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

Abstract

The application discloses a kind of intelligent optimization energy-saving control methods of rotary dehumidification system, belong to the technical field of rotary dehumidification system optimization, method includes: establish the numerical model of multiple change parameters about rotary dehumidification system, set the initial condition and boundary condition about each change parameter in numerical model;According to the energy consumption of regeneration and fan energy consumption of rotary dehumidification system, establish the energy consumption model of rotary dehumidification system, calculate the total energy consumption of rotary dehumidification system;Combined with the total energy consumption and the outlet humidity of processing air, determine objective function;The each change parameter about rotary dehumidification system is distinguished according to whether controllable, select the to-be-optimized parameter that can be directly controlled, wherein, to-be-optimized parameter includes: regeneration temperature, air velocity and rotary speed;Combined with objective function, to-be-optimized parameter is optimized using optimization algorithm, and optimization parameter is obtained;According to optimization parameter, adjust the corresponding equipment of rotary dehumidification system;Repeat optimization, real-time adjustment corresponding equipment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of rotary dehumidification system optimization, and particularly relates to an intelligent optimization energy-saving control method for a rotary dehumidification system. BACKGROUND

[0002] The rotary dehumidification system is gradually becoming the mainstream in the field of air dehumidification due to its compact structure, high dehumidification efficiency, and continuous dehumidification and regeneration process. Energy-saving modification of the rotary dehumidification system is of great significance to reducing system energy consumption. The energy-saving modification of the rotary dehumidification system mainly includes optimizing the operating parameter setting of the rotary dehumidification system and optimizing the structure of the rotary dehumidification system.

[0003] Currently, fixed parameter control is more commonly used in the operating parameter setting of the rotary dehumidification system. The rotary speed, fan frequency and regeneration temperature set value are fixed during system operation. However, changes in environmental temperature and humidity conditions will cause the outlet working condition of the rotary dehumidification system to change with the change in environmental temperature and humidity, making it difficult to maintain at the set target value. In addition, the rotary dehumidification system currently has the problem of high energy consumption in the regeneration process. The control of the traditional rotary dehumidification system usually adopts a constant regeneration temperature mode, which will cause waste of regeneration energy in some working conditions. SUMMARY

[0004] The purpose of the embodiment of the present application is to provide an intelligent optimization energy-saving control method for a rotary dehumidification system, which can solve the technical problems that the existing rotary dehumidification system does not consider the influence of changes in environmental temperature and humidity, sets fixed rotary speed, fan frequency and regeneration temperature for operation, makes it difficult to maintain the dehumidification effect of the rotary dehumidification system at the set target value, and constant regeneration temperature causes waste of regeneration energy in some working conditions.

[0005] In order to solve the above technical problems, the present application is implemented as follows:

[0006] The embodiment of the present application provides an intelligent optimization energy-saving control method for a rotary dehumidification system, comprising:

[0007] S101: A numerical model of a plurality of change parameters of the rotary dehumidification system is established, and initial conditions and boundary conditions of each change parameter in the numerical model are set, wherein the change parameters include air moisture content, air moisture content in equilibrium with the surface of the adsorbent of the rotary dehumidification system, air temperature, air temperature in equilibrium with the surface of the adsorbent, and adsorption capacity of the adsorbent;

[0008] S102: An energy consumption model of the rotary dehumidification system is established according to the regeneration energy consumption and fan energy consumption of the rotary dehumidification system, and the total system energy consumption of the rotary dehumidification system is calculated;

[0009] S103: Determine a target function based on the total energy consumption of the system and the outlet humidity of the treated air, wherein the target function is used to reduce the error of the total energy consumption of the system and the outlet humidity of the treated air as much as possible;

[0010] S104: Differentiate the various changeable parameters of the rotary dehumidification system according to whether they are controllable, and select the to-be-optimized parameters that can be directly controlled, wherein the to-be-optimized parameters include the regeneration temperature, the air speed and the rotary speed of the rotary wheel;

[0011] S105: Optimize the to-be-optimized parameters by using an optimization algorithm in combination with the target function, to obtain the optimized parameters;

[0012] S106: Adjust the corresponding equipment of the rotary dehumidification system according to the optimized parameters;

[0013] S107: Repeat S105-S106.

