An intelligent optimization and energy-saving control method for a heat pump rotary dehumidification system

Abstract

The present invention discloses an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system, belonging to the technical field of heat pump rotary wheel dehumidification system optimization. The method includes: establishing a numerical model of the rotary wheel dehumidification system, and setting the initial conditions and boundary conditions of various variable parameters in the model; establishing a heat pump system model; coupling the numerical model of the rotary wheel dehumidification system with the heat pump system model; solving the mathematical model of the heat pump rotary wheel dehumidification system, calculating and outputting the outlet parameters and performance indicators of the heat pump rotary wheel dehumidification system; establishing an energy consumption model of the heat pump rotary wheel dehumidification system according to the regeneration energy consumption and fan energy consumption of the heat pump rotary wheel dehumidification system, and calculating the total system energy consumption of the heat pump rotary wheel dehumidification system; determining the objective function; selecting the parameters to be optimized that can be directly controlled; combining the objective function, using an optimization algorithm to optimize the parameters to be optimized, and obtaining the optimized parameters; adjusting the corresponding equipment of the heat pump rotary wheel dehumidification system according to the optimized parameters; repeating the optimization and making real-time adjustments.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimizing heat pump rotary wheel dehumidification systems, and particularly relates to an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system. Background Technique

[0002] Due to its compact structure, high dehumidification efficiency, and ability to achieve continuous dehumidification and regeneration processes, the heat pump rotary wheel dehumidification system has gradually become the mainstream in the field of air dehumidification. The energy-saving transformation of the heat pump rotary wheel dehumidification system is of great significance for reducing system energy consumption.

[0003] The energy-saving transformation of the heat pump rotary wheel dehumidification system mainly involves optimizing the set values of the operating parameters of the heat pump rotary wheel dehumidification system and optimizing the structure of the heat pump rotary wheel dehumidification system. Currently, more fixed parameter control is used in setting the operating parameters of the heat pump rotary wheel dehumidification system. When the system operates, fixed rotary wheel speeds, fan frequencies, and regeneration temperature set values are set. However, changes in ambient temperature and humidity will cause the outlet conditions of the heat pump dehumidification rotary wheel system to change with the ambient temperature and humidity, making it difficult to maintain the system dehumidification effect at the set target value. And when operating at a constant set regeneration temperature, when the regeneration temperature of the heat pump rotary wheel dehumidification system can operate normally below the set regeneration temperature, the regeneration temperature remains constant all the time, resulting in waste of regeneration energy. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system, which can solve the technical problems that when the existing system operates, fixed operating parameters are set, but changes in ambient temperature and humidity will cause the outlet conditions of the heat pump dehumidification rotary wheel system to change with the ambient temperature and humidity, making it difficult to maintain the system dehumidification effect at the set target value, and when operating at a constant set regeneration temperature, when the regeneration temperature of the heat pump rotary wheel dehumidification system can operate normally below the set regeneration temperature, the regeneration temperature remains constant all the time, resulting in waste of regeneration energy.

[0005] To solve the above technical problems, the present invention is implemented as follows:

[0006] The embodiments of the present invention provide an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system, including:

[0007] S101: Establish a numerical model for multiple variable parameters of the rotary wheel dehumidification system, and set the initial conditions and boundary conditions of each variable parameter in the numerical model, where the variable parameters include: air moisture content, air moisture content in equilibrium with the adsorbent surface of the rotary wheel dehumidification system, air temperature, air temperature in equilibrium with the surface of the adsorbent, and adsorbent adsorption capacity;

[0008] S102: Establish a heat pump system model, where the heat pump system model includes: a compressor model, a condenser model, a thermostatic expansion valve model, and an evaporator model;

[0009] S103: Couple the numerical model of the rotary wheel dehumidification system with the heat pump system model to obtain a mathematical model of the heat pump rotary wheel dehumidification system;

[0010] S104: Solve the mathematical model of the heat pump rotary wheel dehumidification system, calculate and output the outlet parameters and performance indicators of the heat pump rotary wheel dehumidification system;

[0011] S105: Establish an energy consumption model of the heat pump rotary wheel dehumidification system based on the regeneration energy consumption and fan energy consumption of the heat pump rotary wheel dehumidification system, and calculate the total system energy consumption of the heat pump rotary wheel dehumidification system;

[0012] S106: Combine the total system energy consumption and the moisture content of the outlet air of the processed air to determine an objective function, where the objective function is used to enable the heat pump rotary wheel dehumidification system to meet two objectives of dehumidification requirements and maximum energy efficiency;

[0013] S107: Classify various variable parameters of the heat pump rotary wheel dehumidification system according to whether they are controllable, and select the optimization parameters that can be directly controlled. The optimization parameters include: regeneration temperature, wind speed, and rotary wheel speed;

[0014] S108: Combine the objective function and use an optimization algorithm to optimize the optimization parameters to obtain optimized parameters;

[0015] S109: Adjust the corresponding equipment of the heat pump rotary wheel dehumidification system according to the optimized parameters;

[0016] S110: Repeat S108 - S109.

