Modeling and Optimization of the Turbo-Compression Chiller Systems

Article pubs.acs.org/IECR

Modeling and Optimization of the Turbo-Compression Chiller Systems in the LCD Process Changhyun Jeong, Kiwook Song, Jiyeon Nam, and Chonghun Han* School of Chemical and Biological Engineering, Seoul National University, San 56-1, Shillim-dong, Kwanak-gu, Seoul, 151-742, Korea ABSTRACT: A systematic modeling method is proposed to predict the power requirement of a chiller system consisting of a refrigerator. First, with a lack of measurements, a prediction method for the overall efficiency is developed based on thermodynamic theories and reliable assumptions. An artificial neural network (ANN) is employed to predict the overall efficiency. The actual power consumption of the chiller system is then calculated from the overall efficiency. Next, the proposed modeling method is verified by comparing it with measurements, and it is shown to accurately predict power consumption. Finally, the model is applied to the industrial chiller systems of liquid crystal display (LCD) manufacturing processes with the goal of saving energy. Two proposals for saving energy are suggested. The proposed modeling method can be applied to determine better operating conditions for chiller systems in various processes.

1. INTRODUCTION Chiller systems are major contributors to electrical loads. Therefore, there is a big chance that the operating costs can be reduced and the energy efficiency can be increased by improving the chiller system control strategy. The best way to determine the optimal control of a chiller system is to create a detailed model of the process that operates parallel to the actual system. In a cooling plant, there are many control variables that may be adjusted to minimize energy consumption. At any given time, the chiller system can accommodate the cooling load demand using different operation modes and various set points. A good control strategy is an effective way of improving the performance of a chiller system and its associated equipment. Optimal supervisory control refers to the determination of the cooling plant control rule that minimizes the total operating cost. The global optimum plant has been studied by many researchers.1−14 These studies have primarily demonstrated the savings that can be made by employing optimal control. In the past, such optimization research focused on the component (or subsystem) level, in which the analyses encountered complex thermodynamic phenomena and relied on a series of simplified assumptions. Braun15 and Ahn16 developed a methodology for determining the optimal control strategy for a heating, ventilation, and air conditioning (HVAC) system. They assumed that the power consumption of a cooling plant can be adequately represented as a quadratic function of continuous control and uncontrolled variables. In addition, they showed that the power consumption of a chiller can also be adequately represented as a quadratic function of the load and the temperature difference between the water leaving the condenser and that in the evaporator. They also demonstrated that the power of pumps and fans can be represented by a quadratic function of control variables and flow rates. The optimal set point of the control variables that minimizes the power consumption at any given time was found to be a function of the component parameters. Massie17 used an optimal neural-network-based controller for an ice thermal © 2012 American Chemical Society

storage system. The controller consists of four neural networks, three of which map equipment behavior and one which acts as a global controller. Pape et al.18 developed an empirical cost function of the optimal control for the total power consumption of a cooling system. They considered that the cost function depends on the air supply and chilled-water temperatures as well as on the uncontrolled variables. They did not, however, take into account the variation in the condenser water temperature due to the changes in the outdoor air temperature. Very little information about the global optimization of the complete system has been reported in the related literature. Koeppel et al.19 first used a global optimal algorithm (simulated annealing) to develop optimal supervision control guidelines compared to other control strategies. System-based control (in contrast to individual component control) takes into account the interactive natures of the system components and their associated variables. When implemented with an optimal control scheme, the system approach can utilize the knowledge of the interactions in the system to minimize a cost function because this knowledge is not provided to the controller. Numerous studies have shown that system-based optimal control strategies, unlike traditional control strategies, improve the system responses and reduce energy use, and that further improvements are possible when multiple control variables are optimized simultaneously. The use of artificial neural networks (ANNs) is proliferating with remarkable speed in simulation research. In HVAC, the research on ANN has stressed the importance of self-learning in process control, global optimal control, predicting the energy use in complex systems without need for a data acquisition system, and energy management. The objectives of this paper are to construct a model that describes the entire chiller system and to illustrate the optimal Received: Revised: Accepted: Published: 2974

June 8, 2011 January 5, 2012 January 15, 2012 January 16, 2012 dx.doi.org/10.1021/ie201229s | Ind. Eng. Chem. Res. 2012, 51, 2974−2982

Industrial & Engineering Chemistry Research

Article

the compressor. For centrifugal gas compressors, the power can be calculated using eq 1.

operation of the cooling system under a range of uncontrolled variables, including cooling load and ambient conditions.

