District cooling (DC) continues to proliferate due to increasing global cooling demands and economies of scale beneﬁts; however, most district-scale modeling has focused on heating, and to the best of our knowledge, researchers have yet to model cooling plants featuring waterside economizers in DC settings. With the Modelica Buildings library expanding its capabilities to district scale, this study is one of the ﬁrst to demonstrate how the open-source models can be used for detailed energy and control analysis of a DC plant. For a real-world case study, we developed and calibrated high-ﬁdelity models for a DC system central plant at a college campus in Colorado, USA, and we optimized the condenser water supply temperature (CWST) setpoint for a DC plant across multiple time horizons using the Optimization library in Dymola. Results indicate that annual CWST optimization saves 4.7% annual plant energy, with less than 1% of additional energy savings gained through daily optimization. This conﬁrms previous studies’ ﬁndings that high frequency CWST optimizations are not necessary for the studied system. Keywords District CoolingOptimizationChiller PlantWaterside Economizer Modelica Buildings Library K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT 1 Introduction District cooling (DC) systems typically provide cooling services to buildings from central plants and are increasing in demand. In the United States for example, DC serves 174 million square meters of ﬂoor space, delivering 15 GW of chilled water annually [ 1]. This is currently more than any other country, but global installations are growing rapidly, particularly in the Middle East [ 2]. With buildings consuming 36% of global energy [ 3] and space cooling growing faster than any other end use [ 4], many are looking to DC for its energy efﬁciency and economic beneﬁts [5,6,7]. Rather than individual buildings producing their cooling needs with individual air conditioning equipment, centralized plants produce chilled water (CHW) that can be distributed to multiple buildings connected to the district. This aggregation of cooling equipment to a district scale enables the centralized maintenance, the use of more efﬁcient chillers, and the integration of renewable energy resources at the district scale. Current modeling and simulation work tends to focus on district heating (DH) with limited focus on DC. A simple Scopus search involving the keywords “model” and either “district heating” or “district cooling” produces 20,109 and 1,230 results, respectively. While some DH research can be applied to DC —as suggested in some DH case studies [8,9,10] —there are also important differences that make DC modeling unique, such as the heightened sensitivity of cooling generation efﬁciency to even small changes in CHW temperature (e.g., 0:1K) [6], and "low delta-T syndrome" [11], a common energy efﬁciency problem among DC systems and chiller plants. Several groups have made valuable contributions to DC modeling literature. High-ﬁdelity and reduced-order modeling techniques have been adapted to reduce plant energy consumption [ 12], peak loads [ 13], and implement model predictive control [ 7,14], to name a few. While a variety of chiller types have been studied —including compressor, absorption, turbo, and double-effect varieties —to the authors’ best knowledge, none of the previous literature modeled chiller plants with waterside economizers (WSEs) in DC applications. Further, we only found one study that used Modelica for DC plant modeling [ 7], yet Modelica is a promising platform for these applications due to its acausal modeling scheme, multitude of variable time-step numerical solvers, and rich open-source libraries with high re-usability potential. This work demonstrates how the open-source Modelica Buildings library can be applied for detailed modeling of chiller plants with WSE for DC applications. The university wants to identify energy efﬁciency improvements with little to no ﬁnancial investments in equipment upgrade. Thus, condenser water supply temperature (CWST) optimization was selected for its past successes in reducing chiller plant energy consumption [ 15]. The condenser water supply is the water entering the condenser of the chillers, and it’s temperature setpoint affects the chillers’ operating efﬁciency, the economizing heat exchanger’s effectiveness, and the required cooling tower fan power. Several past works with chiller plant modeling and simulation include CWST optimization [16, 17], and several