[0017] In the embodiment of the present invention, the numerical model of the rotary wheel dehumidification system and the heat pump system model are coupled to establish a mutually dependent relationship between the rotary wheel dehumidification system and the heat pump system, and overall optimization of the heat pump rotary wheel dehumidification system is carried out. Combining the total system energy consumption of the heat pump rotary wheel dehumidification system and the moisture content of the outlet air of the processed air, an objective function is determined. According to the changes in the environment, the optimization algorithm is used to optimize the optimization parameters in real time, and the best values of the optimization parameters are determined, so that the heat pump rotary wheel dehumidification system can meet two objectives of dehumidification requirements and maximum energy efficiency at the same time. It has strong environmental adaptability, can effectively reduce the total system energy consumption while meeting the dehumidification requirements, and improve the system operation efficiency. Brief Description of the Drawings

[0018] Figure 1 It is a schematic flowchart of an intelligent optimization and energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0019] Figure 2It is a schematic diagram of the lgP-H diagram and T-s diagram of the heat pump system cycle of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of the heat transfer of a countercurrent condenser of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0021] Figure 4 It is a schematic diagram of the heat transfer of a countercurrent evaporator of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0022] Figure 5 It is a schematic diagram of the heat pump rotary wheel dehumidification structure of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0023] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings. Specific embodiments

[0024] In order to make the object, technical solution and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] The intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention will be described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0026] Refer to Figure 1 , which shows a schematic flow chart of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0027] An intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention includes:

[0028] S101: Establish a numerical model for multiple variable parameters of the rotary wheel dehumidification system, and set the initial conditions and boundary conditions of each variable parameter in the numerical model, where the variable parameters include: air moisture content, air moisture content in equilibrium with the adsorbent surface of the rotary wheel dehumidification system, air temperature, air temperature in equilibrium with the surface of the adsorbent, and adsorbent adsorption capacity.

[0029] In a possible implementation manner, S101 specifically includes:

[0030] S1011: Establish the numerical model of multiple change parameters of the rotary dehumidification system as follows:

[0031] Introduce the air-side mass balance equation, air-side heat balance equation, adsorption-side mass differential equation, and adsorption-side heat differential equation:

[0032] The air-side mass balance equation is:

[0033] The air-side heat balance equation is:

[0034] The adsorption-side mass differential equation is:

[0035] The adsorption-side heat differential equation is:

[0036] Among them, Y a represents the moisture content of the air, Y d represents the moisture content of the air in equilibrium with the adsorbent surface, T a represents the air temperature, T d represents the air temperature in equilibrium with the adsorbent surface, W represents the adsorption capacity of the adsorbent, u a represents the air flow rate, unit m / s, h m represents the mass transfer coefficient, unit m 2 / s; h represents the heat transfer coefficient, unit W / (m 3 *K), P represents the perimeter of the air channel of the rotary dehumidification system, unit m, A represents the cross-sectional area of the air channel of the rotary dehumidification system, unit m 2 , ρ a and ρ d respectively represent the densities of air and adsorbent material, unit kg / m 3 , c pg , c pv , c pl and c pd respectively represent the specific heat capacities at constant pressure of air, water vapor, liquid water, and adsorbent material, unit J / (kg*K), q st represents the heat of adsorption, unit J / kg;

[0037] Based on the curve of the air channel being a sine curve type, establish the perimeter equation A and the flow area equation P of the air channel of the rotary dehumidification system:

[0038] A = 2ab

[0039]

[0040] Among them, 2a and 2b respectively represent the height and width of the air channel;

[0041] Combined with the moisture content of the air, the moisture content of the air in equilibrium with the adsorbent surface, and the air temperature in equilibrium with the adsorbent surface, establish the vapor saturation pressure equation, the moisture content and relative humidity equation, and the relationship equation between the material balance relative humidity and the adsorbent adsorption capacity as the connection equations among the three:

[0042] Vapor saturation pressure equation:

[0043]

[0044] Among them, p s represents the vapor saturation vapor pressure;

[0045] Moisture content and relative humidity equation:

[0046]

[0047] φ represents the relative humidity of the air, and p represents the ambient atmospheric pressure;

[0048] The relationship equation between the relative humidity of the air and the adsorption capacity of the adsorbent is obtained by fitting the adsorption isotherm of the adsorption material:

[0049]

[0050] W max represents the saturated adsorption capacity of the adsorption material, and C represents the constant fitted from the experimental data.

[0051] S1012: Set the initial conditions and boundary conditions for each variable parameter in the numerical model:

[0052] The initial conditions are:

[0053]

[0054] The boundary conditions of the numerical models of the dehumidification zone and the regeneration zone are respectively:

[0055] For the dehumidification zone:

[0056]

[0057] For the regeneration zone:

[0058]

[0059] Referring to Figure 2 , the lgP-H diagram and T-s diagram of the heat pump system cycle of an intelligent optimization and energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention are shown.

