Cost optimization system and method

Abstract

To minimize a total cost of a cooling tower system including cost of a makeup water amount for a cooling tower.SOLUTION: A cost optimization system comprises: a power cost calculation unit 85 that estimates a fan power cost of a cooling tower, a power cost of a cooling water pump, and compressor power of a freezer; a makeup water cost calculation unit 86 that estimates a makeup water cost for the cooling tower; and a solving unit 87 that while changing a fan frequency of the cooling tower and a flow rate of cooling water, causes a cooling tower outlet temperature estimation unit 80, a freezer outlet temperature estimation unit 81, a cooling tower power estimation unit 82, a cooling water pump power estimation unit 83, a freezer power estimation unit 84, the power cost calculation unit 85, and the makeup water cost calculation unit 86 to execute processing to find the fan frequency of the cooling tower and the flow rate of the cooling water that minimize the sum of the fan power cost of the cooling tower, the power cost of the cooling water pump, the compressor power cost of the freezer, and the makeup water cost of the cooling tower.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present invention relates to a cost optimization system and method for optimizing the cost of cooling tower systems used in air conditioning systems. [Background technology]

[0002] Conventionally, in air conditioning control in facilities such as buildings, chillers have been used as devices for obtaining chilled water for cooling. In an air conditioning system such as that shown in FIG. 14, chiller 100 generates chilled water. The chilled water generated by chiller 100 is supplied to air conditioner 101. Air cooled by air conditioner 101 is sent to the space to be controlled (room). Meanwhile, the chilled water sent to air conditioner 101 is heated and returned to chiller 100. Chiller 100 re-cools the water returned from air conditioner 101 and sends it to air conditioner 101.

[0003] Furthermore, chiller 100 gives heat generated when producing chilled water (heat amount for producing chilled water) to cooling water supplied from cooling tower 102, and returns the cooling water to cooling tower 102. Cooling tower 102 rotates a fan to release the heat given to the cooling water into the outside air, and supplies the cooled cooling water to chiller 100. The cooling water supplied from cooling tower 102 to chiller 100 is circulated by cooling water pump 103.

[0004] 14, if the amount of cooling water is reduced to reduce the power consumption of the cooling water pump 103, the power required for the compressor in the chiller 100 increases, resulting in increased power consumption. Also, if the airflow rate of the cooling tower 102 is reduced to reduce the power consumption of the fan in the cooling tower 102, the power required for the compressor in the chiller 100 increases, resulting in increased power consumption. Thus, a cooling tower optimization problem exists, which is how to balance the trade-off between the power consumption of the chiller 100, the cooling tower 102, and the cooling water pump 103.

[0005] In the cooling tower optimization problem, the air volume of the cooling tower 102 and the flow rate of the cooling water pump 103 are adjusted to maintain the heat content of the chilled water produced by the chiller 100 while minimizing the sum of the fan power of the cooling tower 120, the power of the cooling water pump 103, and the compressor power of the chiller 100.

[0006] Specifically, the objective function is the sum of the fan power of cooling tower 102, the power of cooling water pump 103, and the compressor power of chiller 100. The constraint equations are a function indicating the relationship between the fan power of cooling tower 102 and the fan frequency of cooling tower 102, which is obtained by regression analysis from actual data, a function indicating the relationship between the power of cooling water pump 103 and the cooling water flow rate, which is obtained by regression analysis from actual data, a function indicating the relationship between the compressor power of chiller 100, the cooling water flow rate, the cooling water temperature, and the amount of produced heat (when the amount of produced heat is constant (current value)), and a function indicating the relationship between the cooling water temperature change, the wet-bulb temperature, and the fan frequency of cooling tower 102 (when the wet-bulb temperature is constant (current value)).

[0007] In this way, an optimization problem is formulated, and the fan frequency of the cooling tower 102 and the flow rate of the cooling water pump 103 that minimize the objective function are derived (see Non-Patent Document 1). In the conventional technology disclosed in Non-Patent Document 1, the amount of evaporation of cooling water changes when at least one of the cooling water flow rate, cooling water temperature, air volume, air temperature, and humidity changes. In other words, when high-humidity air is discharged from the cooling tower 102, water needs to be replenished to the cooling tower 102 to compensate for the amount of cooling water that evaporates. A change in the amount of evaporation of cooling water means a change in the amount of make-up water, which means a change in the total cost of the cooling tower system, including the cost of the make-up water amount. However, the conventional technology has an issue in that it has not been possible to minimize the total cost, including the cost of the make-up water amount for the cooling tower 102. [Prior art documents] [Non-patent literature]

[0008] [Non-Patent Document 1] Khin Zaw,Kenichi Matsuoka,“Minimizing primary energy consumption in district cooling system: a showcase of the impact of online optimization control”,ASHRAE ANNUAL CONFERENCE 2016 JUNE 25-29,2016 ST.LOUIS Summary of the Invention [Problem to be solved by the invention]

[0009] The present invention has been made to solve the above-mentioned problems, and has an object to provide a cost optimization system and method that can minimize the total cost of a cooling tower system, including the cost of makeup water for the cooling tower. [Means for solving the problem]

[0010] The cost optimization system of the present invention includes a cooling tower outlet temperature estimation unit configured to input a cooling tower inlet condition into a cooling tower process model to obtain an estimated value of the cooling water outlet temperature of the cooling tower and an estimated value of the cooling water outlet flow rate; a chiller outlet temperature estimation unit configured to input a chiller cooling water inlet temperature, the cooling water flow rate, and chiller compressor power into a chiller process model to obtain an estimated value of the cooling water outlet temperature of the chiller; a cooling tower power estimation unit configured to input the cooling tower fan frequency into a cooling tower power model to obtain an estimated value of the cooling tower fan power; a chilled water pump power estimation unit configured to input the chilled water flow rate into a chilled water pump power model to obtain an estimated value of the chilled water pump power; a chiller power estimation unit configured to input the chiller cooling water outlet temperature into a chiller power model to obtain an estimated value of the chiller compressor power; and an estimate of the cooling tower fan power cost and a previous estimate based on the calculation results of the cooling tower power estimation unit, the chilled water pump power estimation unit, and the chiller power estimation unit. a power cost calculation unit configured to calculate an estimated value of the cooling tower's make-up water cost by using the difference between the cooling tower's coolant inlet flow rate and the estimated value of the cooling water outlet flow rate obtained by the cooling tower outlet temperature estimation unit as an estimated value of the make-up water amount of the cooling tower; and a solution finding unit configured to cause the cooling tower outlet temperature estimation unit, the chiller outlet temperature estimation unit, the cooling tower power estimation unit, the coolant pump power estimation unit, the chiller power estimation unit, the power cost calculation unit, and the make-up water amount cost calculation unit to execute processing while changing at least one of the cooling tower's fan frequency and the coolant flow rate, thereby determining the cooling tower fan frequency and the coolant flow rate that minimize the sum of the estimated value of the cooling tower fan power cost, the estimated value of the cooling water pump power cost, the estimated value of the chiller compressor power cost, and the estimated value of the cooling tower make-up water amount cost.