optimization time horizons from hourly to monthly have been studied [18]. In this work, we modeled the DC plant for an existing college campus featuring six connected buildings in Colorado, United States (ASHRAE Climate Zone 5B). The objectives of this case study are to (1) demonstrate the application of Modelica and the Buildings library for detailed energy analysis of a DC plant with a WSE, and (2) identify the the optimal CWST setpoint by evaluating several optimization time horizons. While we selected CWST optimization for this case study, it is important to note that the model can be used for other analyses as well, such as replacing the chillers or adding thermal storage. In Section 2, we present the mechanical and control systems for the case study DC plant. This is followed by the Modelica implementation in Section 3, and the veriﬁcation and validation of equipment and system models in Section 4. Presentation of the optimization methodology and the optimization results are in Sections 5 and 6. Section 7 concludes the paper with future work. 2 System Description The case study site is a college campus in Colorado’s Denver Metropolitan area with a central plant providing chilled water for space and process cooling to six buildings. This section presents the mechanical and control systems for the DC plant. 2.1 Mechanical System As depicted in Figure 1, the cooling plant is a primary-only chilled water system with parallel connections between a WSE and two chillers on both the plant side (the condenser water (CW) piping) and the load side (the CHW piping). Following standard nomenclature, the condenser water supply (CWS) is the plant-side water being supplied to the chillers, and the return (CWR) is returning to the cooling towers. Similarly, the chilled water supply (CHWS) is being supplied to the district, while the return (CHWR) is returning to the plant. Both the CW and CHW loops contain bypass loops. The CW bypass valve is a two-position directional valve to switch between cooling tower and bypass modes, 2 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT while the CHW bypass valve modulates to maintain the minimum CHW ﬂow rate through the evaporator of the chillers. Although both the CW and CHW pumps are equipped with variable frequency drive (VFD) motor controllers, the CW pumps modulate their speed to maintain a constant ﬂow rate setpoint, while the primary-only CHW pumps operate at variable speeds to maintain a differential pressure setpoint at a distant building. Further details regarding the nominal equipment information can be found in the Appendix. CHW Return from District Waterside EconomizerChillersCondenser Water PumpsCW Bypass Chilled Water Pumps CHW Supply to DistrictCHW BypassCooling Towers Check ValveTwo-Way ValveSump CWR CWSCHWSCHWR Figure 1: Schematic diagram for the central plant. 2.2 Control System The control system includes four levels (Figure 2): a top-level Master Control, Systems Control, Units, and Devices. First, the Master Control determines the operating state of the entire plant and sequences the various "systems". Second, theSystems Control represents the collection of similar "units" physically connected in the process loop. This control level determines correct number of units that should be running to meet the demand (e.g., staging of various equipment). Third, Units represent the collection of devices that combine to perform a speciﬁc task. The Units Control level prescribes the setpoint for equipment operation. Lastly, the Devices layer contains single-input single-output (SISO) systems, providing the fundamental building blocks of the control. These are local control setpoints predominantly met by proportional integral (PI) controllers. Master Control (e.g., cooling mode) Systems Control (e.g., chiller/pump staging) Units (e.g., CW loop, chiller subsystem) Devices (e.g., local SISO PI controllers) Figure 2: Four control levels of the central plant. At the top Master Control level, the cooling plant can operate in three active cooling modes in addition to the Off mode: (1) Free Cooling (FC) mode, (2) Mechanical Cooling (MC) mode, and (3) Pre-Mechanical Cooling (Pre-MC) mode. The state graph in Figure 3 depicts the switching conditions to move between each of these states. Switching 3 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT conditions include the total cooling load _QC(calculated from temperature and mass ﬂow sensors at the plant), the wetbulb temperature WBT and its switching setpoint WBT Set, the