[0060] Figure 2The inlet and outlet state points of the compressor are point 1 and point 2 respectively. The process from 1 to 2 is the compression process of the compressor, and the process from 2 to 3 is the isobaric condensation process of the condenser. The refrigerant is in the superheated vapor state during the 2 - 2' stage, in the gas - liquid coexistence two - phase state under the condensation pressure during the 2' - 3' stage, and in the sub - cooled liquid state during the 3' - 3 stage. The process from 3 to 4 is the expansion process of the thermostatic expansion valve, and the process from 4 to 1 is the isobaric evaporation process of the evaporator. Among them, the refrigerant is in the two - phase state under the evaporation pressure during the 4 - 1' stage and in the superheated vapor state during the 1' - 1 stage.

[0061] S102: Establish a heat pump system model, where the heat pump system model includes: a compressor model, a condenser model, a thermostatic expansion valve model, and an evaporator model.

[0062] Among them, the heat pump system mainly includes four components: a compressor, a condenser, a thermostatic expansion valve, and an evaporator.

[0063] It should be noted that during the process of establishing the compressor model, in the thermodynamic cycle of the refrigerant, the evaporation pressure and the condensation pressure remain unchanged. Given the superheat degree of the evaporator, the enthalpy value h1 of point 1 can be determined according to the evaporation pressure and the superheat degree. Since the compression process of the compressor can be regarded as an adiabatic compression under ideal conditions, and the refrigerant is regarded as an ideal gas, according to the thermodynamic calculation formula of the isentropic process, the enthalpy value h2 of the outlet state point 2 can be obtained.

[0064] Refer to Figure 3 , which shows the counter - flow condenser heat transfer schematic diagram of an intelligent optimization energy - saving control method for a heat pump runner dehumidification system provided by an embodiment of the present invention.

[0065] It should be noted that during the process of establishing the condenser model, the condenser needs to go through three different stages from point 2 to point 2', from point 2' to point 3', and from point 3' to point 3, and the three different stages correspond to the superheat zone, the two - phase zone, and the sub - cooled zone respectively. Figure 3 is the heat transfer process between the refrigerant and the regeneration air in the condenser under the counter - flow state. Since the calculation methods of the heat transfer coefficient of the refrigerant in the two - phase zone and the single - phase zone are different, it is necessary to calculate the heat transfer separately for different stages. Among them, the ε - NTU method is used for calculation in the two - phase zone, and the logarithmic mean temperature difference method is used for calculation in the single - phase zone.

[0066] It should be noted that during the process of establishing the thermostatic expansion valve model, the inlet and outlet state points of the thermostatic expansion valve are Figure 2 points 3 and 4 in. The high - pressure refrigerant coming out of the condenser undergoes a thermodynamic expansion in the thermostatic expansion valve and is cooled and depressurized to the gas - liquid two - phase flow state under the evaporation pressure, and this process can be regarded as an isenthalpic process.

[0067] Refer to Figure 4, showing the countercurrent evaporator heat transfer schematic diagram of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0068] It should be noted that during the process of establishing the evaporator model, the evaporator needs to go through two different stages, such as from point 4 to point 1' and from point 1' to point 1' as shown in Figure 2 . Figure 4 This is the heat transfer process between the refrigerant and the processed air in the evaporator under the countercurrent state. Similar to the condenser, the internal heat transfer process of the evaporator also includes two-phase and single-phase states, and the ε-NTU method and the logarithmic mean temperature difference method need to be used respectively for heat transfer calculation.

[0069] In a possible implementation manner, S102 is specifically:

[0070] S1021: Establishing a heat pump system model includes the following steps:

[0071] Establishing a compressor model:

[0072]

[0073] Among them, P c represents the condensation pressure, P e represents the evaporation pressure, v1 represents the suction specific volume of the compressor, and k represents the adiabatic index of the refrigerant.

[0074] Among them, the calculation method of the adiabatic index of the refrigerant is:

[0075]

[0076] Among them, c p represents the specific heat capacity at constant pressure of the refrigerant, and c v represents the specific heat capacity at constant volume of the refrigerant.

[0077] Calculating the power consumption P com of the compressor according to the inlet enthalpy value and the outlet enthalpy value of the compressor:

[0078]

[0079] Among them, represents the refrigerant flow rate, and η m represents the electrical efficiency of the compressor.

[0080] Among them, the calculation method of the electrical efficiency of the compressor is:

[0081]

[0082] Among them, V h represents the theoretical volume displacement of the compressor, and λ represents the volumetric efficiency.