⎡⎛ ⎞ γ ⎤ ⎛ 3.04 × 10−5 ⎞ P ⎟⎟P1Q1⎢⎜ 2 ⎟ − 1⎥ HP = ⎜⎜ ⎢⎣⎝ P1 ⎠ ⎥⎦ γ ⎝ ⎠

2. CHILLER SYSTEMS The chiller system produces chilled water using electric power as the energy source for clean room (C/R), office air conditioning, and utility equipment cooling load control, and reduces the condensation load of the chiller using the cooling water in the cooling tower. The produced chilled water is supplied to the fabrication facility (FAB) air conditioning, process cooling water (PCW), passivation (PV), module air conditioning, and deionized water (DI). Figure 1 shows a schematic diagram of a chiller system, consisting of a refrigerator, a cooling tower, two pumps, and two circulating systems: a chilled-water cycle and a cooling water cycle. The refrigerator can be divided into two parts, as shown in Figure 2: the mechanical part, consisting of a compressor, an expander or a valve, and two heat exchangers (e.g., an evaporator and a condenser); and the chemical-material part of the refrigerant cycle. The refrigerant circulates continuously within the refrigerator, flowing first through an evaporator and then through a compressor, condenser, valve, and finally back through the evaporator. Figure 3 is a Mollier diagram that shows the physicochemical states (e.g., pressure and enthalpy) of the refrigerant at each section of the chiller system. The curved line in Figure 3 is a saturation curve, where any state is a saturated one. On the internal side of the curve, the temperature and line pressure are constant. To cool the water, phase transitions of the refrigerant occur in the two heat exchangers. In the case where there is no subcooling and no superheating, the temperature and pressure cannot be adjusted at the heat exchangers, and only the phase transitions take place. A compressor and a valve are used to adjust the pressure of the refrigerant so that the phase transitions can occur easily. In the evaporator, refrigerant is evaporated but water is cooled. In the condenser, refrigerant is condensed but water is warmed. Warmed water (cooling water) goes toward the cooling tower. The cooling tower used for the current chiller system is an induced draft type with regard to ventilation, a cross-flow type with regard to air flow vs water flow, and a wet cooling type within the frame of heat transfer. The cooling tower exposes warmed water to the open air, which then cools it again. Therefore, the open air conditions determine the extent of cooling. The dry-bulb temperature, relative humidity, and flow rate of the open air are the main determinants. As the drybulb temperature and relative humidity cannot be changed arbitrarily, they are ambient conditions. The flow rate of the open air is determined by the number of operating fans. Pumps help deliver water. Chilled water is delivered to the location where it will be used, and cooling water is sent to the cooling tower. Performance is a measure of the system ability in comparison with the total power supply to the systems. The power consumption structure can be understood by identifying the power consumption formula of each mechanical unit. The mechanical units of the chiller system include a refrigerator, a cooling tower, and two pumps. The energy consumption of the refrigerator closely resembles the power consumption of the compressor. As units other than the compressor rarely use electricity, the power consumption of the refrigerator is mainly dependent on

⎛ P2 ⎞γ T2 =⎜ ⎟ T1 ⎝ P1 ⎠

(1)

R γ= Cp

Accordingly, the power consumption of the refrigerator is mainly dependent on Q1, P1, and P2. Ambient conditions also affect energy consumption as they determine the extent of water cooling; as such, the heat exchange temperature between the water and the refrigerant depends on the ambient conditions. Temperature and pressure are interdependent. Consequently, ambient conditions influence the pressure of the refrigerant and then influence the power consumption of the compressor. Many previous studies have suggested prediction modeling of the compressor power, but this study proposes a method to construct the chiller system model which has unknown variables in the industrial process.

3. MODELING In the interest of conserving energy in chiller systems, it is important to predict the energy consumption in terms of the process variables that may be associated with the operation of the systems. To calculate the overall energy consumption, the power consumptions of the refrigerator, cooling tower, and pump should be calculated. However, 90% of the power used by the chiller system is used by the refrigerator. Therefore, to predict energy consumption, we focus on making a refrigerator model.

Figure 1. Schematic of the chiller system.

The power consumption formulas of the cooling tower and pump are clear and accurate, but the power consumption of the refrigerator is complicated because it consists of four units, and because the refrigerant is always circulating with a phase transition. Assuming the overall efficiency of the refrigerator system from the viewpoint of power consumption is easier than calculating and summing up the power of each unit. Therefore, the major modeling aspect of the chiller system shown in Figure 1 is to accurately predict the overall power consumption of the chiller system by estimating the overall efficiency of the refrigerator system. 2975

dx.doi.org/10.1021/ie201229s | Ind. Eng. Chem. Res. 2012, 51, 2974−2982


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2. The case where the chilled-water-supply temperature differed from the measured temperature was assumed to be a dynamic condition (operation mode change) to supply the chilled water at the set temperature. Steady-state data were used for modeling. The power prediction became inaccurate in the following operation cases, assumed to be dynamic states: • chiller restart or shutdown • abrupt change in the operating condition • change in the chilled-water-supply condition (temperature, flow rate) To predict the power consumption using the model, validation and optimization were conducted under the general chiller operation condition. 3.1. Definition of Overall Efficiency. The range of the overall efficiency is restricted to the refrigerator. In the strict

Figure 2. Schematic of the refrigerant circulating system.