[0011] Furthermore, one configuration example of the cost optimization system of the present invention is characterized by further comprising: the cooling tower process model that models the relationship between the inlet condition of the cooling tower and the outlet temperature of the cooling water of the cooling tower; the cooling tower power model that models the relationship between the fan frequency of the cooling tower and the fan power of the cooling tower; the cooling water pump power model that models the relationship between the flow rate of the cooling water and the power of the cooling water pump; the chiller process model that models the relationship between the inlet temperature of the cooling water of the chiller, the flow rate of the cooling water, the compressor power of the chiller, and the outlet temperature of the cooling water of the chiller; and the chiller power model that models the relationship between the outlet temperature of the cooling water of the chiller and the compressor power of the chiller. In addition, in one configuration example of the cost optimization system of the present invention, the inlet conditions of the cooling tower include the inlet air temperature of the cooling tower, the relative humidity of the inlet air, the inlet air flow rate calculated from the cooling tower fan rotation speed, the cooling water flow rate, and the cooling water inlet temperature.

[0012] The cost optimization method of the present invention includes a first step of inputting a cooling tower inlet condition into a cooling tower process model to obtain an estimated value of the cooling water outlet temperature of the cooling tower and an estimated value of the cooling water outlet flow rate; a second step of inputting a chiller cooling water inlet temperature, the cooling water flow rate, and the chiller compressor power into a chiller process model to obtain an estimated value of the chiller cooling water outlet temperature; a third step of inputting the cooling tower fan frequency into a cooling tower power model to obtain an estimated value of the cooling tower fan power; a fourth step of inputting the cooling water flow rate into a chiller pump power model to obtain an estimated value of the chiller pump power; a fifth step of inputting the chiller cooling water outlet temperature into a chiller power model to obtain an estimated value of the chiller compressor power; and a previous step of calculating a pre-determined value based on the calculation results of the third step, the fourth step, and the fifth step. a sixth step of calculating an estimated value of the cooling tower fan power cost, an estimated value of the cooling water pump power cost, and an estimated value of the chiller compressor power cost; a seventh step of calculating an estimated value of the cooling tower makeup water cost by using the difference between the cooling water inlet flow rate of the cooling tower and the estimated value of the cooling water outlet flow rate obtained in the first step as an estimate of the cooling tower makeup water rate; and an eighth step of executing the processing in the first step to the seventh step while changing at least one of the cooling tower fan frequency and the chilled water flow rate, thereby determining the cooling tower fan frequency and the chilled water flow rate that minimize the sum of the estimated value of the cooling tower fan power cost, the estimated value of the cooling water pump power cost, the estimated value of the chiller compressor power cost, and the estimated value of the cooling tower makeup water rate cost.

[0013] Furthermore, in one configuration example of the cost optimization method of the present invention, the cooling tower process model models the relationship between the inlet condition of the cooling tower and the outlet temperature of the cooling water of the cooling tower, the chiller process model models the relationship between the inlet temperature of the cooling water of the chiller, the flow rate of the cooling water, the compressor power of the chiller and the outlet temperature of the cooling water of the chiller, the cooling tower power model models the relationship between the fan frequency of the cooling tower and the fan power of the cooling tower, the chilled water pump power model models the relationship between the flow rate of the cooling water and the power of the chilled water pump, and the chiller power model models the relationship between the outlet temperature of the cooling water of the chiller and the compressor power of the chiller. In addition, in one configuration example of the cost optimization method of the present invention, the inlet conditions of the cooling tower include the inlet air temperature of the cooling tower, the relative humidity of the inlet air, the inlet air flow rate calculated from the cooling tower fan rotation speed, the cooling water flow rate, and the cooling water inlet temperature. [Effects of the Invention]

[0014] According to the present invention, the power cost calculation unit estimates the cooling tower fan power cost, the cooling water pump power cost, and the chiller compressor power cost, and the makeup water amount calculation unit estimates the cooling tower makeup water amount cost, and a solution is found for the cooling tower fan frequency and cooling water flow rate that minimizes the sum of the estimated cooling tower fan power cost, the estimated cooling water pump power cost, the estimated chiller compressor power cost, and the estimated cooling tower makeup water amount, thereby minimizing the total cost of the cooling tower system, including the cooling tower makeup water amount cost. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a block diagram showing the configuration of a cost optimization system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram illustrating inputs and outputs of a cooling tower process model according to an embodiment of the present invention. [Figure 3]FIG. 3 is a diagram illustrating a cooling tower process model according to an embodiment of the present invention. [Figure 4] FIG. 4 is a diagram showing a model of the top part of a cooling tower in a cooling tower process model according to an embodiment of the present invention. [Figure 5] FIG. 5 is a diagram illustrating the operation of the cooling tower efficiency estimation unit according to the embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart illustrating the operation of the cooling tower efficiency estimation unit according to the embodiment of the present invention. [Figure 7] FIG. 7 is a conceptual diagram of the energy balance in the cooling water system in the chiller according to the embodiment of the present invention. [Figure 8] FIG. 8 is a diagram illustrating the operation of the bias correction heat quantity estimating unit according to the embodiment of the present invention. [Figure 9] FIG. 9 is a flowchart illustrating the operation of the bias correction heat quantity estimating unit according to the embodiment of the present invention. [Figure 10] FIG. 10 is a diagram illustrating the operation of the cost minimization calculation unit according to the embodiment of the present invention. [Figure 11] FIG. 11 is a flowchart illustrating the operation of the cost minimization calculation unit according to the embodiment of the present invention. [Figure 12] FIG. 12 is a diagram showing the configuration of a compression refrigerator. [Figure 13] FIG. 13 is a block diagram showing an example of the configuration of a computer that realizes a cost optimization system according to an embodiment of the present invention. [Figure 14] FIG. 14 is a block diagram showing the configuration of an air conditioning system. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a block diagram showing the configuration of a cost optimization system according to an embodiment of the present invention. Since the configuration of the air conditioning system is the same as that of a conventional system, the components of the air conditioning system will be described using the symbols in Fig. 14.

[0017] As shown in FIG. 1, the cost optimization system of this embodiment includes a cooling tower process model 1, a cooling tower power model 2, a cooling water pump power model 3, a chiller process model 4, a chiller power model 5, a cooling tower efficiency estimation unit 6, a bias correction heat quantity estimation unit 7, and a cost minimization calculation unit 8.

[0018] The cooling tower process model 1, which is a thermodynamic model of the cooling tower 102, is a mathematical model that models the relationship between the inlet conditions of the cooling tower 102 (the inlet temperature of the cooling water of the cooling tower 102, the flow rate of the cooling water, the inlet air temperature of the cooling tower 102, and the relative humidity of the inlet air) and the outlet temperature of the cooling water of the cooling tower 102.

[0019] The cooling tower power model 2 is a mathematical model that models the relationship between the fan frequency of the cooling tower 102 and the fan power of the cooling tower 102. The relationship between the fan frequency of the cooling tower 102 and the fan power of the cooling tower 102 can be obtained in advance by regression analysis from actual data, as in the conventional case.

[0020] The cooling water pump power model 3 is a mathematical model that models the relationship between the flow rate of cooling water and the power of the cooling water pump 103. The relationship between the flow rate of cooling water and the power of the cooling water pump 103 can be obtained in advance by regression analysis from actual data, as in the conventional case.

[0021] The chiller process model 4, which is an energy balance model of the cooling water of the chiller 100, is a mathematical model that models the relationship between the inlet temperature of the cooling water of the chiller 100, the flow rate of the cooling water, the compressor power of the chiller 100, and the outlet temperature of the cooling water of the chiller 100.