[0083] Among them, the calculation method of the gas transmission index λ is as follows:

[0084] λ = λ v λ p λ T λ D

[0085] Among them, λ v represents the volumetric coefficient of the compressor, λ p represents the pressure coefficient of the compressor, λ T represents the temperature coefficient of the compressor, λ D represents the leakage coefficient of the compressor;

[0086] Establish a condenser model:

[0087] Use the ε-NTU method to calculate the heat transfer quantity Q, c,2 heat transfer effectiveness ε c and the number of transfer units NTU c,2 in the two-phase region of the condenser under the condition of condensation phase change:

[0088]

[0089]

[0090]

[0091] Use the logarithmic mean temperature difference method to determine the temperature difference in the heat transfer process of the superheat zone in the condenser, and calculate the heat transfer quantity Q c,1 and the number of transfer units NTU c,1 :

[0092]

[0093]

[0094]

[0095] Use the logarithmic mean temperature difference method to determine the temperature difference in the heat transfer process of the subcooling zone in the condenser, and calculate the heat transfer quantity Q c,3 and the number of heat transfer units NTU in the subcooling zone c,3 :

[0096] Calculate the total heat transfer quantity, total heat transfer area and total number of transfer units of the condenser:

[0097] Q c = Q c,1 + Q c,2 + Q c,3

[0098] A c = A c,1 + Ac,2 +A c,3

[0099] NTU c = NTU c,1 + NTU c,2 + NTU c,3 ;

[0100] Calculate the overall heat transfer coefficient K in the two-phase region, superheat region, and subcooling region respectively:

[0101]

[0102] Among them, α i and α o represent the convective heat transfer coefficient on the refrigerant side and the convective heat transfer coefficient on the air side respectively. For the refrigerant side heat transfer coefficient in the single-phase region;

[0103] Use the heat transfer correlation when the fluid is cooled to calculate the refrigerant side heat transfer coefficient in the single-phase region including the superheat region and the subcooling region respectively:

[0104] Nui = 0.023Re 0.8 Pr 0.3

[0105] Among them, Nu i = α i d i / λ, Re i = G i d i / μ;

[0106] Use the heat transfer relationship in the condenser to calculate the refrigerant side heat transfer coefficient in the two-phase region:

[0107]

[0108] Among them, α l represents the heat transfer coefficient of the refrigerant in the saturated liquid state at the condensation pressure, which can be calculated through the heat transfer correlation formula, and x represents the dryness of the refrigerant in the two-phase region;

[0109] Calculate the air side heat transfer coefficient:

[0110]

[0111] Among them, Nu o = α o d3 / λ, s represents the fin pitch of the heat exchanger, d3 represents the root diameter of the tube, s2 represents the tube pitch along the air flow direction, and N represents the number of tube rows;

[0112] Establish a thermostatic expansion valve model:

[0113] Calculate the refrigerant flow rate:

[0114]

[0115]

[0116] v out = xv g +(1 - x)v1

[0117] Among them, C D represents the flow coefficient of the expansion valve, A v represents the minimum flow area of the expansion valve spool, v in represents the specific volume of the refrigerant at the inlet of the expansion valve, v out represents the specific volume of the refrigerant at the outlet of the expansion valve, x represents the dryness of the refrigerant at the outlet of the expansion valve, v g represents the specific volume of the saturated vapor of the refrigerant at the outlet of the expansion valve, v l represents the specific volume of the saturated liquid of the refrigerant at the outlet of the expansion valve;

[0118] Establish an evaporator model:

[0119] Use the ε-NTU method to calculate the heat transfer quantity Q in the two-phase region under the evaporation phase change e,1 , heat transfer effectiveness ε e and the number of heat transfer units NTU e,1 :

[0120]

[0121]

[0122]

[0123] Use the logarithmic mean temperature difference method to calculate the heat transfer quantity Q in the superheat region e,2 and the number of heat transfer units NTU in the superheat region e,2 :

[0124]

[0125]

[0126]

[0127] Calculate the total heat transfer quantity, total heat transfer area and total number of heat transfer units of the evaporator:

[0128] Q e = Q e,1 + Q e,2

[0129] A e = A e,1 + A e,2

[0130] NTU e = NTU e,1 + NTU e,2 ;

[0131] The heat transfer correlation when the fluid is heated is used to calculate the refrigerant-side heat transfer coefficient in the single-phase region:

[0132] Nu i = 0.023Re 0.8 Pr 0.4 ;

[0133] The heat transfer relation in the condenser is used to calculate the refrigerant-side heat transfer coefficient and the air-side heat transfer coefficient in the two-phase region.

[0134] S103: Couple the numerical model of the rotary dehumidification system with the heat pump system model to obtain the mathematical model of the heat pump rotary dehumidification system.

[0135] S104: Solve the mathematical model of the heat pump rotary dehumidification system, and calculate and output the outlet parameters and performance indicators of the heat pump rotary dehumidification system.

[0136] In the actual use process, the known structural parameters and known conditions are input into the mathematical model of the heat pump rotary dehumidification system. First, by assuming a regeneration temperature, at the assumed regeneration temperature, calculate the numerical model of the rotary dehumidification system. Check whether the moisture content of the processed air outlet obtained can meet the dehumidification requirement. If the dehumidification requirement is met, then start to adjust the compressor, condenser, and evaporator of the heat pump system. Assume an evaporation temperature, a condensation temperature, and an evaporator superheat degree, calculate the heat pump system model. By judging the refrigerant flow rate, determine whether to enter the debugging of the evaporator model. If the refrigerant flow rate meets the preset index, then calculate the evaporator outlet superheat degree and make another judgment. After meeting the conditions, output the result to complete the solution process of the mathematical model of the heat pump rotary dehumidification system.