[0014] In the embodiment of the present application, the total energy consumption of the system is calculated by establishing a numerical model of the rotary dehumidification system and an energy consumption model of the rotary dehumidification system, the target function based on the total energy consumption of the system and the outlet humidity of the treated air is determined, the controllable variables of the rotary dehumidification system, i.e., the regeneration temperature, the air speed and the rotary speed of the rotary wheel, are selected as the to-be-optimized parameters, the to-be-optimized parameters are optimized by using an optimization algorithm in combination with the target function, the best setting values of the controllable variables are found as the optimized parameters, the equipment parameters of the rotary dehumidification system are adjusted in real time according to the optimized parameters under various working conditions of environmental changes, the influence of environmental changes on the rotary dehumidification system is reduced, the stability and reliability of the rotary dehumidification system are improved, the dehumidification effect of the rotary dehumidification system is ensured, the total energy consumption of the system is reduced, and energy waste is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of a rotary dehumidification system intelligent optimization energy-saving control method provided by the embodiment of the present application.

[0016] Figure 2 is a schematic diagram of a dehumidification rotary wheel of a rotary dehumidification system intelligent optimization energy-saving control method provided by the embodiment of the present application.

[0017] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0019] The following will describe the intelligent optimization energy-saving control method of the rotary dehumidification system provided by the embodiments of the present application in detail by specific embodiments and application scenarios.

[0020] Referring to Figure 1 , a flowchart of an intelligent optimization energy-saving control method of a rotary dehumidification system provided by an embodiment of the present application is shown.

[0021] Referring to Figure 2 , a schematic diagram of a dehumidification wheel of an intelligent optimization energy-saving control method of a rotary dehumidification system provided by an embodiment of the present application is shown.

[0022] As shown in Figure 2 , the dehumidification wheel in the rotary dehumidification system is divided into a dehumidification zone and a regeneration zone. When working, the process air passes through the dehumidification zone, and the dehumidification wheel dehumidifies the process air according to a pre-set dehumidification target. The process air passing through the dehumidification zone can reach a pre-set air humidity index. In the process of continuous rotation of the dehumidification wheel, the dehumidification zone wheel that has processed humid air passes through the regeneration zone. The regeneration zone side continuously blows out a certain temperature of regeneration air that has been heated to regenerate the dehumidification wheel passing through the regeneration zone, so that the dehumidification wheel passing through the regeneration zone regains the adsorption capacity. At this time, the dehumidification wheel turns to the dehumidification zone to dehumidify the process air, and the cycle is repeated to complete the dehumidification work.

[0023] The intelligent optimization energy-saving control method of the rotary dehumidification system provided by the embodiments of the present application comprises:

[0024] S101: establishing a numerical model of a plurality of change parameters of the rotary dehumidification system, and setting initial conditions and boundary conditions of each change parameter in the numerical model, wherein the change parameters include: air moisture content, air moisture content in equilibrium with the adsorbent surface of the rotary dehumidification system, air temperature, air temperature in equilibrium with the adsorbent surface, and adsorption capacity of the adsorbent.

[0025] It should be noted that by establishing the numerical model of the rotary dehumidification system, the controllable and uncontrollable plurality of change parameters are added to the model, which is equivalent to unifying the parameters that are related to each other and have an influence on the rotary dehumidification system, facilitating monitoring and adjusting various influencing factors and providing a theoretical basis for subsequent adjustment of the rotary dehumidification system.

[0026] In a possible implementation, S101 specifically includes:

[0027] S1011: Establish a numerical model of a plurality of changing parameters of the rotary dehumidification system as follows:

[0028] Introduce the airflow side mass balance equation, the airflow side heat balance equation, the adsorption side mass differential equation and the adsorption side heat differential equation:

[0029] The airflow side mass balance equation is:

[0030] The airflow side heat balance equation is:

[0031] The adsorption side mass differential equation is:

[0032] The adsorption side heat differential equation is:

[0033] Wherein, Y a represents the air moisture content, Y d represents the air moisture content 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 amount 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 air channel circumference of the rotary dehumidification system, unit m, A represents the air channel cross-sectional area 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 of air, water vapor, liquid water and adsorbent material at constant pressure, unit J / (kg*K), q st represents the adsorption heat, unit J / kg;

[0034] According to the curve of the air channel being a sinusoidal curve type, the circumference equation A and the flow area equation P of the air channel of the rotary dehumidification system are established:

[0035] A = 2ab

[0036]

[0037] wherein 2a and 2b represent the height and width of the air channel, respectively;

[0038] In combination with the air moisture content, the air moisture content in equilibrium with the adsorbent surface and the air temperature in equilibrium with the adsorbent surface, the water vapor saturation pressure equation, the moisture content and relative humidity equation and the relationship equation between the material equilibrium relative humidity and the adsorbent adsorption amount are established as the connection equation between the three:

[0039] Water vapor saturation pressure equation:

[0040]

[0041] wherein p s represents the water vapor saturation vapor pressure;

[0042] Moisture content and relative humidity equation:

[0043]

[0044] φ represents the air relative humidity, and p represents the environmental atmospheric pressure;

[0045] The relationship equation between the air relative humidity and the adsorbent adsorption amount is obtained by fitting the adsorption isotherm of the adsorption material:

[0046]

[0047] W max represents the saturated adsorption amount of the adsorption material, and C represents the constant of the experimental data fitting.

[0048] S1012: setting the initial conditions and boundary conditions of the numerical model with respect to each change parameter:

[0049] The initial conditions are:

[0050]

[0051] The boundary conditions of the numerical model for the dehumidification zone and for the regeneration zone are respectively:

[0052] For the dehumidification zone:

[0053]

[0054] For the regeneration zone:

[0055]

[0056] It can be understood that the air humidity and air temperature of the air passing through the dehumidification zone and the regeneration zone of the rotary dehumidification system are different during the operation of the rotary dehumidification system, and the processing set values of the dehumidification zone and the regeneration zone are also different, so the boundary conditions of the air humidity and the air temperature in the numerical model are distinguished and set, so that the numerical model established is closer to the actual situation.

[0057] S102: According to the regeneration energy consumption and the fan energy consumption of the rotary dehumidification system, an energy consumption model of the rotary dehumidification system is established, and the total energy consumption of the rotary dehumidification system is calculated.

[0058] It should be noted that the main energy consumption of the rotary dehumidification system is the regeneration energy consumption and the fan energy consumption, and the energy consumption model of the rotary dehumidification system is established by integrating the regeneration energy consumption and the fan energy consumption, which is equivalent to analyzing multiple factors in one model, laying a foundation for subsequent adjustment of the rotary dehumidification system, and achieving a more optimal operating efficiency.

[0059] In one possible implementation, S102 specifically includes:

[0060] S1021: Calculate the regeneration energy consumption E reg :

[0061] E reg =c pg ρ a A reg u reg (T a,reg -T a,ad )

[0062] Wherein, A reg represents the regeneration area in the rotary dehumidification system, unit m 2 , u reg represents the regeneration air speed, unit m / s, T a，re g represents the regeneration temperature, unit K, T a,ad represents the ambient temperature, unit K.

[0063] S1022: Calculate the processing fan energy consumption E fan,ad and the regeneration fan energy consumption E fan,reg :

[0064]

[0065]

[0066] Wherein, P fan represents the fan total pressure, unit Pa, G ad represents the processing air volume, unit m 3 / s, G reg represents the regeneration air volume, unit m 3K represents a motor capacity reserve coefficient of the rotary dehumidification system, and η represents an efficiency value.

[0067] It should be noted that the energy consumption of the rotary dehumidification system mainly includes the regeneration energy consumption and the fan energy consumption. The regeneration energy consumption is that the air heated through the regeneration zone needs to regenerate the dehumidified rotary wheel, that is, the heated regeneration air continuously evaporates the moisture in the dehumidification rotary wheel, so that the adsorbent of the dehumidification rotary wheel has enhanced adsorption capacity. However, in this process, the heating of the regeneration air and the acceleration of the regeneration fan for the regeneration air need to consume energy. If the regeneration energy consumption is set as a fixed value, with the change of the environmental temperature, the regeneration capacity of the regeneration zone cannot meet the predetermined requirement, which may also cause that the regeneration zone does not need a too high regeneration air temperature or a regeneration air speed to meet the regeneration capacity of the regeneration zone. If the operation is still performed according to the pre-set regeneration parameters at this time, a large amount of energy will be wasted, and the enterprise benefit will be reduced.

[0068] S1023: An energy consumption model of the rotary dehumidification system is established in combination with the regeneration energy consumption and the fan energy consumption.

