Energy-saving control method for indirect evaporative cooling tower based on load change and cooling tower

Abstract

The application discloses an energy-saving control method of an indirect evaporative cooling tower based on load change and the cooling tower. The control method comprises the following steps: establishing a heat dissipation model of the indirect evaporative cooling tower and an energy consumption model of the indirect evaporative cooling tower; setting a constraint condition; adjusting the cooling water temperature, the flow of the primary water and the flow of the secondary water according to the set constraint condition to seek the optimal state; and achieving the optimal real-time energy consumption state of the indirect evaporative cooling tower according to the optimal cooling water temperature, the flow of the primary water and the flow of the secondary water. The application aims to control the flow of the primary water and the flow of the secondary water of the indirect evaporative cooling tower according to the cooling side load change, the dry-bulb temperature and the wet-bulb temperature of outdoor air and the flow of the cooling water, so that the cooling capacity of the indirect evaporative cooling tower is matched with the refrigeration load change, the precise adjustment and the energy-saving and high-efficiency advantages are achieved, and the application is suitable for the cooling system in the industrial and commercial fields.

Description

Technical Field

[0001] This invention relates to the field of refrigeration and air conditioning, and in particular to an energy-saving control method for an indirect evaporator based on load changes. Background Technology

[0002] Unlike direct evaporative cooling towers, indirect evaporative cooling towers use a portion of the cooling water after heat and moisture exchange to pre-cool the air entering the tower, thus lowering the dry-bulb temperature of the air and consequently lowering the wet-bulb temperature. The pre-cooled air then exchanges heat and moisture with the cooling water to produce low-temperature water that is lower than the wet-bulb temperature of the outdoor air. Theoretically, indirect evaporative cooling towers can produce cold water with the same dew point temperature as the outdoor air.

[0003] However, the flow distribution of primary and secondary water in indirect evaporative cooling towers is still unclear. Research is urgently needed on how to prepare cooling water that can meet the cooling load while keeping the power consumption of the cooling tower low. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a control method and evaporator for an indirect evaporative cooling tower that controls the primary and secondary water flow rates of the indirect evaporative cooling tower based on changes in cooling load, dry and wet bulb temperatures of outdoor air, and cooling water flow rate, thereby achieving matching between the cooling capacity of the indirect evaporative cooling tower and changes in cooling load, and achieving precise regulation, energy saving and high efficiency.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Energy-saving control methods for indirect evaporation towers based on load changes include:

[0007] A heat dissipation model and an energy consumption model for an indirect evaporative cooling tower are established. The heat dissipation model for the indirect evaporative cooling tower is as follows:

[0008]

[0009] m a =F a ×ρ a ÷3600

[0010] m wr =G wr ×ρ wr ÷3600

[0011] In the formula: Q ct For heat exchange in cooling towers; m a ρ is the mass flow rate of air. a F is the density of air. a The air volume of the cooling tower fan; m wl The flow rate (m) of water passing through the surface cooler in an indirect evaporative cooling tower.wr ρ is the mass flow rate of the cooling water. wr T is the density of chilled water. wr T represents the return temperature of the cooling water. wb a1 to a4 are the air wet-bulb temperature; a1 to a4 are the coefficients of the cooling tower heat transfer constraint model.

[0012] The energy consumption model for an indirect evaporative cooling tower is as follows:

[0013] P ct =b0+b1F a +b2F a 2 +b3G wl +b4G wl 2

[0014]

[0015] In the formula: P ct Energy consumption of indirect evaporative cooling tower; F a G represents the air volume of the cooling tower fan. wl ρ is the water flow rate through the surface cooler of the cooling tower. wl ρ is the density of water; b0~b4 are the energy consumption model coefficients for indirect evaporative cooling towers;

[0016] a1~a4 and b0~b4 were obtained by calling the MATLAB fitting function based on actual operating data of the indirect evaporative cooling tower.

[0017] Calling the NLINFIT function in MATLAB:

[0018] [beta,R,J,CovB,MSE,ErrorModelInfo]=nlinfit(X,p,myfunc,beta0)

[0019] Where R is the residual, J is the Jacobian matrix, CovB is the estimated variance-covariance matrix, MSE is the mean squared error, and ErrorModelInfo is the error model fitting information.

[0020] Set constraints and adjust them to find the optimal cooling water temperature, primary cooling water flow rate, and secondary cooling water flow rate.