[0137] In a possible implementation manner, S104 specifically includes:

[0138] S1041: Input the structural parameters of the rotary wheel, compressor, condenser, and evaporator, as well as the known parameters, where the known parameters include: the inlet temperature, inlet moisture content, air volume, and rotary wheel speed of the dehumidified air and the regeneration air.

[0139] S1042: Preset the regeneration temperature, calculate the moisture content of the processed air outlet according to the numerical model of the rotary dehumidification system, and compare the moisture content of the processed air outlet with the dehumidification requirement.

[0140] S1043: Calculate the relative error between the moisture content of the processed air outlet and the required moisture content of the processed air outlet in the dehumidification requirement. When the relative error is within the acceptable range, proceed to S1044; otherwise, proceed to S1042 to reset the regeneration temperature.

[0141] S1044: Preset the evaporation temperature, condensation temperature, and superheat of the evaporator.

[0142] S1045: Solve the compressor model based on the inlet state parameters of the compressor to obtain the outlet refrigerant state parameters, power consumption, and outlet refrigerant flow rate of the compressor. Solve the condenser model based on the inlet state parameters and structural parameters of the refrigerant and air in the condenser to obtain the outlet state parameters of the refrigerant and air and the heat transfer amount of the condenser. Calculate the outlet state and refrigerant flow rate of the thermostatic expansion valve based on the refrigerant inlet parameters.

[0143] S1046: Compare the refrigerant flow rate calculated through the thermostatic expansion valve model and the refrigerant flow rate calculated through the compressor model. When the error between the two is greater than the first preset error and the outlet refrigerant flow rate of the thermostatic expansion valve is greater than the outlet refrigerant flow rate of the compressor, increase the condensation temperature and proceed to S1044. When the error between the two is greater than the first preset error and the outlet refrigerant flow rate of the thermostatic expansion valve is greater than the outlet refrigerant flow rate of the compressor, decrease the condensation temperature and proceed to S1044; otherwise, proceed to S1047.

[0144] S1047: Solve the evaporator model based on the inlet state parameters of the refrigerant and air in the evaporator and the structural parameters of the evaporator to obtain the outlet state parameters of the refrigerant and air in the evaporator and the heat transfer amount of the evaporator.

[0145] S1048: Compare the calculated superheat of the evaporator outlet and the preset superheat of the outlet. When the error between the two is greater than the second preset error and the calculated evaporation temperature is greater than the preset evaporation temperature, increase the preset evaporation temperature and proceed to S1044. When the error between the two is greater than the second preset error and the calculated evaporation temperature is less than or equal to the preset evaporation temperature, decrease the preset evaporation temperature and proceed to S1044; otherwise, proceed to S1049.

[0146] S1049: Output the outlet parameters and performance indicators of the heat pump rotary wheel dehumidification system according to the requirements.

[0147] S105: Establish an energy consumption model of the heat pump rotary wheel dehumidification system based on the regeneration energy consumption and fan energy consumption of the heat pump rotary wheel dehumidification system, and calculate the total system energy consumption of the heat pump rotary wheel dehumidification system.

[0148] In a possible implementation, S105 specifically includes:

[0149] S1051: Calculate the compressor power consumption P of the heat pump rotary wheel dehumidification system com :

[0150]

[0151] S1052: Calculate the processing fan energy consumption Q in the fan energy consumption fan,ad and the regeneration fan energy consumption Q fan,reg :

[0152]

[0153]

[0154] Among them, P fan represents the total fan pressure, in Pa, G ad represents the processing air volume, in m 3 / s, G reg represents the regeneration air volume, in m 3 / s, K represents the motor capacity reserve coefficient, and η represents the efficiency.

[0155] S1053: Combine the compressor power consumption, the processing fan energy consumption, and the regeneration fan energy consumption to establish an energy consumption model Q of the heat pump rotary wheel dehumidification system sys :

[0156] Q sys = P com + Q fan,ad + Q fan,reg .

[0157] S1054: Use the energy consumption model of the heat pump rotary wheel dehumidification system to calculate the total system energy consumption of the heat pump rotary wheel dehumidification system.

[0158] S106: Combine the total system energy consumption and the moisture content of the outlet of the processed air to determine the objective function, where the objective function is used to enable the heat pump rotary wheel dehumidification system to meet the two objectives of dehumidification requirements and maximum energy efficiency at the same time.

[0159] It should be noted that determining the objective function is to incorporate the total system energy consumption and the moisture content of the outlet of the processed air into a function, and then analyze the objective function to seek to establish a balance between the errors of the total system energy consumption and the moisture content of the outlet of the processed air, which can not only ensure that the moisture content of the outlet of the processed air meets the established requirements, but also keep the total system energy consumption at a relatively low level, improve resource utilization, and maintain the system running at maximum energy efficiency.