Table 1. Variables Used for Modeling a Chiller System system

measured variables

ambient conditions

necessary variables for modeling

refrigerator

pressure and temperature at evaporator and condenser flow rate of chilled water and cooling water

dry-bulb temperature relative humidity

temperature difference in the chilled water and cooling water

altitude

volumetric flow rate of refrigerant pressure at the suction of the compressor pressure at the discharge of the compressor

Figure 3. Mollier diagram.

The data of a one-year operation in 2009 was used for modeling. The data included the chilled-water-supply/return temperature, chilled-water flow rate, cooling water supply/ return temperature, cooling water flow rate, evaporator temperature/pressure, condenser temperature/pressure, compressor discharge temperature, compressor current, and chiller power. The amount of the data was large because all the data were measured in minutes, and raw data were not suitable for use because the data did not represent specific values. Therefore, data reconciliation was required. Steady-state data were used for the modeling, assuming the following: 1. The condition where the chilled-water-supply condition (supply temperature) and measured temperature were not significantly different from each other was assumed as the steady state, for the prediction of the chiller power consumption under normal chiller operation.

Figure 4. Prediction structure of the overall efficiency and the actual power consumption.

Table 2. ANN Configuration input node refrigerator flow rate (chilled water in/out temperature and flow rate)

output node ε (compressor overall efficiency)

prediction structure ANN modeling (1 hidden layer, 15 nodes)

pressure at evaporator pressure at condensor

sense, a refrigerator is separate from the cooling tower and two pumps, which play an ancillary role. The major performance indices of the refrigerator are the efficiencies, such as the unit efficiencies of the compressor, expander, or heat exchangers, and the overall efficiency of the entire refrigerator. The unit 2976



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Figure 5. ANN model of the chiller system.

Table 3. Standard Design Data for the Turbo Compression Chiller chillers cooling water leaving/return temp (°C) chilled water supply/return temp (°C) flow rate of cooling water (m3/h) flow rate of chilled water (m3/h) refrigeration tons (USRT) revolutions per minute (rpm) refrigerant

type 1, type 3

type 2, type 4

5/15 32/37 605 1452 2000 9900 R-134a

13/18 32/37 1210 1452 2000 12000 R-134a

Figure 6. Comparison of measured and predicted values of power consumption (refrigerator type 2).

the compressor. The power delivered to the electric motor is obtained from the power balances for the compressor. Wreal can be measured directly using a wattmeter, and Wideal denotes the minimum power required for compression under an adiabatic and reversible compression process, given in eq 1. In this study, the ideal compression system (whose overall efficiency is equal to 1) follows from the assumptions that there are no energy losses due to friction or leakage in the compressor, bearings, gears, couplings, and other auxiliary components, and that the efficiency of the electric motor is always 1. In general, actual compression systems require more power than do ideal compression systems, and their overall efficiency is thus less than 1, typically ranging from 0.2 to 0.6. When the overall efficiency is calculated using eqs 2 and 1, due to the lack of measurements at the sensors, the unknown variables should be predicted. 3.2. Prediction of the Unknown Variables. The ideal power consumption is needed to estimate the overall efficiency. Not all the variables for estimating power consumption are available. The variables measured in a refrigeration system are the temperature and pressure in the compressor, the temperature and pressure in the heat exchanger (evaporator and

efficiencies, such as the adiabatic or polytropic efficiencies, represent the performance of a compression or expansion stage and can be calculated directly after measurement of the temperatures and pressures at the suction and discharge of a stage. As the energy consumption of the refrigerator closely resembles the power consumption of the compressor, the overall efficiency approximates the efficiency of the compressor. The overall efficiency, ε, can be defined for the refrigeration system as follows: ε=

Wideal Wideal = Wreal Welec + Wloss

(Wideal < Wreal) (2)

where Welec is for the actual power required for compression and Wloss stands for all the energy losses in the bearings, couplings, electric motors, and other auxiliary components of

Table 4. Some Data of Chiller 23 (Type 4) for Training by ANN Model time

Tchill_out [°C]

Tchill_in [°C]

P1 [kg/cm2]

P2 [kg/cm2]

T1 [°C]

T2 [°C]

mchill [m3/h]

Wreal [kW]

current (A)