chilled water mass ﬂow rate _mCHW , the maximum allowable chilled water mass ﬂow rate for the WSE _mWse;CHW;Max , the chilled water supply temperature CHWST and its setpoint CHWST Set, the condenser water supply temperature CWST , and the minimum condenser water supply temperature allowed by the chiller CWST Chi;Min . The offset and dead band temperature of the control signals as well as the waiting time are adjustable. For this plant, the maximum WSE chilled water mass ﬂow rate is 120.5 kg/s. The wetbulb temperature transition setpoint, the chilled water supply temperature setpoint, and minimum chiller condenser water supply temperature are 6.7C, 6.1C, and 10.0C, respectively. 𝑄𝐶> 0and 𝑊𝐵𝑇>𝑊𝐵𝑇 𝑆𝑒𝑡𝑄𝐶> 0and WBT≤ 𝑊𝐵𝑇 𝑆𝑒𝑡𝑄𝐶= 0 for 15 min𝑄𝐶= 0 for 15 min 𝐶𝐻𝑊𝑆𝑇>𝐶𝐻𝑊𝑆𝑇 𝑆𝑒𝑡+ 1.67°𝐶or 𝑊𝐵𝑇>𝑊𝐵𝑇 𝑆𝑒𝑡+ 0.56°𝐶or 𝑚𝐶𝐻𝑊>𝑚𝑊𝑠𝑒,𝐶𝐻𝑊,𝑀𝑎𝑥 + 0.631𝑘𝑔/𝑠 for 15 min𝐶𝐻𝑊𝑆𝑇<𝐶𝐻𝑊𝑆𝑇 𝑆𝑒𝑡−1.67°𝐶 for 15 min 𝐶𝑊𝑆𝑇≥ 𝐶𝑊𝑆𝑇 𝐶ℎ𝑖,𝑀𝑖𝑛 for 120 s𝑊𝐵𝑇<𝑊𝐵𝑇 𝑆𝑒𝑡−0.56°𝐶and 𝑚𝐶𝐻𝑊<𝑚𝑊𝑠𝑒,𝐶𝐻𝑊,𝑀𝑎𝑥 −0.631𝑘𝑔/𝑠 for 15 minMC FCOFF Pre- MC Figure 3: Master control logic for selecting plant cooling mode. 3 Modelica Implementation The DC plant is implemented in Modelica using components from the Modelica Buildings library (Version 7.0.0) [ 19] and Modelica Standard Library (Version 3.2.3). New system and equipment-level models were developed as part of this study, which will be open-source released in the Modelica Buildings library. The system models are presented in a top-down approach in the following sections. 3.1 Mechanical System Shown in Figure 4, the central cooling plant model contains several control blocks on the left with the condenser water (green lines) and chilled water (blue lines) loops on the right. The system’s design schematic (Figure 1) and Modelica diagram contain one-to-one modeling relationships, allowing users to clearly interpret the conﬁguration. We connected the inlet and outlet ports of the plant to a district model that reﬂected the tabulated heat ﬂow rate (broken down by mass ﬂow rate and change in CHW temperature) of the real district from 2018 measured data. For this case study, the cooling plant features two chillers with a WSE connected in parallel on both the chilled water and condenser water sides. We instantiated the Buildings.Applications.DataCenters.ChillerCooled.Equipment. Nonintegrated model with the optional CHW supply temperature control on the WSE disabled, which implements the ElectricEIR chiller model, based on the DOE-2 electric chiller [20]. New subsystem models for the cooling tower with CW bypass and a parallel cooling tower model were developed based on the Modelica Buildings and Modelica Standard libraries. For the cooling tower model, we instantiated the Merkel cooling tower model from the Modelica Building’s library, based on the variable speed Merkel model in EnergyPlus (Version 8.9.0) [21]. The chilled water pump subsystem was modeled as a single ideal mass ﬂow control pump meeting the total ﬂow rate needs of the district. When appropriate, this simpliﬁcation has the beneﬁt of reducing the number of equations present in the model, which in turn reduces the simulation run time. However, to note, modelers should use caution when evaluating the energy consumption of ideal pumps that enforce the ﬂow rate regardless of head, because if the pump works against a closed valve, then unrealistic electric power spikes can occur because the power is proportional to the product of the enforced mass ﬂow rate times the pump head, which can be arbitrarily high for this idealized model. For the constant speed condenser water pumps, the subsystem included a vectorized mass ﬂow-controlled pump model (2 pumps) with the staging and speed determined by their respective control blocks. 4 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT T T T m_flowm_flowMMM dT ++1 -1pro1k=cp_default cpm_flow M 2CHWST Setpoint Cooling Mode ControlChiller Staging WSE Staging CHW Bypass Bypass ValveCheck ValveCHW PumpsExpansion TankChillers with Nonintegrated WSECooling Towers with BypassExpansion Tank CW PumpsCW Loop ControlCW Pump Speed Control CW Pump Staging Wetbulb Temperature Total District Mass Flow RateCW Loop CHW Loop CHW supply to district CHW return from district Figure 4: Diagram of Modelica model for the district cooling plant. 