[0160] In one possible implementation, S106 is specifically:[[]]

[0161] S1061: Determine the objective function J by combining the total system energy consumption and the moisture content at the outlet of the processed air:

[0162]

[0163]

[0164] where COP sys represents the energy efficiency ratio, i.e., the system performance coefficient, Y a,out represents the moisture content at the outlet of the processed air, in g / kg, which can be obtained by calculating through the numerical model of the rotary wheel dehumidification system, Y a,ref represents the set value of the moisture content at the outlet of the processed air, in g / kg, ε aim represents the set value of the acceptable error, represents the mass flow rate of the processed air, and d1 and d2 represent the moisture content at the inlet and outlet of the processed air respectively.

[0165] S107: Classify various variable parameters of the heat pump rotary wheel dehumidification system according to whether they are controllable, and select the controllable parameters to be optimized. Among them, the parameters to be optimized include: regeneration temperature, wind speed, and rotary wheel speed.

[0166] In the actual application process, for the uncontrollable variable parameters, the uncontrollable parameters are collected as acquisition parameters. Among them, the uncontrollable variables include but are not limited to environmental temperature, environmental moisture content, rotary wheel thickness, and rotary wheel material, etc.

[0167] It should be noted that the heat pump rotary wheel dehumidification system has various variables, including processed air-related parameters, regeneration air-related parameters, rotary wheel-related parameters, and so on. Some of these variables are uncontrollable variables, such as environmental temperature, environmental moisture content, rotary wheel thickness, rotary wheel material, etc. During the optimization control process, these uncontrollable variable parameters will be collected in real time as acquisition parameters according to the actual situation. Another part of the controllable variables, such as regeneration temperature, wind speed, rotary wheel speed, etc., can be directly controlled by adjusting the electric heating time, fan, motor, etc. Therefore, the three variables of regeneration temperature, wind speed, and rotary wheel speed are used as the parameters to be optimized for optimization.

[0168] S108: Combine the objective function and use an optimization algorithm to optimize the parameters to be optimized to obtain the optimized parameters.

[0169] In one possible implementation, S108 specifically includes:

[0170] S1081: Initialize the population.

[0171] In one possible implementation, S1081 specifically includes:

[0172] S1081A: Divide 10 - 20% of the samples in the population into discoverers, and divide the remaining samples into followers.

[0173] S1081B: According to the parameters to be optimized, set the solution space of the optimization algorithm to 3 - dimensional. Based on the three parameters to be optimized, determine the position of the i - th sample as:

[0174] X i =(X i,1 ,X i,2 ,X i,3 )=(T a,reg,i ,u a,i ,r i ), i ∈ [1, n]

[0175] Among them, n represents the number of samples, and i represents the i - th sample.

[0176] S1082: Select excellent samples in the population as discoverers according to the objective function, and set the iteration conditions. Among them, the population includes multiple samples, and the multiple samples are divided into three categories: discoverers, followers, and scouting early - warning personnel. The iteration conditions include: the maximum number of iterations and the convergence accuracy of the parameters to be optimized.

[0177] It should be noted that the optimization algorithm needs to set iteration conditions to determine whether the optimization algorithm has achieved its goal and whether to end the optimization. During the optimization process, reaching the maximum number of iterations may be caused by various reasons. During the continuous iteration process, if the parameters to be optimized obtained by the optimization meet the initially set iteration conditions, the iteration can be terminated in advance, and the optimized parameters can also be obtained.

[0178] In a possible implementation manner, S1082 is specifically:

[0179] S1082A: Select excellent groups in the population as discoverers according to the objective function:

[0180] X select ={X s} if Prob s >R1, s ∈ [1, n]

[0181]

[0182]

[0183] Among them, P tot (X i ) represents the value of the objective function corresponding to the i - th sample, Prob i represents the probability that the i - th sample is selected into the excellent group, and R1 represents a random number uniformly distributed between (0, 1).

[0184] S1083: Update the discoverer, follower, and scout early warning agent.

[0185] In a possible implementation, S1083 specifically includes:

[0186] S1083A: Update the discoverer:

[0187]

[0188] Wherein, represents the value of the j-th dimension of the i-th sample at the t-th iteration, R1, R2 represent random numbers uniformly distributed between (0, 1), iter max represents the maximum number of iterations, ST represents a random number between (0.5, 1), t(k = t) represents the t-distribution, and the degree of freedom k takes the current iteration number t;

[0189] S1083B: Update the follower:

[0190]

[0191] A + = A T (AA T ) -1

[0192] Wherein, represents the value of the j-th dimension of the sample with the worst fitness in the current population at the t-th iteration, represents the value of the j-th dimension of the sample with the best fitness discovered by the discoverer at the (t + 1)-th iteration, A is a matrix with 1 row and D columns, and each dimension in matrix A is randomly selected from {-1, 1};

[0193] S1083C: During the entire optimization process, there is a 10 - 20% probability that the sample, discoverer, and follower will become scout early warning agents. The update method for the scout early warning agent is:

[0194]

[0195] Wherein, represents the value of the j-th dimension of a certain sample randomly selected from the excellent group X select at the t-th iteration, β is a random number subject to the standard normal distribution, K represents a random number between (-1, 1), P toti , P totw , P totg respectively represent the fitness of the i-th sample, the worst fitness of the population, and the best fitness of the population, ε represents a very small value to avoid the denominator being 0, and in the program, ε = e -10 .