18/10/2008, 15:42 18/10/2008, 15:43 18/10/2008, 15:44 18/10/2008, 15:45 18/10/2008, 15:46 22/5/2009, 17:31 22/5/2009, 17:33 22/5/2009, 17:35 22/5/2009, 17:36

12.80 12.50 12.60 12.50 12.50 12.50 12.60 12.60 12.60

14.50 14.20 14.50 14.40 14.60 15.00 15.00 15.00 15.00

3.50 3.25 3.41 3.41 3.39 3.37 3.37 3.37 3.38

6.09 6.14 6.46 6.32 6.48 5.46 5.46 5.48 5.47

12.50 10.80 11.90 11.90 11.80 11.60 11.70 11.60 11.70

27.20 27.30 29.00 28.30 29.10 24.00 23.90 24.10 24.20

1191.5 1259 1141 1159.5 1165 1106.5 1151 1130 1155.5

1418.75 1423.75 1430 1422.5 1399.38 1495 1465.63 1453.13 1468.75

43.00 42.00 46.00 45.00 46.00 45.00 45.00 44.00 45.00

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the heat transfer can be calculated. In both the evaporator and condenser, all the changes caused by the heat transfer from water are phase transitions and temperature changes in the refrigerant. Temperature change is calculated using eq 3, and phase transition is calculated using eq 4. (4)

ΔHref = ΔHwater

(5)

ΔHref = c ref mref ΔTref + ΔTref mref

(6)

ΔHwater = c waterm water ΔTwater

(7)

c m ΔT mref = water water water c ref ΔTref + ΔHvap

(8)

Using eq 8, it is possible to calculate the flow rate of the refrigerant (mref) if ΔHvap and the specific heat capacity of water (cwater) are known. Apart from the refrigerant that flows into the compressor, the pressure values before and after the compression stage are required. In Figure 3, the pressure values at the inlet and outlet of the compressor are different from those of the evaporator and condenser in the ideal compression cycle, but the chilling cycle shows that the actual pressure values are equal. Accordingly, Pin and Pout are assumed to be the pressure values of the evaporator and condenser, respectively, in the calculation. 3.3. Prediction Structure of the Overall Efficiency and Power Consumption of the Refrigerator. This section presents a prediction structure of the overall efficiency defined in section 3.2 and of the power consumption of a chiller system.

Figure 7. Comparison of measured and predicted values of power consumption (refrigerator type 4).

condenser), the flow rates of the chilled and cooling water, and the return and feed temperatures of the chilled and cooling water, as shown in Table 1. It is necessary to estimate the variables that are not measured by means of compounding the values of the measured variables and extra information. In the prediction of the unknown variables, some state (process) variables are predicted with a given set of the manipulated variables and assumptions. The predicted state variables are those that affect the overall efficiency and actual powers but cannot be measured directly when there is a change in the other variables because they are neither manipulated variables nor variables with fixed values. It is assumed that the state variables can be predicted from other variables, and that the assumptions are maintained in the state variable prediction. To calculate the power consumption, the flow rates, temperatures, and pressures at the discharge and suction of the compression stage should be measured. In addition, the density, specific heat capacity, and heat of vaporization of the refrigerant passing through the compression stage should be determined. The temperatures and pressures at the discharge and suction of the compressor, however, are not available or are not even measured. Only the temperature and pressure in the compressor are measured. As shown in Figure 3, the refrigerant pressure is constant while it passes through the evaporator and condenser. As such, the suction pressure of the compressor is similar to the pressure in the evaporator, and the discharge pressure of the compressor is similar to the pressure in the condenser. The other values for the estimation of the overall efficiency can be calculated using the measured variables. In the case of the refrigerant flow rate, a simple heat transfer equation is used.

ΔH = cmΔT

ΔH = ΔHvapm

Wreal =

Wideal ε

(9)

Figure 4 shows the overall prediction structure of the overall efficiency and the actual powers of a refrigeration system. To predict the overall efficiency, the overall efficiency has to be represented as a function of the various process variables that affect it, some of which may not be known. The overall efficiency, however, can finally be expressed as a function of the measured variables because the unknown variables are described as functions of the measured variables (step 1). In the efficiency prediction (step 2), the overall efficiency of a refrigeration system is predicted using an empirical modeling tool, such as ANN, along with a given set of predicted state variables and manipulated variables. Through this prediction structure, the overall efficiency and actual power consumption of the refrigeration system can be evaluated when the manipulated variables and ambient conditions change. In the prediction of efficiency (step 2), empirical modeling methods are presented to predict the overall efficiency. The prediction models of the overall efficiency can be constructed by identifying the model parameters (e.g., weights) that minimize the errors between the target values from the past operations of a refrigeration system and the calculated values from an empirical model. In this study, the ANN algorithm is employed as an alternative method for predicting the overall efficiency. The ideal power consumption can be calculated as a function of the main-effect variables, which are abstracted from the state variables using eq 1. Equation 1 consists of the pressures at the suction and discharge of the compressor, and of the flow rate of