3.2 Control System The four control layers are implemented in Modelica. The Master Control (Figure 5) mirrors the schematic state diagram shown previously in Figure 3. Six real inputs decide the state of the Master Control mode: Off, FC, MC, or Pre-MC. An integer output ranging from 0 to 3 corresponds to the cooling mode status. This control is packaged as one block and instantiated in the top-level system model for the central plant. All Systems Control blocks follow a similar implementation. Figure 6 exempliﬁes the CW loop control implementation. This includes determining the operating state through the CW control mode staging (layer 2), specifying the temperature setpoint in the CW loop subsystem (layer 3), and implementing the local PI controllers for the cooling tower fan and bypass valves (layer 4). Depending on the cooling mode (FC, MC, Pre-MC), either the chilled water or condenser water supply temperature will be controlled. Further, the condenser water supply temperature setpoint changes between MC and Pre-MC modes. In FC mode, the chilled water supply temperature is controlled. If the measured temperature reading is greater than the setpoint plus the dead band, then the cooling tower fan PI controller is engaged to maintain the setpoint and the CW bypass valve is closed. If the measured temperature reading is less than the setpoint minus the dead band, then the cooling tower fans are off, the CW bypass valve opens, and the cooling tower isolation valves are controlled with the PI controller to maintain the setpoint. This control model is also instantiated on the top system model of the central plant. 5 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT offmecCooactivecon1 tWai QLoa <0 and TWetBul > TSwicon8tWaiQLoa >=0stateGraphRoot root freCooactivepreMecCooactive con2 tWai QLoa < 0 and TWetBul < TSwi con3tWaiTWetBul < TSwi - TDeaBan and m_flow_CHW <= m_flow_swi - m_flow_deaBan con4 tWai TCHWSup > TCHWSupSet + TCHWDif or TWetBul >= TSwi + TDeaBan or m_flow_CHW > m_flow_swi + m_flow_deaBancon5 tWai TCHWSup < TCHWSupSet - TCHWDif con6120 sTCDWSup >= TMinEntChi con7tWaiQLoa >= 0 else: 0swi MC,FC,PreMCCHWST Setpoint CHWST CWST Wetbulb Temperature CHW Mass Flow Rate Flow RateTotal HeatCooling ModeOff FC MC MCPre Figure 5: Diagram of the Modelica model for the Master Control. k=TCWSupSetMC 1mecCooMod 2preMecCook=273.15 + 10 k=TCHWSupSetPI1fanConproFan[]proFan[]B IbooToRea[] B IbooToRea[] off k=0PI proVal[]proVal[] B Iope k=1TSet TSet1 fcMod =conInt TMeaCooling ModeCooling Towers On State Fan Speed Cooling Tower Valve Positions CWST CHWSTCWST Setpoint - MC CWST Setpoint PreMC CHWST Setpoint FCFan PI Control Bypass PI ControlCW Control Mode StagingFan Control Switch Isolation SwitchControlValve Bypass SetpointValveBypass Valve PositionCooling Tower Valve PositionFan Speed Figure 6: Diagram of the condenser water loop control. While most local control was implemented with SISO PI controllers, some control actions were assumed to be ideal when the simpliﬁcations would not signiﬁcantly impact system energy performance. For example, the chilled water pump control was idealized to directly read the mass ﬂow setpoint from tabulated mass ﬂow rate data rather than staging and modulating the pumps’ variable frequency drives to maintain the pressure drop setpoint at distant buildings. Conversely, the chilled water bypass valve control implemented PI controllers like the condenser water bypass and cooling tower fan speed shown in Figure 6. 3.3 Simulation Settings All simulations ran in Dymola 2021 on Linux. While there are many suitable numerical solvers in Dymola for this type of application, CVODE [ 22] was selected for its suitability for solving stiff numerical problems (e.g., the system of differential algebraic equations contain both fast and slow dynamics, which make the selection of a variable time step 6 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT size difﬁcult for the solver), and in our experience, it typically simulates thermo-ﬂuid systems quickly and robustly. All simulations ran using a tolerance of 1e-6. The computer contained 32 GB of RAM and a 3.60GHz CPU. 4 Veriﬁcation and Validation To establish an accurate baseline model, we validated major cooling equipment and system-level operation with respect to the measured data. We evaluated the Coefﬁcient of Variation of the Root Mean Square Error (CVRMSE) using hourly time steps as follows: CVRMSE =qP(yi^yi)2 N1 y(1) whereyiis the individual measured data, ^yiis the corresponding simulation-predicted data, yis the mean of the measured dataset, and Nis the total number of datapoints. Due to uncertainties and gaps in measured data, validation of the entire DC plant for one year of measured data was not possible. This is consistent with many past DC and chiller