[0069] E total = E reg +E fan,ad +E fan,reg .

[0070] S1024: The total system energy consumption of the rotary dehumidification system is calculated through the energy consumption model of the rotary dehumidification system.

[0071] S103: A target function is determined in combination with the total system energy consumption and the outlet humidity of the process air, wherein the target function is used to reduce the error between the total system energy consumption and the outlet humidity of the process air as much as possible.

[0072] It should be noted that the target function is determined to combine the total system energy consumption and the outlet humidity of the process air into a function, and then the target function is analyzed to seek a balance between the error of the total system energy consumption and the outlet humidity of the process air, so as to ensure that the outlet humidity of the process air meets the predetermined requirement and the total system energy consumption is kept at a relatively low state, thereby improving the resource utilization rate.

[0073] In a possible implementation, S103 specifically includes the following steps.

[0074] S1031: A target function J is determined in combination with the total system energy consumption and the outlet humidity of the process air.

[0075] J = λE total +(1-λ)(Y a,out -Y a,ref ) 2 , λ ∈ [0, 1]

[0076] wherein, λ represents a weight factor, E total represents the total energy consumption of the system, unit: kW, Y a,out represents the outlet humidity of the treated air, unit: g / kg, Y a,out obtained by numerical model calculation, Y a,ref represents the outlet humidity set value of the treated air, unit: g / kg.

[0077] S104A: select the uncontrollable parameters in the plurality of changeable parameters of the rotary dehumidification system, and collect the uncontrollable parameters as the collection parameters, wherein the uncontrollable variables include: ambient temperature, ambient humidity, rotary thickness and rotary material.

[0078] It can be understood that the rotary dehumidification system will be affected by a plurality of factors during operation, including parameters that can be adjusted by humans and parameters that cannot be adjusted by humans. For uncontrollable factors in the rotary dehumidification system, including but not limited to ambient temperature, ambient humidity, rotary thickness and rotary material, for real-time changing parameters, real-time collection can be performed, and for fixed parameters that do not change, the parameters can be obtained by checking the calibration parameters of the dehumidification rotary or the parameters provided by the manufacturer. These are called uncontrollable parameters.

[0079] S104: distinguish the changeable parameters of the rotary dehumidification system according to whether they are controllable, and select the to-be-optimized parameters that can be directly controlled, wherein the to-be-optimized parameters include: regeneration temperature, air speed and rotary speed.

[0080] In actual application, some parameters of the rotary dehumidification system can be controlled by the system itself according to environmental changes, wherein the regeneration temperature can control the heating time or the heating temperature for adjustment. Such controlled parameters are controllable parameters. Controllable parameters are selected, each controllable parameter has an effect on the operation of the rotary dehumidification system, and the operation efficiency of the rotary dehumidification system is adjusted by adjusting the controllable parameters, so as to reduce unnecessary energy consumption as much as possible by adjusting the controllable parameters.

[0081] S105: combining the target function, the optimization algorithm is used to optimize the to-be-optimized parameters to obtain the optimized parameters.

[0082] It should be noted that the target function includes two targets of the total energy consumption of the system and the error of the outlet humidity of the treated air. The purpose of the optimization algorithm is to find the best values of the three parameters by continuously adjusting the three to-be-optimized parameters, so that the two targets of the total energy consumption of the system and the error of the outlet humidity of the treated air can achieve the best effect, that is, while ensuring the outlet humidity of the treated air, the total energy consumption of the system is maximally reduced.

[0083] In a possible implementation, S105 specifically includes:

[0084] S1051: initialize the population.

[0085] In a possible implementation, S1051 specifically includes:

[0086] S1051A: divide 10-20% of the samples in the population as discoverers, and divide the remaining samples as followers.

[0087] S1051B: according to the parameters to be optimized, set the solution space of the optimization algorithm as 3D, and determine the position of the i-th sample based on the three parameters to be optimized as:

[0088] X i =(X i,1 ,X i,2 ,X i,3 )=(T a,reg,i ,u a,i ,r i ),i∈[1,n]

[0089] wherein n represents the number of samples, and i represents the i-th sample.

[0090] S1052: select excellent samples in the population as discoverers according to the objective function, and set iteration conditions, wherein the population includes a plurality of samples, the plurality of samples are divided into three types of discoverers, followers and reconnaissance warners, and the iteration conditions include: maximum iteration number and convergence accuracy of the parameters to be optimized.