[0021] Based on the sought optimal cooling water temperature, primary cooling water flow rate, and secondary cooling water flow rate, the indirect evaporative cooling tower achieves the optimal real-time energy consumption operating state.

[0022] The constraints are as follows:

[0023] Q ct =c w m wr(T w,r -T w )

[0024] c a m a (T o -T a ) = c w m wl (T wl -T w )

[0025] m wl (T w,sp -T wl ) = m wr (T wr -T w,sp )

[0026] c w (m wl +m wr (T) w,sp -T w ) = m a (d w -d o )r

[0027] 15℃≤T w ≤32℃

[0028] Where: m wr c is the mass flow rate of cooling water. a T is the specific heat capacity of air. o Outdoor dry-bulb temperature; T a c is the dry-bulb temperature of the air after it passes through the surface cooler. w T is the specific heat capacity of water. wl The temperature of water after it has passed through the surface cooler; T w The temperature of the cooling water produced by the indirect evaporative cooling tower; T w,sp The temperature of the mixture of primary water and cooling water return water after the primary water flows through the surface cooler; d w d represents the saturated air moisture content corresponding to the state of the air passing through the surface cooler. o ρ represents the humidity of outdoor air; r represents the latent heat of vaporization of water.

[0029] outdoor air humidity d o for:

[0030]

[0031] Where: P s P is the saturated water vapor pressure; P is the atmospheric pressure. This refers to the relative humidity of the air.

[0032] The saturated water vapor pressure P s for:

[0033]

[0034] The saturated air moisture content d corresponding to the state of the air passing through the surface cooler w for:

[0035] d w =622P s / (P-P s ).

[0036] Genetic algorithms are used to find the optimal cooling water temperature and the flow rates of primary and secondary cooling water.

[0037] Methods for finding the optimal cooling water temperature, primary cooling water flow rate, and secondary cooling water flow rate using genetic algorithms include:

[0038] (1) Generation of the initial population: Select n genotypes as the initial population and randomize the gene values ​​F. a,i G wl,i Among them, F a,i For different airflow values ​​of the cooling tower; G wl,i The values ​​are taken to represent different water flow rates through the surface cooler; i = 1, 2, 3, ..., n;

[0039] (2) Determining the fitness function: Based on the characteristics of the problem, determine a suitable fitness function to evaluate the fitness of each individual in the solution space;

[0040]

[0041] Where Fit(i) is the fitness function; P(i) is the energy consumption of the objective function in the control strategy under different independent variables of air volume and water volume; δ is a constant, which can be taken as 1 to avoid the denominator being 0;

[0042] (3) Selection operation: Through a certain selection strategy, superior individuals in the population are passed on to the next generation, while inferior individuals are eliminated; individuals with high fitness values ​​are selected for crossover operation, and the selection probability of each individual is Q(i):

[0043]

[0044] Using the selection probability Q(i) as the new probability distribution, individuals are selected from the current population to reorganize a new individual population newF. a,i (t+1), newG a,i (t+1);

[0045] (4) Crossover operation: Randomly select two different individuals from the population with probability Pc By exchanging genes, two new individuals are obtained. This process is repeated n / 2 times to obtain a new population, crossF. a,i (t+1), crossG a,i (t+1);

[0046] (5) Mutation operation: Randomly select an individual from the population with a mutation probability P m Chromosomal mutations were performed to obtain a new population, mutF. a,i (t+1), mutG a,i (t+1), this population, as the subpopulation that has completed one genetic operation, is then passed on to step (2);

[0047] (6) Termination condition check: When the fitness function reaches its maximum value or a certain number of iterations, stop the genetic operation and return the optimal solution;

[0048] Determine the objective function:

[0049] P ct =b0+b1F a +b2F a 2 +b3G wl +b4G wl 2

[0050] Constraints:

[0051]

[0052] Q ct =c w m wr (T w,r -T w )

[0053] c a m a (T o -T a ) = c w m wl (T wl -T w )

[0054] m wl (T w,sp -T wl ) = m wr (T wr -T w,sp )

[0055] c w (m wl +m wr (T)w,sp -T w ) = m a (d w -d o )r

[0056] 15℃≤T w ≤32℃

[0057] In MATLAB, the genetic algorithm toolkit is called, and the cooling load and outdoor environmental parameters are used as input parameters to iteratively find the optimal cooling water temperature and the flow rates of primary and secondary cooling water.