plant modeling endeavors [ 6,23]. Thus, two time periods representing typical summer and winter conditions were selected to validate the model, encompassing both full and part load conditions. With hourly data, the CVRMSE needs to be within 30% for the model to be considered validated [ 24]. Chilled water heat ﬂow, mass ﬂow, supply temperature, and return temperature were used to validate the model based on the limited availability of historical measurements. Ideally, the pump, chiller, and fan power would be used to validate the model; however, these electrical data points were not available. Thus, we veriﬁed equipment and system-level performance with design documents and by consulting plant operators, and historical data was used to the full extent possible to validate the model. The results are summarized in Table 1. The simulations fell within 12% CVRMSE for all locations, well within the acceptable ranges of modeling uncertainty. During the summer period, the plant operated in mechanical cooling mode with the chiller meeting the cooling demand. While the plant operated in free cooling mode with the WSE meeting the entire cooling demand during the winter period. Table 1: Validation results targeting CVRMSE less than 30%. Equipment/ CVRMSE (%) System _QCHW _mCHWTCHWSTCHWR Summer Period (August 1-3, 2018) Plant 6.2 7.5 6.7 8.9 Chiller 6.5 7.5 8.9 6.7 Winter Period (January 28-30, 2018) Plant 12.0 2.8 8.5 11.8 WSE 12.0 2.8 8.5 11.8 Figure 7 visualizes the primary chiller’s validation results. Upon inspection, the simulated CHW mass ﬂow rate and supply and return temperatures match the measured data well. However during the nighttime, the measured CHW outlet temperature drifts below the minimum allowable value per the control speciﬁcations. Contrarily, the CHW outlet temperature is well controlled at the desired setpoint in the simulation. It is unknown why the real system does not maintain the CHW outlet temperature, and this is undesirable from a control standpoint. Based on these validation results, the accuracy of the DC plant model is within acceptable limits of the real system’s measured data and expected performance. 5 Optimization Problem We formulated a sequence of single objective optimization problems that, collectively, minimize the plant’s annual energy consumption. The sequence of problems were formulated as follows. Let = 1year andM2N be the number 7 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT Figure 7: Primary chiller validation results in early August with the highlighted region indicating the control limits for the CHW outlet temperature. of intervals over which the optimization problem was solved. Then, we solved the set of problems Pi, with Pi min x2[TCWS;TCWS ]EPla;i(x); (2) EPla;i(x) =Zti+1 ti(PCH(x;s) +PCWP (x;s) +PCHWP (x;s) +PCT(x;s))ds withti2fti2Rjti=i =M; i2f0;:::;M1gg, where the independent variable xis condenser water supply temperature, EPla;i is the total plant energy during the optimization period t2[ti; ti+1),PCHis the power of the chillers,PCWP is the power of the condenser water pumps, PCHWP is the power of the chilled water pumps, PCT is the power of the cooling towers, TCWS is the condenser water supply temperature low limit, and TCWS is the condenser water supply temperature high limit. Based on the chiller’s speciﬁcation documents, the condenser water supply temperature low and high limits are 10:0C and29:4C, respectively. These are used through the optimization process. Optimization problems with time horizons of one day, week, month, and year are solved using the Optimization library Version 2.2.4 [ 25]. Released alongside Dymola 2021, this library allows multi-objective optimization of complex systems within Dymola’s modeling and simulation environment. The user interface allows for quick formulation of optimization problems, while the model’s state values can be reinitialized for consecutive optimization runs without needing to rerun the entire optimization. For numerical optimization algorithms, we employed the Simplex Method due to it quicker computational speed as a local method and suitability for handling functions that are not smooth. Optimization and simulation tolerances of 1e-5 and 1e-6 respectively are used for all cases. 