(3)

The ΔH of the water is transferred to the refrigerant in the heat exchanger. Assume the heat transfer efficiency of the heat exchanger and insert that value into eq 3; then the quantity of 2978



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Full interconnections of all nodes between the layers are assumed for developing the network. Artificial neural network model is a typical type of black-box model. However, one might remove the nodes and weights without sacrificing the prediction accuracy. Simplified models show key links between the input and output variables, providing much more valuable physical insight into the process. Many works have shown optimization of the network configuration. It is well-known that variation of the number of hidden layer nodes has a significant effect on the predictive ability of the network.22 Commonly, authors have optimized ANN performance by varying the number of nodes in the hidden layer(s) and selecting the architecture with the highest predictive ability.23−25 A sensitivity analysis approach is used to identify the effective interconnections in ANN and to eliminate the redundant part from the ANN. The authors identified lower salient weights/ interconnections by their sensitivity and checked cross validation using standard deviation and correlation coefficeints.26−28 The data from the four chiller types listed in Table 3 in an LCD process in Korea were tested from June 1, 2008 to May 31, 2009 to verify the correctness of the ANN model. Table 4 lists some of these data. Figures 6 and 7 illustrate the prediction results from the ANN models for the refrigeration system. The figures show excellent prediction performance. As shown in Figures 6 and 7, the root-mean-square errors of the actual power consumption are 0.97 and 0.92, respectively. This ANN model has a small number of input (fewer than six) and output (fewer than six) nodes; minimal effort is thus required to identify the optimal model structure for the learning procedure. Hence, the proposed modeling method can be easily applied to similar refrigeration systems with minimal effort.

the refrigerant. The flow rate of the refrigerant is not measured but was calculated using eq 8 as a function of the flow rate of the chilled water, the temperature change in the chilled water, and the range of superheating of the refrigerant at the evaporator. The specific heat capacity of water, the specific heat capacity of the refrigerant, and the heat of vaporization of the refrigerant are also essential data determined by physical states like pressure and temperature. Assuming a saturated state at the evaporator, the pressure can be determined when the temperature is known, and vice versa. Thus, only either the temperature or pressure has to be known. As mentioned above, the suction pressure of the compressor is approximated by the pressure in the evaporator, and the discharge pressure of the compressor is approximated by the pressure in the condenser. Consequently, the main-effect variables are the pressure at the evaporator, the pressure at the condenser, the flow rate of the chilled water, and the temperature difference in the chilled water through the evaporator. 3.4. Artificial Neural Networks. Although many chiller power predicted models have been presented, the ANN model has a good accuracy since it can manage nonlinearity.20 The ANN prediction structure is shown in Table 2. The ANN configuration is shown in Figure 5. Wprediction = ANN function(P2 , P2 , mref (Tchill_in , Tchill_out , mchill ), ε)

(10)

After calculating the ideal power consumption as a function of the main-effect variables, that value is inserted into the ANN model (especially the back-propagation training algorithm), which determines the overall efficiency by mapping the ideal power consumption onto a predicted power consumption. The back-propagation ANN is a multilayer framework that requires the application of the chain rule to determine the variation of weight value. Figure 5 shows the three-layer network which is consists of an input layer, a hidden layer, and an output layer. More detailed ANN theory can be referenced.21 The sigmoid transfer function was used in the ANN modeling because the sigmoid transfer function is used widely. The relationship can be expressed as hj =

4. OPTIMIZATION A chiller system that uses a centrifugal chiller was studied. The nominal system parameters are listed in Table 3. Types 1 and 2 and types 3 and 4 are made by the same production company, but types 1 and 3 and types 2 and 4 have the same production data specifications. The following two optimal control cases were studied from the viewpoint of energy savings: case 1: selection of the number of chillers that must be operated to supply the chilled water to the places where it is to be used when the input conditions (chilled-water volume, chilled-water temperature, evaporator pressure, and condenser pressure) have been determined for each chiller using the minimum power case 2: determination of the operation condition for each chiller to minimize the total power The chiller system consists of thirteen 7 °C chillers and six 14 °C chillers in Figure 8. The number of chillers to be operated is determined by the operator in a heuristic way. The chillers with low power consumption are empirically selected and operated. A maximum of 5 (7 °C)/4 (14 °C) chillers are operated in the summer and a minimum of 2 (7 °C)/2 (14 °C) chillers are operated in the winter. The optimal operation based on modeling is needed because the chiller performance is not constant but changes; as such, the heuristic chiller operation has its limits. The characteristics of the equipment and the operation conditions are considered in the modeling-based chiller selection, and the optimal chiller operation is made possible by the selection of efficient chillers based on the recent data.