[0022] The chiller power model 5 is a mathematical model that models the relationship between the outlet temperature of the cooling water of the chiller 100 and the compressor power of the chiller 100. The relationship between the outlet temperature of the cooling water of the chiller 100 and the compressor power of the chiller 100 can be obtained in advance by regression analysis using actual data.

[0023] First, we will explain the cooling tower process model 1. The cooling tower process model 1 models the relationship between the inlet conditions (cooling water, cooling air) to the cooling tower 102 and the outlet temperature of the cooling water from the cooling tower 102, based on the vapor-liquid equilibrium relationship between the air and cooling water in the cooling tower 102, the material balance, and the heat balance equation (unit operation equation).

[0024] 2 is a diagram for explaining input and output of the cooling tower process model 1. In the example of FIG. 2, it is assumed that there are ten cooling towers 102-1 to 102-10. In FIG. 2, Tcw,out [degC] is the outlet temperature (estimated value) of the cooling water of the cooling tower 102, Tcw,in [degC] is the return temperature (actual value) of the cooling water from the chiller 100 to the cooling tower 102, Fcw,act [m 3 / h] is the return flow rate (actual value) of the cooling water from the chiller 100 to the cooling tower 102, T [degC] is the temperature (actual value) of the inlet air of the cooling tower 102, H [%] is the relative humidity (actual value) of the inlet air of the cooling tower 102, and Fair [m 3 / h] is the flow rate of air into the cooling tower 102. The following (I) to (V) are set as prerequisites.

[0025] (I) Each of the cooling towers 102-1 to 102-10 has the same cooling performance characteristics. (II) The cooling water flow rate is assumed to be distributed equally in accordance with the number of operating cooling towers 102-1 to 102-10 (actual value). (III) The moisture content of the inlet air of the cooling towers 102-1 to 102-10 is set based on the actual dry-bulb temperature and relative humidity data. (IV) The air flow rates Fair to the cooling towers 102-1 to 102-10 are calculated from the measured values ​​of the fan rotation speeds of the cooling towers 102-1 to 102-10. (V) Set the cooling tower efficiency (estimated value).

[0026] The cooling tower efficiency is an internal estimate based on the mass and heat balance calculations around the cooling tower, which are based on the most recent process data (every minute) around the cooling tower, and is updated every moment as explained below.

[0027] 3 is a diagram showing cooling tower process model 1. Here, cooling tower process model 1 is a model divided into four parts in the height direction, from Tray #1 at the top of the cooling tower through Tray #2 and Tray #3 in the middle to Tray #4 at the bottom of the cooling tower. High-temperature cooling water from chiller 100 is supplied to Tray #1 at the top of the cooling tower, and cold air is supplied to Tray #4 at the bottom of the cooling tower.

[0028] In Figure 3, x1(1)[m 3 / h] is the flow rate of air flowing from the outside into Tray #1 at the top of the cooling tower, and x1(2) [m 3 / h] is the flow rate of cooling water flowing from chiller 100 into the top tray #1 of the cooling tower, x1(3) [degC] is the inlet temperature of cooling water flowing from chiller 100 into the top tray #1 of the cooling tower, h1(1) [kcal / kg] is the enthalpy of air flowing from the outside into the top tray #1 of the cooling tower, and h1(2) [kcal / kg] is the enthalpy of cooling water flowing from chiller 100 into the top tray #1 of the cooling tower.

[0029] However, in this embodiment, it is assumed that no air flows from the outside into the uppermost tray #1 of the cooling tower, and the air flow rate x1(1) and air enthalpy h1(1) are set to zero.

[0030] x1(22)[m 3 / h] is the flow rate of the inlet air flowing into the bottom tray #4 of the cooling tower from the outside, x1(23) [m 3 / h] is the amount of moisture in the inlet air flowing from outside into Tray #4 at the bottom of the cooling tower, x1(24)[degC] is the dry-bulb temperature of the inlet air flowing from outside into Tray #4 at the bottom of the cooling tower, h1(22)[kcal / kg] is the enthalpy of the inlet air flowing from outside into Tray #4 at the bottom of the cooling tower, and h1(23)[kcal / kg] is the enthalpy of the moisture in the inlet air flowing from outside into Tray #4 at the bottom of the cooling tower.

[0031] x2(22)[m 3 / h] is the flow rate of air flowing out of Tray #4 at the bottom of the cooling tower, x2(23) [m 3 / h] is the outlet flow rate of the cooling water flowing out from the lowest tray #4 of the cooling tower to the chiller 100, x2(24) [degC] is the outlet temperature of the cooling water flowing out from the lowest tray #4 of the cooling tower to the chiller 100, h2(22) [kcal / kg] is the enthalpy of the air flowing out from the lowest tray #4 of the cooling tower, and h2(23) [kcal / kg] is the enthalpy of the cooling water flowing out from the lowest tray #4 of the cooling tower to the chiller 100.

[0032] Also, x2(1)[m 3 / h] is the flow rate of the outlet air flowing out of Tray #1 at the top of the cooling tower, x2(2) [m 3 / h] is the moisture content of the outlet air flowing out of the top Tray #1 of the cooling tower, x2(3) [degC] is the dry-bulb temperature of the outlet air flowing out of the top Tray #1 of the cooling tower, h2(1) [kcal / kg] is the enthalpy of the outlet air flowing out of the top Tray #1 of the cooling tower, and h2(2) [kcal / kg] is the enthalpy of the moisture content of the outlet air flowing out of the top Tray #1 of the cooling tower.

[0033] Focusing on the cooling tower top tray #1 of cooling tower process model 1, it can be described as shown in Figure 4. In Figure 4, x1(4) is the flow rate of air flowing from middle Tray #2 to the top Tray #1 of the cooling tower, x1(5) is the amount of moisture in the air flowing from middle Tray #2 to the top Tray #1 of the cooling tower, x1(6) is the dry-bulb temperature of the air flowing from middle Tray #2 to the top Tray #1 of the cooling tower, h1(4) is the enthalpy of the air flowing from middle Tray #2 to the top Tray #1 of the cooling tower, and h1(5) is the enthalpy of the moisture in the air flowing from middle Tray #2 to the top Tray #1 of the cooling tower.

[0034] x2(4) is the flow rate of air flowing out from the top tray #1 of the cooling tower to the middle tray #2, x2(5) is the flow rate of cooling water flowing out from the top tray #1 of the cooling tower to the middle tray #2, x2(6) is the temperature of cooling water flowing out from the top tray #1 of the cooling tower to the middle tray #2, h2(4) is the enthalpy of air flowing out from the top tray #1 of the cooling tower to the middle tray #2, and h2(5) is the enthalpy of cooling water flowing out from the top tray #1 of the cooling tower to the middle tray #2.

[0035] However, in this embodiment, x2(4) and h2(4) are set to 0 because air does not flow from the cooling tower's top tray #1 to the middle tray #2.