[0091] It should be noted that the optimization algorithm needs to set iteration conditions to determine whether the optimization algorithm has reached the purpose and whether the optimization needs to be ended. In the process of optimization, reaching the maximum iteration number may be caused by various reasons. In the process of continuous iteration, if the optimization of the parameters to be optimized meets the initial set iteration condition, the iteration can be terminated in advance, and the optimized parameters are also obtained.

[0092] In a possible implementation, S1052 specifically includes:

[0093] S1052A: select excellent groups in the population as discoverers according to the objective function:

[0094] X select ={X s}if Prob s >R1,s∈[1,n]

[0095]

[0096]

[0097] wherein P tot (X i ) represents the value of the objective function corresponding to the i-th sample, Prob i represents the probability of the i-th sample being selected into the excellent group, R1 represents a random number obeying uniform distribution between (0, 1).

[0098] S1053: updating the discoverer, the follower and the scout.

[0099] It should be noted that in the whole optimization process, each sample has a certain probability to become the discoverer, the follower or the scout. The discoverer is the member with the highest fitness, and its search range is the widest, which can guide the population to forage. The joiners are divided into two groups according to their fitness. The joiner with high fitness moves to the position of the discoverer to pursue higher fitness, and the joiner with low fitness will find food by itself because it is too far away from the discoverer. If the joiner with low fitness finds food, it will become a discoverer. Therefore, the identities of the discoverer and the follower are dynamically changed, but the proportion of the discoverer and the follower in the population is unchanged. The scout is part of the individuals located at the edge of the population. If danger occurs, the scout will immediately remind the population to perform anti-predation behavior to escape danger.

[0100] In one possible implementation, S1053 specifically includes:

[0101] S1053A: updating the discoverer:

[0102]

[0103] wherein, represents the value of the j-th dimension of the i-th sample at the t-th iteration, R1 and R2 represent random numbers obeying uniform distribution between (0, 1), iter max represents the maximum number of iterations, ST represents a random number between (0.5, 1), t (k = t) represents t distribution, and the degree of freedom k takes the current iteration number t.

[0104] S1053B: updating the follower:

[0105]

[0106] A + = A T (AA T ) -1

[0107] 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, and Ajt+1is the value of the jth dimension of the sample found by the discoverer at the (t+1)th iteration, A is a 1 by D matrix, each dimension in matrix A is randomly selected from {-1, 1}.

[0108] S1053C: During the whole optimization process, the sample, the discoverer and the follower have a probability of 10-20% to become the scout, and the update method of the scout is:

[0109]

[0110] wherein, Ajtis the value of the jth dimension of a randomly selected sample from the excellent population X select at the tth iteration, β is a random number obeying the standard normal distribution, K is a random number between (-1, 1), P toti ,P totw ,P totg respectively represent the fitness of the ith sample, the worst fitness of the population and the best fitness of the population, ε represents a minimum value to avoid the denominator being zero, and e -10 is taken as ε in the program.

[0111] S1054: When the current iteration number reaches the maximum iteration number or the convergence precision of the to-be-optimized parameter is met, the iteration is ended, and the optimized parameter is output.

[0112] S1055: When the current iteration number does not reach the maximum iteration number and the convergence precision of the to-be-optimized parameter is not met, S1052-S1054 are repeated.

[0113] It can be understood that, before the maximum iteration number is reached, if the optimization algorithm fails to obtain the optimized parameter meeting the convergence precision, the optimization algorithm will continuously iterate until the optimized parameter meeting the set condition is obtained.

[0114] S106: Adjusting the corresponding equipment of the rotary dehumidification system according to the optimized parameter.

[0115] It should be noted that the optimized parameter is obtained under the condition that the two predetermined requirements of the total energy consumption of the system and the outlet humidity of the treated air in the objective function are met, and after the rotary dehumidification system is adjusted according to the optimized parameter, the humidity of the treated air can meet the actual requirements, and in this case, the total energy consumption of the system is relatively lowest. The optimized parameter is a controllable parameter, and only the corresponding equipment needs to be controlled to adjust the selected three controllable variables to the value of the optimized parameter.