2 −1 1 + exp(− 2( ∑i (uj , ixi + b1, j)))

∀j yk =

2 −1 1 + exp(− 2( ∑j (wk , jhj + b2, k )))

(11)

∀k

where u is the weight between input and hidden layers, w is the weight between hidden and output layers, b1 and b2 are the bias, and h is the hidden node. Table 2 shows the configuration of the ANN model. The ANN model captures the nonlinear relationship between the process variables and the overall efficiency. In the ANN model, the number of nodes in the hidden layer is the major parameter, and the numbers of latent variables and nodes are determined for the values that minimize the prediction error when using the input variable data sets. The actual power consumption can then be calculated from the predicted overall efficiency. 2979



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Figure 8. Structure of the chiller systems.

The total power consumption is reduced in two aspects. First, the chiller with low power consumption in the supply conditions (chilled-water supply/return temperature, chilledwater flow rate, and chilled-water supply temperature) is selected for the supply that uses the chiller model (Table 6). The current chiller has been selected based on the operator’s experience, and it may not be the optimal chiller for the condition. Therefore, the power consumption can be reduced by 6% if the chiller will be selected using the efficiency list from the optimization formulation. In the case of the 7 °C water supply, chillers 6, 11, and 14 were in operation. The total supply volume was 2136 Nm3/h, and the efficiencies of the chillers under the current operation conditions were 0.17, 0.17, and 0.27, respectively. (The modeling error was 7%, and the power consumption of the

Four types of chillers were modeled in the aforementioned method. For the validation of the entire system, the results were applied to the system with 19 chillers in all. In the actual operation in November 2009, 8 of 19 chillers were operated, and they consumed 7530 kW power. The same operation conditions (number of chillers operated, chilled-water-supply volume, chilled-water-supply/return temperature, evaporator pressure, and condenser pressure) were applied to the chiller modeling system. When eight chillers were operated, 7635 kW power was consumed and the error compared with the actual operation was about 3.96%. The error was 3% if the big error of chiller 14 (13%) was excluded and about 2.16% if the error of chiller 20 (7%) was excluded. The modeling was conducted using the data of chillers 2, 12, 16, and 23. The error was calculated when the same-type chillers were verified (Table 5). 2980



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operation conditions, or to determine the operation for energy saving. In this study, an energy-saving method was derived from modeling and case studies, but its application to the field will bring about the calculated energy-saving effect and will help those who are interested.

model was 3856.3 kW.) Chillers 12, 13, and 14 were selected when the chiller operation was optimized for optimal chiller efficiency. The efficiencies of the chillers (0.21, 0.22, and 0.27) were improved from the previous operation, and the power consumption was 3279 kW, which was reduced by about 12%. Second, the power consumption can be reduced by changing the chiller operation conditions in the individual chillers being operated (Table 7). The control variables in the individual chillers were the chilled-water-supply temperature and volume. Therefore, the power consumption decreased by about 1.5% when the chilled-water-supply temperature was changed to attain the optimal efficiency of the chiller, which was operated in the same way.

5. CONCLUSIONS The optimal use of electricity in the chiller system is important for the economical operation of the system. A mathematical model of the optimal operation of the chiller system is developed based on the energy analysis of the main dynamic facilities, including the refrigerator, cooling tower, and pumps. The recent research favors the use of a multivariable, systembased control approach. Neural networks are especially appropriate for investigating the complex nonlinear control system, using the ability of the ANN to perform complex nonlinear mapping. A term called “overall efficiency” is suggested in this paper to analyze the power consumption of the refrigerator in a chiller system. Consequently, in this model, many variables, such as the pressures at the evaporator and condenser, the flow rate and temperature difference of the water, the volumetric flow rate, and power consumption, and some ambient conditions can be manipulated in a simulation model. In other words, the power consumption of the overall chiller system can be predicted from the aforementioned variables. The desired value (power consumption of the chiller system) is obtained despite a lack of measurements, and the changes in power consumption in accordance with the various input variables can be observed. Moreover, the results obtained by manipulating the input variables are easily visualized. For the overall cooling system, the optimization problem is formally stated as the minimization of power consumption while maintaining the comfort conditions at the target production standard during operation. The optimal control includes the cooling (condenser) water flow rates and the temperature of the cooling water. Reducing the flow rate of the cooling water and decreasing the temperature of the cooling water decreases the energy consumption of the entire chiller system. Further research on real-time monitoring and optimization is needed so that this model can be used more comfortably.