[0196] It should be noted that during the entire optimization process, each sample has a certain probability of becoming a discoverer, a follower, or a scout early warning member. The discoverer is the member with the highest fitness, with the widest search range, and can guide the population to forage. The joiners are divided into two groups according to their fitness levels. The joiners with high fitness move towards the position of the discoverer to pursue higher fitness. The joiners with low fitness, due to being too far away from the discoverer, will search for food on their own. If they find food, they will transform into discoverers. Therefore, the identities of the discoverer and the follower are dynamically changing, but the proportion of the discoverer and the follower in the population remains unchanged. The early warning members are some individuals located at the edge of the population. If danger occurs, they will immediately remind the population to perform anti-predation behaviors and escape the danger.

[0197] S1084: Determine whether the current iteration number has reached the maximum iteration number or whether the optimization parameters meet the convergence accuracy of the parameters to be optimized. If the current iteration number reaches the maximum iteration number or the optimization parameters meet the convergence accuracy of the parameters to be optimized, end the iteration and output the optimization parameters; otherwise, repeat S1082 - S1084.

[0198] It can be understood that before reaching the set maximum iteration number, if the optimization algorithm fails to obtain optimization parameters that meet the convergence accuracy, the optimization algorithm will continuously iterate until optimization parameters that meet the set conditions are obtained.

[0199] Refer to Figure 5 , which shows the schematic diagram of the heat pump rotary wheel dehumidification structure of an intelligent optimization energy-saving control method for a heat pump rotary wheel dehumidification system provided by an embodiment of the present invention.

[0200] As Figure 5 shown, 1 is the regeneration air, 2 is the rotary wheel regeneration area, 3 is the regeneration fan, 4 is the dehumidification fan, 5 is the rotary wheel dehumidification area, 6 is the terminal room, 7 is the evaporator, 8 is the compressor, 9 is the condenser, and 10 is the thermostatic expansion valve.

[0201] S109: Adjust the corresponding equipment of the heat pump rotary wheel dehumidification system according to the optimization parameters.

[0202] Optionally, the regeneration temperature can be adjusted by adjusting the electric heating time of the rotary wheel dehumidification system, the wind speed can be adjusted by adjusting the fan speed, and the rotary speed of the rotary wheel of the heat pump rotary wheel dehumidification system can be adjusted by adjusting the motor output power.

[0203] S110: Repeat S108 - S109.

[0204] It should be noted that during the operation of the heat pump rotary wheel dehumidification system, the environmental conditions change in real time. Therefore, it is necessary to adjust the controllable parameters, namely the regeneration temperature, the wind speed, and the rotary wheel speed, in real time, so as to minimize the total system energy consumption of the heat pump rotary wheel dehumidification system while ensuring that the dehumidification effect always meets the pre-set dehumidification requirements. Therefore, it is necessary to optimize the controllable parameters in real time in combination with the objective function to keep the heat pump rotary wheel dehumidification system running at high efficiency.

[0205] In the embodiment of the present invention, the numerical model of the rotary wheel dehumidification system and the heat pump system model are coupled to establish the interdependent relationship between the rotary wheel dehumidification system and the heat pump system, and the overall optimization of the heat pump rotary wheel dehumidification system is carried out. Combining the total system energy consumption of the heat pump rotary wheel dehumidification system and the moisture content of the outlet air to be treated, the objective function is determined. According to the environmental changes, the parameters to be optimized are optimized in real time in combination with the optimization algorithm to determine the optimal values of the parameters to be optimized, so that the heat pump rotary wheel dehumidification system can meet the two objectives of dehumidification demand and maximum energy efficiency at the same time. It has strong environmental adaptability, can effectively reduce the total system energy consumption while meeting the dehumidification demand, and improve the system operation efficiency.