[0036] The following equation (1) is obtained from the water mass balance in the model of the top tray #1 of the cooling tower in Figure 4. x1(1)+x1(4)=x2(1)+x2(4) ···(1)

[0037] By transforming equation (1), the following equation is obtained. f(1)={x1(1)+x1(4)}-x2(1)+x2(4)} ···(2)

[0038] The following equation (3) is obtained from the mass balance of air in the model of the top tray #1 of the cooling tower in Figure 4. x1(2)+x1(5)=x2(2)+x2(5) ···(3)

[0039] By transforming equation (3), the following equation is obtained. f(2)={x1(2)+x1(5)}-{x2(2)+x2(4)} ···(4)

[0040] The following equation (5) is obtained from the heat balance in the model of the top tray #1 of the cooling tower in Figure 4. h1(1)×x1(1)+h1(2)×x1(2)+h1(4)×x1(4)+h1(5)×x1(5) =h2(1)×x2(1)+h2(2)×x2(2)+h2(4)×x2(4)+h2(5)×x2(5) ···(5)

[0041] By transforming equation (5), the following equation is obtained. f(3)={h1(1)×x1(1)+h1(2)×x1(2)+h1(4)×x1(4)+h1(5)×x1(5)}-{h2(1)×x2(1)+h2(2)×x2(2)+h2(4)×x2(4)+h2(5)×x2(5)}...(6)

[0042] The following equation (7) is obtained from the vapor-liquid equilibrium relationship between air and cooling water in the model of the top tray #1 of the cooling tower in Figure 4. a(1)×x2(1)=a(2)×x2(2)×efficiency ·(7)

[0043] a(1) and a(2) in equation (7) are expressed as equations (8) and (9). a(1)=(1-yH2O(1)) / MWH2O ···(8) a(2)=yH2O(1) / Mwair (9)

[0044] Where MWH2O is the known molecular weight of water and Mwair is the known molecular weight of air. The mole fraction of water in the gas phase, yH2O, is given by Raoult's law as follows: yH2O[mole-fraction]=H2Ovaporpressure(T) / totalpressure×xH2O[mole-fraction] ···(10)

[0045] H2Ovaporpressure(T) is the known water vapor pressure at equilibrium temperature T, totalpressure is the actual total pressure, and xH2O is the known mole fraction of water in the liquid phase. Efficiency in equation (7) is the efficiency of the cooling tower. Rearranging equation (7) gives the following equation: f(4)={a(1)+x2(1)}-{a(2)+x2(2)} ···(11)

[0046] f(1) to f(4) are evaluation functions. For each tray from the top tray #1 of the cooling tower to the bottom tray #4 of the cooling tower, equations similar to equations (2), (4), (6), and (11) can be set.

[0047] Then, by calculating the evaluation functions f(1) to f(4) for each of the stages from the uppermost Tray #1 to the lowermost Tray #4 of the cooling tower so that they are 0, the flow rate x2 (23) of the cooling water flowing out from the lowermost Tray #4 of the cooling tower to the chiller 100, the outlet temperature x2 (24) of the cooling water flowing out from the lowermost Tray #4 of the cooling tower to the chiller 100, and the enthalpy h2 ( 23), the flow rate x2(1) of the outlet air flowing out from the top tray #1 of the cooling tower, the amount of moisture in the air flowing out from the top tray #1 of the cooling tower x2(2), the dry-bulb temperature x2(3) of the outlet air flowing out from the top tray #1 of the cooling tower, the enthalpy h2(1) of the outlet air flowing out from the top tray #1 of the cooling tower, and the enthalpy h2(2) of the moisture in the outlet air flowing out from the top tray #1 of the cooling tower can be calculated.

[0048] The actual calculation is performed using a determinant that combines the equations of the evaluation functions for each of the uppermost tray #1 of the cooling tower to the lowermost tray #4 of the cooling tower.

[0049] Furthermore, the outlet temperature x2 (24) of the cooling water flowing out from the bottommost tray #4 of the cooling tower to the chiller 100 can be calculated based on the flow rate x2 (23) of the cooling water and the enthalpy h2 (23) of the cooling water. The dry-bulb temperature x2 (3) of the outlet air flowing out from the topmost tray #1 of the cooling tower can be calculated based on the flow rate x2 (1) of the outlet air and the enthalpy h2 (1). The relative humidity of the outlet air can be calculated based on the moisture content x2 (2) in the outlet air, the dry-bulb temperature x2 (3) of the outlet air, and the known amount of saturated water vapor at the dry-bulb temperature x2 (3).

[0050] In order to find a solution to the cooling tower optimization problem, it is necessary to estimate the efficiency of the cooling tower 102 in advance using the cooling tower efficiency estimation unit 6 and set it in the cooling tower process model 1.

[0051] The cooling tower efficiency estimation unit 6 calculates the amount of evaporation of cooling water and the relative humidity of the outlet air of the cooling tower 102 based on the actual values ​​of the inlet conditions (air, cooling water) of the cooling tower 102 and the actual value of the outlet temperature of the cooling water of the cooling tower 102. Theoretically, the relative humidity of the outlet air of the cooling tower 102 does not exceed 100%. As the maximum efficiency (100%), the relative humidity of the outlet air of the cooling tower 102 in current operation is set as the efficiency of the cooling tower 102 in current operation.

[0052] FIG. 5 is a diagram for explaining the operation of the cooling tower efficiency estimation unit 6, and FIG. 6 is a flowchart for explaining the operation of the cooling tower efficiency estimation unit 6. The cooling tower efficiency estimation unit 6 estimates the flow rate x1(1)=0 of air flowing into the cooling tower's topmost Tray #1 of the cooling tower 102, the actual value of the flow rate x1(2) of cooling water flowing into the cooling tower 102 from the chiller 100 (Fcw,act above), the actual value of the inlet temperature x1(3) of cooling water flowing into the cooling tower 102 from the chiller 100 (Tcw,in above), the enthalpy h1(1)=0 of air flowing into the cooling tower's topmost Tray #1, the actual value of the enthalpy h1(2) of cooling water flowing into the cooling tower 102 from the chiller 100, the value of the flow rate x1(22) of inlet air flowing into the cooling tower's bottommost Tray #4 of the cooling tower 102 (Fair above), and the actual value of the amount of moisture x1(23) in the inlet air flowing into the cooling tower's bottommost Tray #4. The cooling tower process model 1 then inputs the actual dry-bulb temperature x1 (24) of the inlet air flowing into the cooling tower's lowest tray #4, the actual enthalpy h1 (22) of the inlet air flowing into the cooling tower's lowest tray #4, the actual enthalpy h1 (23) of the moisture in the inlet air flowing into the cooling tower's lowest tray #4, and the actual outlet temperature x2 (24) of the cooling water flowing from the cooling tower's lowest tray #4 to the chiller 100 (the actual value of Tcw,out described above) into the cooling tower process model 1 (FIG. 6, step S100). The cooling tower process model 1 then obtains an estimate of the moisture content x2 (2) in the outlet air flowing out of the cooling tower's highest tray #1 of the cooling tower 102 and an estimate of the outlet air dry-bulb temperature x2 (3) from the cooling tower process model 1 (FIG. 6, step S101).

[0053] The cooling tower efficiency estimation unit 6 can calculate the actual value of the inlet air flow rate x1 (22) of the cooling tower 102 from the measured value of the fan rotation speed (fan frequency FRfan) of the cooling tower 102. The cooling tower efficiency estimation unit 6 can calculate the actual value of the enthalpy h1(2) of the cooling water flowing from the chiller 100 into the cooling tower 102 based on the actual value of the cooling water flow rate x1(2) and the actual value of the cooling water temperature x1(3).