[0116] Optionally, the regeneration temperature is adjusted by adjusting the electric heating time of the rotary dehumidification system, the air speed is adjusted by adjusting the fan speed, and the rotary speed of the rotary dehumidification system is adjusted by adjusting the motor output power.

[0117] S107: repeating S105-S106.

[0118] It should be noted that during the operation of the rotary dehumidification system, the environmental conditions change accordingly every moment, and accordingly, the to-be-optimized parameters need to be adjusted in real time, so that under the condition that the environmental conditions and other uncontrollable parameters change continuously, the total energy consumption of the system is reduced to the greatest extent by adjusting the controllable parameters in real time, and the rotary dehumidification system can complete the dehumidification task under the premise that the rotary dehumidification system can complete the dehumidification task, so the whole optimization process needs to be cycled in real time to adjust the corresponding equipment to cope with the influence of environmental changes.

[0119] In the embodiment of the present application, the total energy consumption of the system is calculated by establishing a numerical model of the rotary dehumidification system and an energy consumption model of the rotary dehumidification system, a target function based on the total energy consumption of the system and the outlet humidity of the treated air is determined, the controllable variables of the rotary dehumidification system, i.e., the regeneration temperature, the air speed and the rotary speed, are selected as the to-be-optimized parameters, the to-be-optimized parameters are optimized by combining the target function and the optimization algorithm, and the best setting value of the controllable variables is found as the optimized parameter. Under various working conditions of environmental changes, the equipment parameters of the rotary dehumidification system are adjusted in real time according to the optimized parameter, the influence of environmental changes on the rotary dehumidification system is reduced, the stability and reliability of the rotary dehumidification system are improved, the dehumidification effect of the rotary dehumidification system is ensured, the total energy consumption of the system is reduced, and energy waste is reduced.

[0120] The above only describes the embodiments of the present application and is not used to limit the present application. The present application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for intelligent optimization and energy-saving control of a rotary dehumidification system, characterized in that, include: S101: Establish a numerical model for multiple varying parameters of the rotary dehumidification system, and set initial conditions and boundary conditions for each of the varying parameters in the numerical model. The varying parameters include: air humidity, air humidity in equilibrium with the surface of the adsorbent in the rotary dehumidification system, air temperature, air temperature in equilibrium with the surface of the adsorbent, and the adsorption capacity of the adsorbent. S102: Based on the regeneration energy consumption and fan energy consumption of the rotary dehumidification system, establish the energy consumption model of the rotary dehumidification system and calculate the total system energy consumption of the rotary dehumidification system; S103: Combine the total energy consumption of the system and the moisture content at the outlet of the processed air to determine an objective function, wherein the objective function is used to minimize the error between the total energy consumption of the system and the moisture content at the outlet of the processed air; S104: The varying parameters of the rotary dehumidification system are distinguished according to whether they are controllable, and the parameters to be optimized that can be directly controlled are selected. The parameters to be optimized include: regeneration temperature, wind speed and rotary speed. S105: Combining the objective function, an optimization algorithm is used to optimize the parameters to be optimized, thereby obtaining the optimized parameters; S106: Adjust the corresponding equipment of the rotary dehumidification system according to the optimized parameters; S107: Repeat S105-S106; Specifically, S102 includes: S1021: Calculate the regeneration energy consumption E reg : ; where c pg ρ represents the specific heat capacity of air at constant pressure. a A represents the density of air. reg This represents the regeneration area in the rotary dehumidification system, in meters (m²). 2 u reg Represents the regenerated air velocity, in m / s and T. a，re g represents the regeneration temperature, in K or T. a,ad Indicates ambient temperature, in Kelvin (K). S1022: Calculate the processing fan energy consumption in the aforementioned fan energy consumption. Energy consumption of regenerated air fans : ； Among them, P fan Indicates the total pressure of the fan, in units of G ad Indicates the volume of air processed, in units of G reg Indicates regenerated air volume, unit K represents the motor capacity reserve coefficient of the rotary dehumidification system, and η represents the efficiency value. S1023: Combining the regeneration energy consumption and the fan energy consumption, establish an energy consumption model for the rotary dehumidification system: ； S1024: Calculate the total energy consumption of the rotary dehumidification system using the energy consumption model of the rotary dehumidification system; Specifically, S103 is: S1031: Based on the total energy consumption of the system and the moisture content of the treated air outlet, determine the objective function J: , Where λ represents the weighting factor, E total This represents the total energy consumption of the system, in kW. This indicates the moisture content of the treated air outlet, in g / kg. Obtained through calculation using the numerical model. This indicates the set value of the outlet moisture content of the processed air, in g / kg.