Table 5. Chiller System Modeling Validation chiller

predicted (kW)

measured (kW)

error (kW)

error (%)

2 12 13 16 22 23 total

1480.77 1015.76 1124.05 875.16 641.829 726.163 5863.74

1436 1009.1 1090.3 888 621.9 719.6 5764.9

44.77 6.66 33.75 12.83 19.92 6.56 124.51

2.53 0.66 3.09 1.44 3.20 0.91 2.16

Table 6. Optimization Results of Case 1 no. of refrigerator efficiency flow rate (Nm3/h) total power (kW)

operation

optimal case 1

6, 11, 14 0.17, 0.17, 027 2136 3853.3

12, 13, 14 0.21, 0.22, 0.27 2136.5 3279 (12%↓)

Table 7. Optimization Results of Case 2 no. of refrigerator efficiency flow rate (Nm3/h) total power (kW)

operation

optimal case 2

21, 23 0.26, 0.23 2700 1009.4

21, 23 0.31 (full), 0.21 2700 982 (2.7%↓)

Two 14 °C chillers (21 and 23) were operated, with efficiency values of 0.26 and 0.23 and 1009.482 kW power consumption. The efficiency values and loads of the chillers were plotted so that the chiller operation conditions can be changed for better efficiency without changing the chillers, and they had a proportional relationship. Accordingly, because chiller 21 has better efficiency when the two chillers are operated under a full load, exchanging the operation conditions of the two chillers resulted in efficiency values of 0.31 and 0.21, respectively, and 982 kW power consumption. The improvement was about 2.7%. The full-load operation limit is determined by the chiller capacity, but it can be exceeded by a heuristic error of the operators. With the chiller system modeling error ranging from 2 to 3%, the total power consumption reduction was about 7.5%, which indicated that the total power consumption was reduced even when the modeling error was considered. The LCD factories do not consider the operation conditions using this chiller model; as such, chiller modeling can be used to predict the operation of the chiller system according to the changes in the

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AUTHOR INFORMATION

Corresponding Author

*Tel.: 82-2-880-1887. E-mail: [email protected].

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ACKNOWLEDGMENTS

The authors gratefully acknowledge the program for the Korea Science and Engineering Foundation provided through the Advanced Environmental Biotechnology Research Center (R11-2003-006) at Pohang University of Science and Technology, the Brain Korea 21 Project initiated by the Ministry of Education of Korea (ME), the Energy Resources Technology Development Project provided through the Korea Energy Management Corporation/Ministry of Knowledge Economy of Korea (MKE), the Industrial strategic Technology Development Program Design of topside LNG regasfication plant of LNG FSRU (10031883) by the MKE, and the LNG Plant R&D Center funded by the Ministry of Land, 2981



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(10) Cumali, Z. Global optimization of HVAC system operations in real time. ASHRAE Trans. 1988, 94 (1), 1729−1744. (11) Braun, J. E.; Klein, S. A.; Mitchell, J. W.; Beckman, W. A. Applications of optimal control to chilled water systems without storage. ASHRAE Trans. 1989, 95 (1), 663−675. (12) Braun, J. E. Reducing energy costs and peak electrical demand through optimal control of building thermal capacitance. ASHRAE Trans. 1990, 96 (2), 876−888. (13) Kintner-Meyer, M.; Emery, A. F. Optimal control of an HVAC system using cold storage and building thermal capacitance. Energy Build. 1995, 23 (1), 19−31. (14) King, D. J.; Potter, R. A. Description of a steady-state cooling plant model developed for use in evaluating optimal control of ice thermal energy storage systems. ASHRAE Trans. 1998, 104 (1), 42− 53. (15) Braun, J. E.; Klein, S. A.; Beckman, W. A.; Mitchell, J. W. Methodologies for optimal control of chilled water systems without storage. ASHRAE Trans. 1989, 95 (1), 652−662. (16) Ahn, B. C.; Mitchell, J. W. Optimal control development for chilled water plants using a quadratic representation. Energy Build. 2001, 33 (4), 371−378. (17) Massie, D. D. Optimization of a building’s cooling plant for operating cost and energy use. Int. J. Therm. Sci. 2002, 41 (12), 1121− 1129. (18) Pape, F. L. F.; Mitchell, J. W.; Beckman, W. A. Optimal control and fault detection in heating, ventilating, and air-conditioning systems. ASHRAE Trans. 1991, 97 (1), 729−745. (19) Koeppel, E. A.; Klein, S. A.; Mitchell, J. W.; Flake, B. A. Optimal supervisory control of an absorption chiller system. HVAC&R Res. 1998, 1 (4), 325−342. (20) Swider, D. J. A comparison of empirical based steady-state models for vapor-compression liquid chillers. Appl. Therm. Eng. 2003, 23, 539−556. (21) Hagen, M. T.; Demuth, H. B. Neural Network Design; PWS Publishing Co.: New York, 1996. (22) Murtoniemi, E.; Merkku, P.; Kinnunnen, P.; Leiviskä, K.; Yliruusi, J. Effect of neural network topology and training end point in modelling the fluidised bed granulation process. Int. J. Pharm. 1994, 110, 101−108. (23) Plumb, A. P.; Rowe, R. C.; York, P.; Doherty, C. The effect of experimental design on the modelling of a tablet coating formulation using artificial neural network. Eur. J. Pharm. Sci. 2002, 16, 281−288. (24) Hussain, A. S.; Yu, A.; Johnson, R. D. Application of neural computing in pharmaceutical product development. Pharm. Res. 1991, 8, 1248−1252. (25) Jiao, T.; McCall, T. W.; Baichwal, A. R.; Meyer, M. C. Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets. Pharm. Dev. Technol. 2002, 7, 373−379. (26) Lee, H.; Park, C. H. A pruning algorithm of neural networks using impact factor regulation. Proc. ICONIP 2002, 5, 2605−2609. (27) Sabo, D.; Yu, X. H. A new pruning algorithm for neural network dimension analysis. IJCANN 2008, 3313−3318. (28) Ash, T. Dynamic neuron creation in back-propagation networks. Connect. Sci. 1989, 1, 365−375.

