means that the EC system is capable of working with a small enough approach and, at the same time, of rejecting the desired amount of heat. Many factors like the design of the cooling tower and the system, dimensioning of all components as well as control strategy affect the efficiency of the concept. Best results are obtained with an optimised system. Optimisation can be carried out by simulating the building together with the EC system and searching for the best combination of the most important system parameters. Recent activities focus on developing thermal models of the tower or possibilities of application of this cooling system. Studies on optimisation of such a system using optimisation tools are lacking. 2. Building To carry out a computational optimisation of an EC system combined with a building, a typical Finnish four storey residential building was used. Five flats on the 3rd floor were chosen to form the load for the EC system, Fig 1. Since the main concern was about energy consumption in the flats, a very detailed description of the building was not necessary. Therefore in the computation each of

2 218 A. Hasan et al. Figure 1. Half of the symmetrical fl oor plan of the residential building. Table 1. Some characteristics of the fi ve building zones (fl ats) zone no floor area m 2 window area m 2 no of occupants the five flats was considered as one air-zone. The floor area, windows area and number of occupants for each zone are indicated in Table 1. The walls, floor and ceiling construction were taken according to a Finnish building specification [6]. The construction is classified as a heavyweight construction with reinforced concrete in the external and internal walls as well as in the floor/ceiling. The external wall U-value is 0.25 W/m 2 K. The windows are 3 layers, clear glass, U-value 1.4 W/m 2 K, solar heat gain coefficient 0.692, solar transmittance 0.577, with blinds between the outer panes. The height of the rooms is 2.5 m. Heating energy is supplied to the zones from district heating by means of hydronic radiators. Typical values and profiles for the internal heat gains (from occupants, appliances and lighting) were adopted from the building specification [6]. Fig. 2 shows the

3 A cooling tower combined with chilled ceiling: system optimisation 219 Figure 2. Example of internal heat gains, workdays zone no 3. workday profile for zone 3. For weekends a different profile was used. The average value of the internal gains ranges from 5.33 to 7.36 W/m 2 of floor area. 3. Evaporative cooling system The evaporative cooling system is shown in Fig. 3. The tower is a closed wet cooling tower [1]. In this type of tower, usually antifreeze liquid flows inside tubes arranged as a bundle inside the tower where the connection between the tower and an intermediate heat exchanger HE1 is the primary circuit. Pump P3 circulates the antifreeze liquid in the primary circuit. The outer surface of the tube bundle is wetted by spray water that is circulated inside the tower by pump P4. Water coming from the building flows through the intermediate heat exchanger HE1 and back to the cooling panels, forming the secondary circuit. Air flows across the wetted tubes inside the tower by means of a fan. In the exchanger heat is transferred to the antifreeze liquid. The liquid in turn is releasing the heat to the spray water across the tubes inside the tower. The contact of air with the spray water on the tubes surfaces results in evaporation of a small

4 220 A. Hasan et al. Figure 3. The evaporative cooling system with a closed wet cooling tower, storage tank and a chiller option. amount of spray water, which produces the main part of the cooling effect besides sensible heat transfer. The idea for using antifreeze liquid in the primary circuit is to use the tower in dry operation when there is a risk of spray water freezing. However, this is a practical implementation that was not investigated in the current study as the investigation here covers the wet operation of the tower only. The performance of the tower is controlled by means of variation of the rotational speed of the fan. This is done according to the temperature of the outlet water from the tower in comparison with the tower set temperature, where the latter temperature

5 A cooling tower combined with chilled ceiling: system optimisation 221 is the required outlet water temperature from the tower. The higher the fan speed, the lower the tower outlet temperature. The control is on the fan and not on the tower pumps because the electrical energy consumption of the fan is dominating. The cooling system in Fig. 3 combines the cooling tower with a storage tank and a backup chiller. Such a combination will prevent overheating in the zones when the tower alone is not capable of supplying the required cooling energy. It is supplied then from the storage tank or the chiller. This is a general control strategy, for any location or weather, which will be tested here for the weather of Helsinki. 4. Simulation and optimisation The thermal behaviour of the building and the cooling load of the zones are calculated using the IDA ICE 3.0 building simulation program [7]. A model for the tower was implemented and added to the simulation program as well. The data for the tower model is based on the experimental measurements of the EcoCool project s prototype tower [1, 2]. The empirical correlation used to calculate the mass transfer coefficient K m [kg/(s m 2 )] is K m = ( ρv ), a where V a is the air velocity inside the tower [m/s] and r is the air density (kg/m3). Otherwise the tower is modelled according to governing equations of heat and mass transfer [1]. Used equations are based on a simplification in the solution by assuming a constant spray water temperature along the tower. The IDA ICE program, including the building model and a model for the complete cooling system was combined with the GenOpt [8] optimisation tool. GenOpt has been implemented in the optimisation of parameters of building design and HVAC systems [9, 10]. The building simulation program was used to calculate the cooling load and the heat and mass transfer processes in the EC-system. The computation was made on an hourly basis using Helsinki 1979 weather data. The results from the simulation were passed to the optimisation algorithm, which in turn generated the next solution vector to be used in the following simulation run. This sequence was repeated until the optimum was found. 5. Results First the performance of the cooling system was evaluated without optimisation. Three different system alternatives were investigated, Table 2. In Table 2 T max is the maximum annual indoor air/zone temperature and N 24.2 is the number of annual hours the indoor air temperature is equal to or higher than the limit 24.2ºC. COP a is the system annual coefficient of performance defined as the ratio of annual load removed from the building to annual electrical energy required to run the whole cooling system. Table 2 shows that the benefit of the chiller is in this case very small and the penalty is a lower COP a because the chiller is assumed to work with a typical vapour (1)