[0058] The present invention also provides an indirect evaporative cooling tower, wherein the cooling water temperature and the flow rates of primary and secondary cooling water are controlled according to the aforementioned indirect evaporative cooling tower energy-saving control method based on load variation.

[0059] The beneficial effects of this invention are:

[0060] Compared to the traditional operation mode of indirect evaporative cooling towers, this invention uses a genetic algorithm to optimize the cooling water temperature, the water flow rate through the surface cooler, and the water flow rate through the chiller unit's cooling side based on the cooling-side load and the outdoor air wet-bulb temperature. This determines the cooling water supply temperature and the maximum heat dissipation of the cooling tower under different operating conditions. Therefore, the water valve A through the surface cooler and the water valve B through the cooling side can be adjusted according to actual heat dissipation needs to achieve the optimal real-time energy consumption of the indirect evaporative cooling tower, thus achieving the goal of energy-saving operation. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of the indirect evaporative cooling tower provided in an embodiment of the present invention;

[0063] Explanation of icon numbers:

[0064] 1. Chiller unit; 2. Cooling water supply valve; 3. Surface cooler supply valve; 4. Water pump; 5. Surface cooler (water-air heat exchanger); 6. Fan; 7. Spray nozzle; 8. Packing layer; 9. Water collection tray;

[0065] Figure 2 A schematic diagram of the process for solving the cooling water parameters of an indirect evaporative cooling tower using a genetic algorithm;

[0066] Figure 3 This is a schematic diagram of the control strategy flow for the indirect evaporative cooling tower of the present invention. Detailed Implementation

[0067] Reference Figure 1 As shown, the indirect evaporative cooling tower of this invention includes an air inlet, a fan, a cooling spray device, cooling packing, a self-circulating water pump, and a water-air heat exchanger.

[0068] Reference Figure 2 , Figure 3 The control method of the present invention is as follows: establish a heat dissipation model and an energy consumption model of the indirect evaporative cooling tower, determine appropriate constraints, and use an optimization algorithm to calculate the optimal cooling water temperature, cooling water flow rate, and primary and secondary cooling water flow rates based on outdoor air conditions and cooling load changes.

[0069] Indirect evaporative cooling tower heat dissipation model:

[0070]

[0071] m a =F a ×ρ a ÷3600

[0072] m wr =G wr ×ρ wr ÷3600

[0073] In the formula: Q ct For heat exchange in the cooling tower, kW; m a ρ is the mass flow rate of air, kg / s; a The density of air, kg / m³ 3 , ρ a =1.2kg / m 3 ;F a For cooling tower fan airflow, m 3 / h;m wl The flow rate of water passing through the surface cooler in an indirect evaporative cooling tower is expressed in kg / s and m³. wr The mass flow rate of cooling water is kg / s; ρ wr The density of chilled water is kg / m³. 3 , ρ wr =1.0×10 3 kg / m 3 ;T wr T represents the return water temperature, in °C. wb denoted as the wet-bulb temperature of air, in °C; a1~a4 are the coefficients of the cooling tower heat transfer constraint model.

[0074] Indirect evaporative cooling tower energy consumption model:

[0075] P ct =b0+b1F a +b2F a 2 +b3G wl +b4G wl 2

[0076]

[0077] In the formula: P ct Energy consumption of indirect evaporative cooling tower, kW; F a For cooling tower fan airflow, m 3 / h;G wl The flow rate of water passing through the surface cooler of the cooling tower is m. 3 / h, ρ wl ρ is the density of water. wl =1.0×10 3 kg / m 3 b0~b4 are the energy consumption model coefficients for indirect evaporative cooling towers.

[0078] a1~a4 and b0~b4 were obtained by calling the MATLAB fitting function based on actual operating data of the indirect evaporative cooling tower.

[0079] Calling the NLINFIT function in MATLAB:

[0080] [beta,R,J,CovB,MSE,ErrorModelInfo]=nlinfit(X,p,myfunc,beta0)

[0081] Where R is the residual, J is the Jacobian matrix, CovB is the estimated variance-covariance matrix, MSE is the mean squared error, and ErrorModelInfo is the error model fitting information.