[0054] The cooling tower efficiency estimation unit 6 can calculate the actual value of the moisture content x1 (23) of the inlet air flowing into the lowest tray #4 of the cooling tower based on the design value of the inlet air flow rate x1 (22), the actual value of the inlet air dry-bulb temperature x1 (24), the actual value of the inlet air relative humidity H [%], and the known saturated water vapor content at the dry-bulb temperature x1 (24).

[0055] The cooling tower efficiency estimation unit 6 can calculate the actual value of the inlet air enthalpy h1 (22) based on the design value of the inlet air flow rate x1 (22) and the actual value of the inlet air dry-bulb temperature x1 (24).

[0056] The cooling tower efficiency estimation unit 6 can calculate the actual value of the enthalpy h1 (23) of moisture in the inlet air based on the actual value of the moisture content x1 (23) in the inlet air and the actual value of the dry-bulb temperature x1 (24) of the inlet air.

[0057] In order to remove noise, it is desirable that the cooling tower efficiency estimation unit 6 performs a primary filter process on the actual values ​​of the inlet conditions of the cooling tower 102 (x1(1), x1(2), x1(3), h1(1), h1(2), x1(22), x1(23), x1(24), h1(22), h1(23), x2(24)) before inputting them into the cooling tower process model 1.

[0058] The cooling tower efficiency estimation unit 6 calculates an estimate of the relative humidity of the outlet air of the cooling tower 102 based on the estimate of the moisture content x2(2) in the outlet air obtained in step S101, the estimate of the dry-bulb temperature x2(3) of the outlet air, and the known saturated water vapor content at the dry-bulb temperature x2(3) (step S102 in FIG. 6).

[0059] The cooling tower efficiency estimation unit 6 performs the processing of steps S100 to S102 for each data pair of the actual values ​​of the inlet conditions of the cooling tower 102 (x1(1), x1(2), x1(3), h1(1), h1(2), x1(22), x1(23), x1(24), h1(22), h1(23), x2(24)) and the actual value of the outlet temperature x2(24) of the cooling water at the same date and time as these inlet conditions, to obtain an estimate of the relative humidity of the outlet air of the cooling tower 102.

[0060] Then, the cooling tower efficiency estimation unit 6 performs exponential smoothing filtering on the estimated value of the outlet air relative humidity obtained for each set of input data, and sets the result as the efficiency of the cooling tower 102 (step S103 in FIG. 6). The cooling tower efficiency estimation unit 6 sets the efficiency value calculated in step S103 in the cooling tower process model 1 (step S104 in FIG. 6).

[0061] The cooling tower efficiency estimation unit 6 may be configured to perform the above-described processing of steps S100 to S104 at regular time intervals (for example, every minute). Note that, in the initial calculation of step S101, the efficiency of the cooling tower 102 is not set, so a predetermined value may be set as the efficiency in the cooling tower process model 1, and the calculation of step S101 may be performed.

[0062] Next, the chiller process model 4 will be described. Fig. 7 shows the chiller process model 4, which models the energy balance in the cooling water system in the chiller 100. Tcws [degC] is the inlet temperature of the cooling water flowing into the chiller 100 (the outlet temperature of the cooling water from the cooling tower 102, Tcw,out), Fcw [m 3 / h] is the flow rate of cooling water flowing into the chiller 100 (the outlet flow rate of cooling water from the cooling tower 102), Tcwr [degC] is the outlet temperature of cooling water flowing out of the chiller 100 (the inlet temperature Tcw,in of cooling water from the cooling tower 102), Qcomp [kcal / h] is the power (amount of energy transferred) of the compressor of the chiller 100, and Qchw [kcal / h] is the amount of heat (amount of energy transferred) used to produce chilled water. The amount of heat used to produce chilled water, Qchw, can be expressed by the following heat balance equation. Qchw=Fcw×(Tchwr-Tchws) ···(12) Here, Tchws [degC] is the outlet temperature of the chilled water flowing out from the evaporator of the chiller 100, and Tchwr [degC] is the inlet temperature of the chilled water flowing into the evaporator.

[0063] Theoretically, the heat discharged to the cooling water in the chiller 100 is based on the sum of the compressor power Qcomp and the heat amount of chilled water production Qchw, but in reality, other miscellaneous energy amounts that cannot be measured are also discharged to the cooling water system of the chiller 100. Therefore, in this embodiment, the correction value for the heat amount discharged to the cooling water system of the chiller 100 is set as the bias correction heat amount Qbias [kcal / h]. In this case, the following heat balance equation is established. Qcw=Qcomp+Qchw+Qbias (13) Qcw=Cp×Fcw×(Tcwr-Tcws) ···(14)

[0064] Cp is the known specific heat of the cooling water. All values ​​except for the chiller waste heat quantity Qcw and the bias corrected heat quantity Qbias are known (measured values), so the bias corrected heat quantity Qbias can be calculated from equations (13) and (14) using the following equation (15). Qbias=Cp×Fcw×(Tcwr-Tcws)-(Qcomp+Qchw) ···(15)

[0065] In order to find a solution to the cooling tower optimization problem, the bias-corrected heat quantity Qbias must be estimated in advance by the bias-corrected heat quantity estimator 7 and set in the chiller process model 4. Fig. 8 is a diagram for explaining the operation of the bias-corrected heat quantity estimator 7, and Fig. 9 is a flowchart for explaining the operation of the bias-corrected heat quantity estimator 7.

[0066] The bias-corrected heat quantity estimation unit 7 calculates the bias-corrected heat quantity Qbias using equation (15) based on the actual value of the inlet temperature Tcws of the cooling water flowing into the chiller 100, the actual value of the flow rate Fcw of the cooling water flowing into the chiller 100, the actual value of the outlet temperature Tcwr of the cooling water flowing out from the chiller 100, the actual value of the power Qcomp of the compressor of the chiller 100, the known chilled water production heat quantity Qchw of the chiller 100, and the known specific heat Cp of the cooling water (step S200 in FIG. 9).

[0067] In order to remove noise, it is desirable that the bias correction heat quantity estimation unit 7 performs primary filtering on the actual values ​​of the cooling water inlet temperature Tcws, the actual values ​​of the cooling water flow rate Fcw, the actual values ​​of the cooling water outlet temperature Tcwr, and the actual value of the compressor power quantity Qcomp before calculating the bias correction heat quantity Qbias.

[0068] The bias-corrected heat quantity estimation unit 7 groups the actual values ​​of the coolant inlet temperature Tcws, the actual values ​​of the coolant flow rate Fcw, the actual values ​​of the coolant outlet temperature Tcwr, and the actual value of the compressor power Qcomp for each data set with the same date and time, and performs the process of step S200 for each data set to obtain an estimate of the bias-corrected heat quantity Qbias.The bias-corrected heat quantity estimation unit 7 then performs primary filtering on the bias-corrected heat quantity Qbias obtained for each input data set, and sets the result as the final bias-corrected heat quantity Qbias.

[0069] The bias-corrected heat quantity estimation unit 7 sets the bias-corrected heat quantity Qbias obtained in step S200 in the chiller process model 4 (step S201 in FIG. 9). The bias-corrected heat quantity estimation unit 7 only needs to perform the above-described processes of steps S200 and S201 once before finding a solution to the cooling tower optimization problem.