6 222 A. Hasan et al. Table 2. Results from annual simulation for non-optimised system alternatives Cooling system tower tower + tank tower + tank + chiller T max [ºC] N 24.2 [h] COP a compression efficiency COP c = 2.5. The tower + tank system is able to maintain the indoor temperatures practically on the same level as the more complicated system and with slightly less energy consumption. The simplest system containing only the cooling tower is forced to let the indoor temperatures rise slightly. On the other hand it has the highest COP a of the three alternatives. The hourly COP h values for the tower-only system during the period May September are shown in Fig. 4. The zero values are indicating no use of cooling. The maximum COP h can reach values close to 40. To illustrate the optimisation, an example case was chosen where the cooling system contains the cooling tower + storage tank + chiller. Five parameters were taken as decision variables: supply water temperature to cooling panels T cp, temperature difference dt 1 = T cp T st where T st is the set temperature for the storage tank, temperature difference dt 2 = T st T sc where T sc is the set temperature for charging the tank, the charging flow rate q mc and the tank volume V st. For each variable a feasible range was defined with an initial value and a step between adjacent values. The task was to find the variable combination, which gives the highest possible value for the COP a. For optimisation the General Pattern Search with Hooke-Jeeves algorithm [6] was used. Since GenOpt always finds the minimum value of the objective function, the latter was defined as the annual system COP a with a negative sign. For the optimised case the annual COP a is 8.27 compared with the non-optimised case value The maximum indoor air temperature T max < 25ºC and the number of hours N 24.2 = 2. The decision variables of the optimised solution are shown in Table 3. The optimised supply water temperature T cp takes the highest value in the range because the tower is able to reject more heat on a higher temperature level. On the other hand, there must be enough cooling panel area in the building to prevent indoor temperature rise. The storage tank temperature set value is 18ºC. A higher value would reduce the useful capacity of the storage. A lower value would increase the storage capacity but demand more energy to charge the tank. The set temperature for charging the tank, which is the lower band for the tank temperature in charging mode, takes the value 16.8ºC. There is no benefit in charging to a lower temperature using the cooling tower since the energy to run the tower fan and pumps is increasing radically. The system performance is not very sensible for changes in charging flow rate or storage tank volume. The flow rate is not changed from the initial value and the tank volume is changed only 10%.

7 A cooling tower combined with chilled ceiling: system optimisation COP h Outdoor air temperature [ C] Figure 4. Hourly COP values for the cooling tower only system during the period May September. Table 3. Decision variables of the optimised example case T cp ºC dt 1 ºC dt 2 ºC q mc kg/h min max step initial optimised Conclusions An evaporative cooling system serving a residential building has been simulated and optimised. The annual coefficient of performance COP a of the cooling system consisting of a cooling tower + storage tank + optional chiller was increased 15% from an initial value 7.22 to the optimised value However, the most energy efficient system for this application in Helsinki weather conditions seems to be the tower V st l

8 224 A. Hasan et al. only without a storage or chiller. It has the highest annual coefficient of performance and is capable of maintaining acceptable thermal conditions in the building. References [1] A. Hasan and K. Sirén, Theoretical and computational analysis of closed wet cooling towers and its applications in cooling of buildings, Energy and Buildings, 34 (2002), [2] J. Facão and A. Oliveira, Thermal behaviour of closed wet cooling towers for use with chilled ceilings, Applied Thermal Engineering, 20 (2000), [3] Sprecher P, J. Borth and R. Niessen, Cooler ceilings with less energy, Sulzer Technical Review, No. 1, (2000), [4] M. Koschenz, Model for closed circuit evaporative cooling tower, IBPSA International Building Performance Simulation Association, 4th Int. Conf., Madison, Wisconsin, USA (1995). [5] B. Costelloe and D. Finn, Indirect evaporative cooling potential in air-water systems in temperate climates, Energy and Buildings, 35 (2002), [6] LVIS 2000 Type buildings, Technical Research Centre of Finland VTT, Report 17 (1992). [7] P. Sahlin, L. O. Eriksson, P. Grozman, H. Johnsson, A. Shapovalov and M. Vuolle, Whole-building simulation with symbolic DAE equations and general purpose solvers, Building and Environment, 39 (2004), [8] M. Wetter, GenOpt, Generic optimization program, User manual Ver (2004), Technical report LBNL [9] J. N. Holst, Using Whole Building Simulation Models and Optimizing Procedures to Optimize Building Envelope Design With Respect to Energy Consumption and Indoor Environment, 8th IBPSA Conference, Eindhoven, Netherlands (2003), [10] M. Wetter and J. Wright, A Comparison of Deterministic and Probabilistic Optimization Algorithms for Nonsmooth Simulation-Based Optimization, Building and Environment 39 (2004),