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2) ,) () (ln) ( ) (wi aowo aiwi ao wo aimT TT TT T T TT−−− − −= Δ (3) where Qcc is the heat absorbed by the chilled water in the cooling coil tubes, Ar is the ratio of outside surface area of the coil to inner surface of tubes, Ìs is the fin efficien-cy, ho and hi are respectively the heat transfer coefficient of the outer surface and inner surface of the cooling coil, Tai and Tao are respectivelly the air temperature en-tering and leaving the cooling coil and Twi and Two 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 in-terfaced 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 mo-nitored 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.
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 Ptotal,i is determined for each 968 V. Vakiloroaya and J. Madadnia operation hour i, in response to the different cooling coil designs by using the devel-oped TRNSYS model. Finally the summer energy consumption Ptotal is summed up for all the operating hours. Consequently, the objective function can be explicitly established as follows: ), ( , , , ,1,1, i cwp i chp i ctf i ahunii chnii total totalP P P P P P P + + + = = =+= (4) where N=2170 H (5×31×14) for May, June, July, August and September (based on 14-h daily operation) and Ptotal is the summer energy consumption of the CCP includ-ing energy usage of the chiller Pch,i , the AHU variable air volume (VAV) fan Pahu,i , the cooling tower fan Pctf,i , the chilled water pump Pchp,i and the cooling tower pump Pcwp,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 algo-rithm 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 un-changed. 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 fol-lowed 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 require-ments: physical limitation of parameters and interaction between components which are shown in Fig. 1 and listed as follows: Requirement 1. The cooling coil capacity Qcc must be more than the building cooling load Qb and less than the cooling capacity of the chiller Qch : .ch cc b Q Q Q ≤ ≤ (5) Requirement 2. The supply air temperature Tsup is restricted to avoid overcooling or becoming too humid inside the building as: . 20 10 sup C T C ≤ ≤ (6) Requirement 3. The cooling coil aspect ratio (AR) should be within its limitation: . 6 1 ≤ ≤ AR (7) Requirement 4.The comfort ranges for indoor air temperature Troom and relative hu-midity RH during occupied periods are given respectively: %. 60 % 40, 27 20≤ ≤≤ ≤RHC T C room (8) 970 V. Vakiloroaya and J. Madadnia 2.4 Model Verification The simulation is run with a time interval of 15 minutes that is equal to the monitor-ing 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 simula-tion 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. 3 Case 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 140-2007. 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). Fig. 3. The simulation information flow diagram in TRNSYS work space 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