[0070] Next, in the chiller process model 4, the bias-corrected heat quantity Qbias is assumed to be a process value that is not affected by changes in the cold heat production load, and the outlet temperature Tcwr of the chiller 100 is estimated when the inlet temperature Tcws of the chiller 100 changes due to at least one of a change in the fan frequency of the cooling tower 102 and a change in the number of cooling towers 102 during calculation of the solution to the cooling tower optimization problem, or when the flow rate Fcw of the chiller 100 changes.

[0071] When the bias correction heat quantity Qbias is set by the bias correction heat quantity estimating unit 7 and the heat quantity Qchw of chilled water production by the chiller 100 is known, the following equation is established. Qcw * =Qcomp * +Qchw+Qbias ···(16) Qcw * =Cp×Fcw * ×(Tcwr * -Tcws * ) ···(17)

[0072] The variables marked with * are the state values ​​after the process state is changed. From equations (16) and (17), the outlet temperature Tcwr of the cooling water of the refrigerator 100 is * can be calculated by the following formula: Tcwr * =Tcws * +(Qcomp * +Qchw+Qbias) / (Cp×Fcw * ) ···(18)

[0073] Next, a description will be given of the cost minimization calculation unit 8. Fig. 10 is a diagram for explaining the operation of the cost minimization calculation unit 8, and Fig. 11 is a flowchart for explaining the operation of the cost minimization calculation unit 8. The cost minimization calculation unit 8 includes a cooling tower outlet temperature estimation unit 80, a chiller outlet temperature estimation unit 81, a cooling tower power estimation unit 82, a cooling water pump power estimation unit 83, a chiller power estimation unit 84, a power cost calculation unit 85, a make-up water amount cost calculation unit 86, and a solution-finding unit 87.

[0074] The cooling tower outlet temperature estimation unit 80 of the cost minimization calculation unit 8 calculates the flow rate of air flowing into the cooling tower's top Tray #1 of the cooling tower 102, x1(1)=0, the inlet flow rate Fcw of cooling water flowing into the cooling tower 102 (x1(2) above), the inlet temperature Tcw,in of cooling water flowing into the cooling tower 102 (x1(3) above), the enthalpy h1(1)=0 of air flowing into the cooling tower's top Tray #1, the enthalpy h1(2) of cooling water flowing into the cooling tower 102, the inlet air flow rate Fair of cooling tower's bottom Tray #4 (x1(22) above), and the inlet temperature Tcw,in of cooling water flowing into the cooling tower 102 (x1(3) above). The moisture content x1 (23) in the inlet air flowing into Tray #4, the current value of the inlet air dry-bulb temperature T (x1 (24) above) measured by a temperature sensor not shown, the enthalpy h1 (22) of the inlet air flowing into Tray #4 at the bottom of the cooling tower, and the moisture enthalpy h1 (23) in the inlet air flowing into Tray #4 at the bottom of the cooling tower are input into Cooling Tower Process Model 1, and an estimate of the cooling water outlet temperature Tcw,out (x2 (24) above) of the cooling tower 102 and an estimate of the cooling water outlet flow rate x2 (23) are obtained from Cooling Tower Process Model 1 (FIG. 11, Step S300).

[0075] In the cooling tower optimization problem, the estimated value of the fan power Qfan of the cooling tower 102, the estimated value of the power Qpomp of the cooling water pump 103, and the estimated value of the compressor power Qcomp of the chiller 100 are repeatedly calculated while changing the fan frequency FRfan of the cooling tower 102, the cooling water flow rate Fcw, and the number of cooling towers 102 Nct.

[0076] Therefore, the estimated value of the outlet temperature Tcwr of the cooling water of the chiller 100 calculated immediately before by the chiller outlet temperature estimation unit 81 during calculation of the solution to the optimization problem can be used as the inlet temperature Tcw,in of the cooling water flowing into the cooling tower 102. Furthermore, in the initial calculation, a predetermined value can be used as the value of the inlet temperature Tcw,in.

[0077] The cooling tower outlet temperature estimation unit 80 can calculate the flow rate Fair of the inlet air of the cooling tower 102 from the fan rotation speed (fan frequency FRfan) of the cooling tower 102. The cooling tower outlet temperature estimation unit 80 can calculate the enthalpy h1(2) of the cooling water flowing into the cooling tower 102 based on the flow rate Fcw of the cooling water and the inlet temperature Tcw,in of the cooling water.

[0078] In addition, the cooling tower outlet temperature estimation unit 80 can calculate the moisture content x1 (23) in the inlet air flowing into the cooling tower's lowest tray #4 based on the inlet air flow rate Fair, the current value of the inlet air dry-bulb temperature T, the current value of the inlet air relative humidity H [%] measured by a humidity sensor (not shown), and the known saturated water vapor content at the dry-bulb temperature T.

[0079] Furthermore, the cooling tower outlet temperature estimation unit 80 can calculate the enthalpy h1 (22) of the inlet air based on the flow rate Fair of the inlet air and the current value of the dry-bulb temperature T of the inlet air.

[0080] Furthermore, the cooling tower outlet temperature estimation unit 80 can calculate the enthalpy h1 (23) of moisture in the inlet air based on the amount of moisture x1 (23) in the inlet air and the current value of the dry-bulb temperature T of the inlet air.

[0081] The outlet temperature Tcw,out of the cooling water from the cooling tower 102 varies depending on the fan rotation speed, dry-bulb temperature T, inlet air relative humidity H [%], inlet temperature Tcw,in of the cooling water, and flow rate Fcw of the cooling water. Therefore, it was difficult to construct a regression model to estimate the outlet temperature Tcw,out of the cooling water from these actual data.

[0082] To solve this problem, in this embodiment, a thermodynamic model of the cooling tower 102 (cooling tower process model 1) is constructed, and the cooling tower efficiency in the cooling tower process model 1 is corrected as needed using actual data, and then operated. This makes it possible to accurately calculate the amount of change in the outlet temperature Tcw,out of the cooling water due to changes in the fan rotation speed of the cooling tower 102.

[0083] Next, the chiller outlet temperature estimation unit 81 of the cost minimization calculation unit 8 inputs the inlet temperature Tcws of the cooling water flowing into the chiller 100, the flow rate Fcw of the cooling water, the compressor power Qcomp of the chiller 100, the known heat quantity of chilled water production Qchw of the chiller 100, and the known specific heat Cp of the cooling water into the chiller process model 4 (equation (18)), and obtains an estimate of the outlet temperature Tcwr of the cooling water of the chiller 100 from the chiller process model 4 (step S301 in FIG. 11).

[0084] The cooling water inlet temperature Tcws may be the cooling water outlet temperature Tcw,out of the cooling tower 102 calculated in step S300. The compressor power Qcomp of the chiller 100 may be the estimated value of the compressor power Qcomp calculated immediately before by the chiller power estimation unit 84 during calculation of the solution to the optimization problem. In addition, in the initial calculation, a predetermined value may be used as the value of the compressor power Qcomp.

[0085] Next, the cooling tower power estimation unit 82 of the cost minimization calculation unit 8 inputs the fan frequency FRfan of the cooling tower 102 into the cooling tower power model 2, and obtains an estimated value of the fan power Qfan of the cooling tower 102 from the cooling tower power model 2 (step S302 in FIG. 11).

[0086] The cooling water pump power estimation unit 83 of the cost minimization calculation unit 8 inputs the cooling water flow rate Fcw to the cooling water pump power model 3, and obtains an estimated value of the power Qpomp of the cooling water pump 103 from the cooling water pump power model 3 (step S303 in FIG. 11).