[0082] Constraints:

[0083] Q ct =c w m wr (T w,r -T w )

[0084] c a m a (T o -T a ) = c w m wl (T wl -T w )

[0085] m wl (T w,sp -T wl ) = m wr (T wr -T w,sp )

[0086] c w (m wl +m wr (T) w,sp -T w ) = m a (d w -d o )r

[0087] 15℃≤T w ≤32℃

[0088] Where: m wr The mass flow rate of cooling water is kg / h; c a Let c be the specific heat capacity of air. a = 1.005 kJ / (kg·K); T o Outdoor dry-bulb temperature, °C; T a The dry-bulb temperature of the air after passing through the surface cooler, in °C; c w Let Cw be the specific heat capacity of water, and take it as cw = 4.2 kJ / (kg·K); T wl The temperature of water after it has passed through the surface cooler, in °C; T w The temperature of the cooling water produced by the indirect evaporative cooling tower, in °C; T w,sp The temperature of the mixture of primary water flow and cooling water return water after passing through the surface cooler, in °C; d w The moisture content of saturated air after passing through the surface cooler, in g / kg; d o ρ is the humidity of outdoor air, g / kg; r is the latent heat of vaporization of water, r = 2257.2 kJ / kg.

[0089]

[0090] Where: P s The saturated water vapor pressure, Pa; P is atmospheric pressure, P = 101325 Pa; The relative humidity of the air, in %.

[0091] d w =622P s / (P-P s )

[0092] To address the above, the Genetic Algorithm (GA) is employed to solve for the optimal parameters. GA is a global optimization algorithm that simulates natural evolution and genetic theory. By transferring the optimization problem, it successfully avoids the need to consider too much dynamic information in general optimization algorithms. In principle, it breaks through the framework of conventional optimization algorithms, with a simpler algorithm structure, stronger information processing capabilities, and good robustness.

[0093] Genetic algorithms typically involve the following steps:

[0094] (1) Generation of the initial population: Select an appropriate number of genotypes as the initial population, and randomize the values ​​of their genes, F a,i For different airflow values ​​of the cooling tower; G wl,i The values ​​are taken to represent different water flow rates through the surface cooler.

[0095] F a,i G wl,i i = 1, 2, 3, ..., n

[0096] (2) Determining the fitness function: Based on the characteristics of the problem, determine a suitable fitness function to evaluate the fitness of each individual in the solution space.

[0097]

[0098] Where P(i) represents the energy consumption of the objective function in the control strategy under different independent variables, i.e., air volume and water volume, and δ is a constant, which can be set to 1 to avoid the denominator being 0.

[0099] (3) Selection operation: A selection strategy is used to pass on superior individuals to the next generation and eliminate inferior individuals. If individuals with high fitness values ​​are selected for crossover, the selection probability of each individual is Q(i):

[0100]

[0101] Using the selection probability Q(i) as the new probability distribution, individuals are selected from the current population to reorganize a new individual population newF. a,i (t+1), newG a,i (t+1):

[0102] (4) Crossover operation: Randomly select two different individuals from the population with probability P c By exchanging genes, two new individuals are obtained. This process is repeated n / 2 times to obtain a new population, crossF. a,i (t+1), crossG a,i (t+1).

[0103] (5) Mutation operation: Randomly select an individual from the population with a mutation probability P m Chromosomal mutations were performed to obtain a new population, mutF. a,i (t+1), mutG a,i (t+1), this population, as the subpopulation that has completed one genetic operation, is then passed on to step (2).

[0104] (6) Termination condition check: When the fitness function reaches its maximum value or a certain number of iterations, stop the genetic operation and return the optimal solution.

[0105] Determine the objective function:

[0106] P ct =b0+b1F a +b2F a 2 +b3G wl +b4G wl 2

[0107] Constraints:

[0108]

[0109] Q ct =c w m wr (T w,r -T w )

[0110] c a m a (T o -T a ) = c w m wl (T wl -T w )

[0111] m wl (T w,sp -T wl ) = m wr (T wr -T w,sp )

[0112] c w (m wl +m wr (T) w,sp -T w ) = m a (d w -d o )r

[0113] 15℃≤T w ≤32℃

[0114] In MATLAB, the genetic algorithm toolkit is called, and the cooling load and outdoor environmental parameters are used as input parameters to iteratively find the optimal cooling water temperature and the flow rates of primary and secondary cooling water.