[0206] The above are only the embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and changes can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. An intelligent optimization energy-saving control method for a heat pump rotary dehumidification system, characterized in that, include: S101: establishing a numerical model of multiple variable parameters of a rotary dehumidification system, and setting initial conditions and boundary conditions of each variable parameter in the numerical model, wherein the variable parameters include: air humidity, air humidity in equilibrium with the adsorbent surface of the rotary dehumidification system, air temperature, air temperature in equilibrium with the surface of the adsorbent, and adsorption capacity of the adsorbent; S102: Establishing a heat pump system model, wherein the heat pump system model includes: a compressor model, a condenser model, a thermal expansion valve model and an evaporator model; S103: coupling the numerical model of the rotary dehumidification system with the heat pump system model to obtain a mathematical model of the heat pump rotary dehumidification system; S104: solving the mathematical model of the heat pump rotary dehumidification system, calculating and outputting the outlet parameters and performance indicators of the heat pump rotary dehumidification system; S105: establishing an energy consumption model of the heat pump rotary dehumidification system according to the regeneration energy consumption and fan energy consumption of the heat pump rotary dehumidification system, and calculating the total system energy consumption of the heat pump rotary dehumidification system; S106: Determine an objective function in combination with the total energy consumption of the system and the outlet moisture content of the processed air, wherein the objective function is used to enable the heat pump rotary dehumidification system to simultaneously meet two objectives: dehumidification demand and maximum energy efficiency; S107: differentiating various variable parameters of the heat pump rotor dehumidification system according to whether they are controllable, and selecting parameters to be optimized that can be directly controlled, wherein the parameters to be optimized include: regeneration temperature, wind speed, and rotor speed; S108: In combination with the objective function, an optimization algorithm is used to optimize the parameters to be optimized to obtain optimized parameters; S109: adjusting corresponding equipment of the heat pump rotary dehumidification system according to the optimization parameters; S110: repeat S108-S109; Wherein, the S106 is specifically: S1061: Determine the objective function in combination with the total energy consumption of the system and the moisture content of the treated air at the outlet J : Among them, represents the energy efficiency ratio, i.e., the system performance coefficient, represents the moisture content of the treated air outlet, in g / kg, obtained by calculating through the numerical model of the rotary dehumidification system, represents the set value of the moisture content of the treated air outlet, in g / kg, represents the set value of the acceptable error, represents the mass flow rate of the treated air, and respectively represent the inlet moisture content and the outlet moisture content of the treated air.

2. The intelligent optimization energy-saving control method for the heat pump rotary wheel dehumidification system according to claim 1, wherein The S104 specifically includes: S1041: Inputting structural parameters of the rotor, the compressor, the condenser and the evaporator, and known parameters, wherein the known parameters include: inlet temperature, inlet moisture content, air volume and rotor speed of dehumidified air and regeneration air; S1042: Preset the regeneration temperature, calculate the moisture content of the processed air outlet according to the numerical model of the rotary dehumidification system, and compare the moisture content of the processed air outlet with the dehumidification requirement; S1043: Calculate the relative error between the processed air outlet moisture content and the processed air outlet moisture content required in the dehumidification requirement. If the relative error is within an acceptable range, proceed to S1044; otherwise, proceed to S1042 to reset the regeneration temperature. S1044: Preset evaporation temperature, condensation temperature and evaporator superheat; S1045: Solve the compressor model according to the inlet state parameters of the compressor to obtain the outlet refrigerant state parameters, power consumption, and outlet refrigerant flow rate of the compressor. Solve the condenser model according to the inlet state parameters and structural parameters of the refrigerant and air in the condenser to obtain the outlet state parameters of the refrigerant and air and the heat transfer amount of the condenser. Calculate the outlet state and refrigerant flow rate of the thermal expansion valve according to the refrigerant inlet parameters; S1046: Compare the refrigerant flow rate calculated through the thermal expansion valve model with the refrigerant flow rate calculated through the compressor model. When the error between the two is greater than the first preset error and the outlet refrigerant flow rate of the thermal expansion valve is greater than the outlet refrigerant flow rate of the compressor, increase the condensation temperature and enter S1044. Otherwise, enter S1047; S1047: Solve the evaporator model according to the inlet state parameters of the refrigerant and air in the evaporator and the structural parameters of the evaporator to obtain the outlet state parameters of the refrigerant and air in the evaporator and the heat transfer amount of the evaporator; S1048: Compare the calculated superheat at the outlet of the evaporator with the preset superheat at the outlet. When the error between the two is greater than the second preset error and the calculated evaporation temperature is greater than the preset evaporation temperature, increase the preset evaporation temperature and enter S1044. When the error between the two is greater than the second preset error and the calculated evaporation temperature is less than or equal to the preset evaporation temperature, decrease the preset evaporation temperature and enter S1044. Otherwise, enter S1049; S1049: Output the outlet parameters and performance indicators of the heat pump rotary wheel dehumidification system according to requirements.

3. The intelligent optimization energy-saving control method for the heat pump runner dehumidification system according to claim 1, characterized in that, The said S108 specifically includes: S1081: Initialize the population; S1082: Select excellent samples in the population as discoverers according to the objective function, and set iteration conditions. Among them, the population includes multiple samples, and the multiple samples are divided into three categories: discoverers, followers, and scouting early warning personnel. The iteration conditions include: the maximum number of iterations and the convergence accuracy of the parameters to be optimized; S1083: Update the discoverers, the followers, and the scouting early warning personnel; S1084: Judge whether the current number of iterations reaches the maximum number of iterations or whether the optimization parameters meet the convergence accuracy of the parameters to be optimized. If the current number of iterations reaches the maximum number of iterations or the optimization parameters meet the convergence accuracy of the parameters to be optimized, end the iteration and output the optimization parameters. Otherwise, repeat S1082 - S1084.

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