Transportation and Maritime Affairs (MLTM) of the Korean government.

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NOMENCLATURE HP = ideal power consumption of compressor [hp] P1 = pressure of evaporator = pressure at suction of compressor [kg/cm2] P2 = pressure of condenser = pressure at discharge of compressor [kg/cm2] T1 = temperature of evaporator = temperature at suction of compressor [°C] T2 = temperature of condenser = temperature at discharge of compressor [°C] Q1 = volumetric flow rate of refrigerant [m3/h] R = gas constant [J/g·K] Cp = specific heat capacity of refrigerant at constant pressure [J/g·K] ε = efficiency of compressor Wideal = minimum power required for compression under an adiabatic and reversible compression process [kW] Wreal = real power consumption of compressor, measured directly using a wattmeter [kW] ΔH = heat difference [J/h] ΔHref = heat difference in the refrigerant [J/h] ΔHwater = heat difference in the water [J/h] cref = specific heat capacity of refrigerant [J/g·K] cwater = specific heat capacity of water [J/g·K] mref = flow rate of refrigerant [m3/h] mwater = flow rate of water [m3/h] ΔT = temperature difference [°C] ΔTref = temperature difference in refrigerant [°C] ΔTwater = temperature difference in water [°C] ΔHvap = latent heat of vaporization [J/g] mchill = flow rate of chilled water [m3/h] Tchill_in = inlet temperature in chilled water [°C] Tchill_out = outlet temperature in chilled water [°C]

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REFERENCES

(1) Lee, K. J.; Bang, K. H. A Theoretical Evaluation of the Effect of Refrigerant Charge on the Performance of Vapor-Compression AirConditioning System. SAREK J. 2000, 12 (5), 486−493. (2) Lee, S. H.; Woo, S. W.; Kim, B. S. Thermodynamic Properties of Refrigerant R-134a and Simulation of Simple Refrigeration Cycles. SAREK J. 1991, 79−86. (3) Han, I. S.; Han, C. H. Modeling of Multistage Air-Compression Systems in Chemical Processes. Ind. Eng. Chem. Res. 2003, 42, 2209− 2218. (4) Lau, A. S.; Beckman, W. A.; Mitchell, J. W. Development of computer control routines for a large chilled water plant. ASHRAE Trans. 1985, 91 (1), 780−791. (5) Johnson, G. A. Optimization techniques for a centrifugal chiller plant using a programmable controller. ASHRAE Trans. 1985, 91 (2), 835−847. (6) Hackner, R. J.; Mitchell, J. W.; Beckman, W. A. HVAC system dynamics and energy use in buildings―Part I. ASHRAE Trans. 1984, 91 (2), 523−535. (7) Hackner, R. J.; Mitchell, J. W.; Beckman, W. A. HVAC system dynamics and energy use in buildings―Part II. ASHRAE Trans. 1985, 91 (2), 781−793. (8) Jackson, W. L.; Chen, F. C.; Hwang, B. C. The simulation and performance of a centrifugal chiller. ASHRAE Trans. 1987, 93 (2), 1751−1767. (9) Spethmann, D. H. Optimized control of multiple chillers. ASHRAE Trans. 1985, 91 (2), 848−856. 2982


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