Claims

1. An energy-saving control method for indirect evaporative cooling towers based on load variation, characterized in that, include: A heat dissipation model and an energy consumption model for an indirect evaporative cooling tower are established. The heat dissipation model for the indirect evaporative cooling tower is as follows: In the formula: To exchange heat in the cooling tower; Mass flow rate of air; The density of air; This refers to the airflow of the cooling tower fan. This refers to the water flow rate through the surface cooler in an indirect evaporative cooling tower. This refers to the mass flow rate of the cooling water. The density of chilled water; T w,r This refers to the cooling water return temperature. The wet-bulb temperature of the air; These are the coefficients of the heat transfer constraint model for the cooling tower; The energy consumption model for an indirect evaporative cooling tower is as follows: In the formula: Energy consumption of indirect evaporative cooling tower; This refers to the airflow of the cooling tower fan. The water flow rate through the surface cooler of the cooling tower. The density of water; These are the coefficients for the energy consumption model of indirect evaporative cooling towers; Set constraints and adjust them to find the optimal cooling water temperature, primary cooling water flow rate, and secondary cooling water flow rate. Based on the sought optimal cooling water temperature, primary cooling water flow rate, and secondary cooling water flow rate, the indirect evaporative cooling tower achieves the optimal real-time energy consumption operating state. Genetic algorithms are used to find the optimal cooling water temperature and the flow rates of primary and secondary cooling water.

2. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 1, characterized in that, The constraints are as follows: in: This refers to the mass flow rate of the cooling water. The specific heat capacity of air; The outdoor dry-bulb temperature; The dry-bulb temperature of the air after it passes through the surface cooler; This is the specific heat capacity of water; The temperature of water after it has flowed through the surface cooler; The temperature of the cooling water produced by the indirect evaporative cooling tower; The temperature of the water mixed with the cooling water return after the primary water flows through the surface cooler; This represents the saturated air moisture content corresponding to the state of air passing through the surface cooler. ρ represents the humidity of outdoor air; r represents the latent heat of vaporization of water.

3. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 2, characterized in that, Moisture content of outdoor air for: in: It is the saturated water vapor pressure; Atmospheric pressure; This refers to the relative humidity of the air.

4. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 3, characterized in that, Saturated water vapor pressure for: 。 5. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 2, characterized in that, The saturated air moisture content corresponding to the state of the primary air passing through the surface cooler for: 。 6. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 1, characterized in that, Cooling tower heat transfer constraint model coefficients Indirect evaporative cooling tower energy consumption model coefficients The following results were obtained by using actual indirect evaporative cooling tower operating data and applying a MATLAB fitting function: Calling the NLINFIT function in MATLAB: Where R is the residual and J is the Jacobian matrix. To estimate the variance-covariance matrix, MSE is the mean squared error. This is used to fit information to the error model.

7. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 1, characterized in that, Methods for finding the optimal cooling water temperature, primary cooling water flow rate, and secondary cooling water flow rate using genetic algorithms include: (1) Generation of the initial population: Select n genotypes as the initial population and randomize the values ​​of their genes. , ;in, Different values ​​are taken for different cooling tower airflow rates; The values ​​represent different flow rates of water passing through the surface cooler; i = 1, 2, 3, ..., n; (2) Determination of fitness function: Based on the characteristics of the problem, a suitable fitness function is determined to evaluate the fitness of each individual in the solution space; in, The fitness function; The objective function in the control strategy is to control energy consumption under different independent variables of air volume and water volume; It is a constant; (3) Selection operation: Through a certain selection strategy, superior individuals in the population are passed on to the next generation, while inferior individuals are eliminated; individuals with high fitness values ​​are selected for crossover operation, and the selection probability of each individual is... : Based on selection probability As a new probability distribution, individuals are selected from the current population to reorganize a new individual population. , ; (4) Crossover operation: Randomly select two different individuals from the population, with probability... By exchanging genes, two new individuals are obtained. This process is repeated n / 2 times to obtain a new population. , ; (5) Mutation operation: Randomly select individuals from the population and perform mutation operations based on the mutation probability. Chromosomal mutations were performed to obtain a new population. , This population, as a subpopulation that has completed one genetic operation, is then passed on to step (2); (6) Verification of termination conditions: When the fitness function reaches its maximum value or a certain number of iterations, stop the genetic operation and return the optimal solution; Determine the objective function: Constraints: In MATLAB, the genetic algorithm toolkit is called, and the cooling load and outdoor environmental parameters are used as input parameters to iteratively find the optimal cooling water temperature and the flow rates of primary and secondary cooling water.

8. The energy-saving control method for indirect evaporative cooling towers based on load variation according to claim 7, characterized in that, The value is 1.

9. An indirect evaporative cooling tower, characterized in that, The cooling water temperature and the flow rates of primary and secondary cooling water in the indirect evaporative cooling tower are controlled according to any one of claims 1-8.

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