6 Results For all optimization cases, the optimized condenser water supply temperature and energy savings followed similar trends (Figure 8). Due to the limited number of MC hours in winter and fall seasons, the optimized CWST setpoint often stayed at the current setpoint during these times. The optimized CWST setpoint during MC mode was generally above the current setpoint for all cases. The condenser water supply temperature optimization reduced the plant’s annual energy consumption under all time horizon cases (Table 2), while maintaining the same mass ﬂow rates and temperature changes (and thus, the same heat transfer rates) at all building energy transfer stations. Annual energy savings were achieved from 4.7% (annual optimization) to 5.4% (daily optimization). Because the CWST is controlled in MC mode while the CHWST is controlled in FC mode, the energy savings from CWST optimization occurred during MC mode only. During mechanical cooling, the annual energy savings ranged from 7.4% with annual optimization to 8.6% with daily optimization. 8 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT Figure 8: Condenser water supply temperature optimization results across multiple time horizons. Table 2: Condenser water supply temperature optimization results across multiple time horizons. Plant site energy and savings are relative to the performance of the plant’s current implementation and on an annual basis. Optimization CWST (C) Site Energy Savings Time Horizon Mean SD (MWh) (%) Daily 17.9 2.7 536.8 5.4 Weekly 18.6 2.9 538.9 5.0 Monthly 19.5 2.6 539.6 4.9 Annually 20.5 N/A 541.0 4.7 7 Conclusion Modeling and simulation of DC systems present ample opportunities for energy-efﬁcient cooling systems at district scales. While Modelica is promising for this application, research in this area is still generally lacking, particularly for central plants featuring free cooling from WSEs. This work aimed to ﬁll this gap by demonstrating how the new models contributed to the open-source Modelica Buildings library can be used for detailed energy analysis and optimization of a DC plant with a WSE connected in parallel with the chillers. Through CWST optimization cases, around 5% plant energy was saved with minimal improvements achieved by decreasing the optimization time horizon. This indicates that the seasonal variation on daily through monthly scales does not greatly affect the optimization results, reconﬁrming the results achieved in previous studies [ 18]. We recommend that the plant implement the annual CWST optimization because it is a robust and simple control retroﬁt. The CWST optimizations exemplify retroﬁt strategies that are possible with the detailed Modelica models, but are by no means comprehensive. In the future, we plan to pursue additional retroﬁt strategies with higher energy saving potentials, including integrating the WSE with the chillers, adding thermal storage, and integrating the high-ﬁdelity plant model with a complete district model to evaluate co-operational strategies across buildings and the plant. Acknowledgements This work was supported by the Building Technologies Ofﬁce of the U.S. Department of Energy, under contract numbers DE-AC36-08GO28308 and DE-AC02-05CH11231. Further, this work emerged from the IBPSA Project 1, an international project conducted under the umbrella of the International Building Performance Simulation Association 9 K. Hinkelman et al.: Condenser Water Supply Temperature Optimization A P REPRINT (IBPSA). Project 1 will develop and demonstrate a BIM/GIS and Modelica Framework for building and community energy system design and operation. The authors would also like to thank the campus facilities team for their assistance with data collection, expert advice, and overall support of this project. 8 Appendix See Table 3 for the plant’s nominal information. Table 3: Nominal information for the central cooling plant equipment. Equipment Qty. Nominal Equipment Information Unit Value Chiller 2Nominal Capacity kW 2450 Design Efﬁciency COP – 6.80 (@TCWS = 23C) kW/ton kW/ton 0.517 EvaporatorFlow Rate m3=s 0.0878 Pressure Loss kPa 29.0 CondenserFlow Rate m3=s 0.133 Pressure Loss kPa 64.6 CompressorNumber – 1 Speed Type – Variable Power kW 366 WSE 1Nominal Capacity kW 2820 Design Approach TemperatureC 1.7 Chilled Water SideFlow Rate m3=s 0.121 Pressure Loss kPa 48.4 Condenser Water SideFlow Rate m3=s 0.151 Pressure Loss kPa 83.1 Chilled Water Pump3Head kPa 252 Power kW 29.8 Flow Rate m3=s 0.0883 Speed Type – Variable Condenser Water Pump2Head kPa 338 Power kW 55.9 Flow Rate m3=s 0.126 Speed Type – Constant Cooling Tower2Nominal Capacity kW 2813 Nominal Flow Rate m3=s 0.133 Design TemperatureHot Water C28.2 Cold Water 22.6 Wetbulb 17.8 Number of Cells – 2 Nominal Fan Power kW 22.4 Fan Speed Type – Variable