[0087] The chiller power estimation unit 84 of the cost minimization calculation unit 8 inputs the outlet temperature Tcwr of the cooling water of the chiller 100 into the chiller power model 5, and obtains an estimated value of the compressor power Qcomp of the chiller 100 from the chiller power model 5 (step S304 in FIG. 11). The value calculated in step S301 may be used as the outlet temperature Tcwr of the cooling water.

[0088] 12 is a diagram showing the configuration of a compression-type refrigerator 100. The refrigerator 100 includes an evaporator 1000 that evaporates a refrigerant, a compressor 1001 that compresses the low-temperature, low-pressure refrigerant gas vaporized in the evaporator 1000, a condenser 1002 that cools and condenses the high-temperature, high-pressure refrigerant gas, and a pressure reducing valve 1003 that reduces the pressure of the liquefied refrigerant in the condenser 1002 to a state where it can easily evaporate.

[0089] Conventionally, the calculation of compressor power Qcomp, which is performed in power optimization calculations, uses a correlation curve between the chiller load and compressor power Qcomp for each cooling water inlet temperature to the condenser 1002. However, during actual operation of the cooling tower 102, not only the cooling water inlet temperature to the condenser 1002 but also the cooling water flow rate changes, affecting the compressor power Qcomp. The refrigerant state (refrigerant temperature, pressure) in the condenser 1002 is highly dependent on the amount of waste heat (mainly heat generated by the operation of the evaporator 1000 = amount of heat required to produce chilled water) and the cooling water outlet temperature of the condenser 1002. For example, even if the cooling water inlet temperature to the condenser 1002 is lowered, if the cooling water flow rate of the condenser 1002 is reduced, the cooling water outlet temperature will rise, causing the refrigerant pressure in the condenser 1002 to rise and the compressor power Qcomp to increase.

[0090] In the conventional method of calculating the compressor power Qcomp from actual data on the inlet temperature of the cooling water to the chiller 100, the cooling water flow rate, and the chiller's production heat quantity, it was difficult to build a practical regression model because there were many measurement points and measurement errors. Therefore, in this embodiment, in order to solve the conventional problems, regression is performed on actual data of the chiller load and actual data of the compressor power Qcomp for each chiller outlet temperature of the chiller 100, which is uniquely determined by the chiller flow rate, the inlet temperature of the chiller to the chiller 100, and the amount of exhaust heat (amount of heat produced by the chiller + compressor power Qcomp). This makes it possible to construct a practical regression model (chiller power model 5) for estimating the compressor power Qcomp.

[0091] In terms of the process, there is a very high correlation between the cooling water outlet temperature → condenser refrigerant temperature → condenser refrigerant pressure (= compressor discharge pressure). Therefore, it is possible to construct a chiller power model 5 based on the high correlation between the cooling water outlet temperature of the chiller 100 and the compressor power Qcomp.

[0092] The power cost calculation unit 85 of the cost minimization calculation unit 8 calculates an estimated value of the fan power cost of the cooling tower 102, an estimated value of the power cost of the cooling water pump 103, and an estimated value of the compressor power of the chiller 100 based on the calculation results of the cooling tower power estimation unit 82, the cooling water pump power estimation unit 83, and the chiller power estimation unit 84 (step S305 in Figure 11).

[0093] Specifically, the power cost calculation unit 85 converts the estimated value of the fan power Qfan [kcal / h] of the cooling tower 102 calculated by the cooling tower power estimation unit 82 into the fan power consumption Wfan [kW], and multiplies the fan power consumption Wfan [kW] by the unit price of electricity [yen / kWh] to calculate an estimated value of the fan power cost Cfan [yen] of the cooling tower 102 per hour.

[0094] In addition, the power cost calculation unit 85 converts the estimated value of the power Qpomp [kcal / h] of the cooling water pump 103 calculated by the cooling water pump power estimation unit 83 into the pump power consumption Wpomp [kW], and multiplies the pump power consumption Wpomp [kW] by the unit price of electricity [yen / kWh] to calculate an estimated value of the power cost Cpomp [yen] of the cooling water pump 103 per hour.

[0095] Furthermore, the power cost calculation unit 85 converts the estimated value of the compressor power Qcomp [kcal / h] of the chiller 100 calculated by the chiller power estimation unit 84 into compressor power consumption Wcomp [kW], and multiplies the compressor power consumption Wcomp [kW] by the electricity unit price [yen / kWh] to calculate an estimated value of the compressor power cost Ccomp [yen] of the chiller 100 per hour.

[0096] As described above, the outlet flow rate of the cooling water flowing out of the cooling tower 102 is x2(23) [m3 / h] can be estimated using the cooling tower process model 1. The makeup water amount cost calculation unit 86 of the cost minimization calculation unit 8 calculates the inlet flow rate Fcw [m 3 / h] (x1(2) input to the cooling tower process model 1 in step S300), and the cooling water outlet flow rate x2(23) [m 3 / h] is calculated as an estimate of the makeup water amount Fsupply of the cooling tower 102 (step S306 in FIG. 11). Fsupply=Fcw-x2(23) ···(19)

[0097] Then, the makeup water amount cost calculation unit 86 multiplies the calculated makeup water amount Fsupply by the water amount unit price [yen / m 3 ] to calculate an estimated value of the cost Csupply [yen] of makeup water amount for the cooling tower 102 per hour (Step S307 in FIG. 11). The cooling tower 102 is automatically replenished with water by an automatic water supply device (not shown) in the amount of Fsupply.

[0098] The solution-finding unit 87 of the cost minimization calculation unit 8 repeats the calculations of steps S300 to S307 while varying at least one of the fan frequency FRfan of the cooling tower 102, the cooling water flow rate Fcw, and the number of cooling towers 102 Nct (step S308 in Figure 11), and obtains the fan frequency FRfan of the cooling tower 102, the cooling water flow rate Fcw, and the number of cooling towers 102 Nct that minimize the objective function (the sum of the estimated value of the fan power cost Cfan of the cooling tower 102, the estimated value of the power cost Cpomp of the cooling water pump 103, the estimated value of the compressor power cost Ccomp of the chiller 100, and the estimated value of the makeup water cost Csupply of the cooling tower 102) (steps S309 and S310 in Figure 11).

[0099] In this way, optimal solutions for the cooling tower 102 fan frequency FRfan, the cooling water flow rate Fcw, and the number of cooling towers 102 Nct can be obtained. In this embodiment, the power cost calculation unit 85 estimates the cooling tower 102 fan power cost Cfan, the cooling water pump 103 power cost Cpomp, and the chiller 100 compressor power cost Ccomp, and the makeup water cost calculation unit 86 estimates the cooling tower 102 makeup water cost Csupply. A solution is found that minimizes the sum of the estimated cooling tower 102 fan power cost Cfan, the estimated cooling water pump 103 power cost Cpomp, the estimated chiller 100 compressor power cost Ccomp, and the cooling tower 102 makeup water cost Csupply. This makes it possible to minimize the total cost of the cooling tower system, including the makeup water cost of the cooling tower 102. The amount of cooling water evaporated in the cooling tower 102 depends on the cooling water flow rate, cooling water temperature, air volume, its dry-bulb temperature, and humidity. In this embodiment, the amount of cooling water evaporated in the cooling tower 102 can be calculated using a thermodynamic model.

[0100] The cost optimization system described in this embodiment can be realized by a computer equipped with a CPU (Central Processing Unit), a storage device, and an interface, and a program that controls these hardware resources. An example of the configuration of this computer is shown in Figure 13.

[0101] The computer includes a CPU 200, a storage device 201, and an interface device (I / F) 202. A temperature sensor, a humidity sensor, a cooling tower cooling water inlet temperature sensor, a cooling tower cooling water outlet temperature sensor, a chiller cooling water inlet temperature sensor, a chiller cooling water outlet temperature sensor, a flow rate sensor, etc. are connected to the I / F 202. In such a computer, a program for realizing the cost optimization method of the present invention is stored in the storage device 201. The CPU 200 executes the processing described in this embodiment in accordance with the program stored in the storage device 201. [Industrial Applicability]

[0102] The present invention can be applied to techniques for optimizing the cost of air conditioning systems. [Explanation of symbols]

[0103] 1...Cooling tower process model, 2...Cooling tower power model, 3...Cooling water pump power model, 4...Children's process model, 5...Children's machine power model, 6...Cooling tower efficiency estimation unit, 7...Bias correction heat quantity estimation unit, 8...Cost minimization calculation unit, 80...Cooling tower outlet temperature estimation unit, 81...Children's machine outlet temperature estimation unit, 82...Cooling tower power estimation unit, 83...Cooling water pump power estimation unit, 84...Children's machine power estimation unit, 85...Power cost calculation unit, 86...Make-up water amount cost calculation unit, 87...Solution finding unit, 100...Children's machine, 101...Air conditioner, 102...Cooling tower, 103...Children's water pump

Claims

1. a cooling tower outlet temperature estimator configured to input cooling tower inlet conditions into a cooling tower process model to obtain an estimate of a cooling water outlet temperature of the cooling tower and an estimate of a cooling water outlet flow rate of the cooling tower; a chiller outlet temperature estimation unit configured to input a chiller cooling water inlet temperature, a flow rate of the chiller, and compressor power of the chiller into a chiller process model to obtain an estimated value of a chiller cooling water outlet temperature of the chiller; a cooling tower power estimator configured to input the cooling tower fan frequency into a cooling tower power model to obtain an estimate of the cooling tower fan power; a cooling water pump power estimation unit configured to input the cooling water flow rate into a cooling water pump power model to obtain an estimated value of the power of the cooling water pump; a chiller power estimation unit configured to input the outlet temperature of the cooling water of the chiller into a chiller power model to obtain an estimated value of compressor power of the chiller; a power cost calculation unit configured to calculate an estimated value of a fan power cost of the cooling tower, an estimated value of a power cost of the cooling water pump, and an estimated value of a compressor power of the chiller based on the calculation results of the cooling tower power estimation unit, the cooling water pump power estimation unit, and the chiller power estimation unit; a make-up water amount cost calculation unit configured to estimate a make-up water amount cost for the cooling tower by using a difference between an inlet flow rate of the cooling water of the cooling tower and an estimated value of the outlet flow rate of the cooling water obtained by the cooling tower outlet temperature estimation unit as an estimated value of a make-up water amount for the cooling tower; and a solution finding unit configured to determine the cooling tower fan frequency and the cooling water flow rate that minimize the sum of an estimated value of the cooling tower fan power cost, an estimated value of the cooling water pump power cost, an estimated value of the chiller compressor power cost, and an estimated value of the cooling tower makeup water cost by having the cooling tower outlet temperature estimating unit, the chiller outlet temperature estimating unit, the cooling tower power estimating unit, the cooling water pump power estimating unit, the chiller power estimating unit, the power cost calculation unit, and the make-up water cost calculation unit execute processing while changing at least one of the cooling tower fan frequency and the coolant flow rate.

2. 2. The cost optimization system of claim 1, a cooling tower process model that models the relationship between the cooling tower inlet conditions and the cooling water outlet temperature of the cooling tower; a cooling tower power model that models the relationship between the cooling tower fan frequency and the cooling tower fan power; a cooling water pump power model that models the relationship between the flow rate of the cooling water and the power of the cooling water pump; a chiller process model that models the relationship between an inlet temperature of the chiller cooling water, a flow rate of the chiller cooling water, compressor power of the chiller cooling water, and an outlet temperature of the chiller cooling water; The cost optimization system further comprises a chiller power model that models the relationship between the outlet temperature of the cooling water of the chiller and the compressor power of the chiller.

3. 3. The cost optimization system according to claim 1, The cooling tower inlet conditions include the inlet air temperature of the cooling tower, the relative humidity of the inlet air, the inlet air flow rate calculated from the cooling tower fan rotation speed, the cooling water flow rate, and the cooling water inlet temperature.

4. a first step of inputting cooling tower inlet conditions into a cooling tower process model to obtain an estimate of a cooling water outlet temperature of the cooling tower and an estimate of a cooling water outlet flow rate of the cooling tower; a second step of inputting the inlet temperature of the chiller cooling water, the flow rate of the chiller cooling water, and the compressor power of the chiller into a chiller process model to obtain an estimate of the outlet temperature of the chiller cooling water; a third step of inputting the cooling tower fan frequency into a cooling tower power model to obtain an estimate of the cooling tower fan power; a fourth step of inputting the cooling water flow rate into a cooling water pump power model to obtain an estimate of cooling water pump power; a fifth step of inputting the outlet temperature of the cooling water of the chiller into a chiller power model to obtain an estimated value of compressor power of the chiller; a sixth step of calculating an estimated value of the cooling tower fan power cost, an estimated value of the cooling water pump power cost, and an estimated value of the chiller compressor power cost based on the calculation results of the third step, the fourth step, and the fifth step; a seventh step of setting the difference between the inlet flow rate of the cooling water of the cooling tower and the estimated outlet flow rate of the cooling water obtained in the first step as an estimated make-up water amount of the cooling tower, and calculating an estimated make-up water amount cost of the cooling tower; an eighth step of determining the cooling tower fan frequency and the cooling water flow rate that minimize the sum of an estimated cooling tower fan power cost, an estimated cooling water pump power cost, an estimated chiller compressor power cost, and an estimated cooling tower makeup water cost by executing the processing of the first step to the seventh step while changing at least one of the cooling tower fan frequency and the cooling water flow rate.

5. 5. The cost optimization method of claim 4, the cooling tower process model is a model of a relationship between an inlet condition of the cooling tower and an outlet temperature of the cooling water of the cooling tower; the chiller process model is a model of a relationship between an inlet temperature of cooling water of the chiller, a flow rate of the cooling water, a compressor power of the chiller, and an outlet temperature of the cooling water of the chiller, the cooling tower power model is a model of the relationship between the fan frequency of the cooling tower and the fan power of the cooling tower; the cooling water pump power model is a model of the relationship between the flow rate of the cooling water and the power of the cooling water pump, The cost optimization method, wherein the chiller power model is a model of the relationship between the outlet temperature of the cooling water of the chiller and the compressor power of the chiller.

6. 6. The cost optimization method according to claim 4 or 5, The cooling tower inlet conditions include the inlet air temperature of the cooling tower, the relative humidity of the inlet air, the inlet air flow rate calculated from the cooling tower fan rotation speed, the cooling water flow rate, and the cooling water inlet temperature.

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