2. The intelligent optimization and energy-saving control method for the rotary dehumidification system according to claim 1, characterized in that, S101 specifically includes: S1011: The following numerical models are established for multiple varying parameters of the rotary dehumidifier system: Introducing the airflow-side mass balance equation, airflow-side heat balance equation, adsorption-side mass differential equation, and adsorption-side heat differential equation: The mass balance equation on the airflow side is: ; The heat balance equation on the airflow side is: ; The adsorption-side mass differential equation is: ; The differential equation for heat on the adsorption side is: ; Among them, Y a Indicates the air humidity content, Y d The moisture content, T, of the air in equilibrium with the adsorbent surface. a Indicates the air temperature, T d The air temperature at which the adsorbent is in equilibrium with the surface of the adsorbent is represented by W, and the amount of adsorption by the adsorbent is represented by W. Indicates air velocity, unit h m Indicates the mass transfer coefficient, in units of h represents the heat transfer coefficient, in units of... P represents the circumference of the air passage in the rotary dehumidification system, in units of... A represents the cross-sectional area of ​​the air passage in the rotary dehumidification system, in units of... , and These represent the densities of air and the adsorbent material, respectively, in units of... , , , and The specific heat capacities at constant pressure for air, water vapor, liquid water, and the adsorbent material are respectively expressed in units of... , This represents the heat of adsorption, expressed in J / kg. Based on the fact that the air passage curve is a sine curve, the equations for the perimeter A and flow area P of the air passage in the rotary dehumidification system are established: ； ； Wherein, 2a and 2b represent the height and width of the air passage, respectively; Combining the air humidity, the air humidity in equilibrium with the adsorbent surface, and the air temperature in equilibrium with the adsorbent surface, we establish the water vapor saturation pressure equation, the humidity-relative humidity equation, and the relationship between the material equilibrium relative humidity and the adsorbent adsorption capacity as the equations relating these three factors: The equation for the saturated pressure of water vapor is: ； Where, p s This represents the saturated vapor pressure of water vapor; The equation relating moisture content and relative humidity is as follows: ； ρ represents relative humidity, and p represents ambient atmospheric pressure. The relationship between the relative humidity of the air and the adsorption capacity of the adsorbent was obtained by fitting the adsorption isotherm of the adsorbent material: ； W max The saturated adsorption capacity of the adsorbent material is represented by C, and C represents a constant for fitting the experimental data. S1012: Set the initial conditions and boundary conditions for each of the changing parameters in the numerical model.

3. The intelligent optimization and energy-saving control method for the rotary dehumidification system according to claim 1, characterized in that, The following is included before S104: S104A: Select the uncontrollable parameters from the multiple variable parameters of the rotary dehumidification system, and collect the uncontrollable parameters as acquisition parameters. The uncontrollable parameters include: ambient temperature, ambient humidity, rotary wheel thickness, and rotary wheel material.

4. The intelligent optimization and energy-saving control method for the rotary dehumidification system according to claim 1, characterized in that, Specifically, S105 includes: S1051: Initialize the population; S1052: Select the best sample in the population as the discoverer according to the objective function, and set the iteration conditions. The population includes multiple samples, which are divided into three categories: discoverer, follower and scout. The iteration conditions include: the maximum number of iterations and the convergence accuracy of the parameter to be optimized. S1053: Update the discoverer, the follower, and the reconnaissance and early warning agent; S1054: If the current iteration count reaches the maximum iteration count or the convergence accuracy of the parameters to be optimized is met, the iteration ends and the optimized parameters are output.

5. The intelligent optimization and energy-saving control method for the rotary dehumidification system according to claim 4, characterized in that, S1051 specifically includes: S1051A: Classify 10-20% of the samples in the population as discoverers and the remaining samples as followers; S1051B: Based on the parameters to be optimized, the solution space of the optimization algorithm is defined as 3-dimensional. The position of the i-th sample is determined based on the three parameters to be optimized as follows: ； Where n represents the number of samples, and i represents the i-th sample.

6. The intelligent optimization and energy-saving control method for the rotary dehumidification system according to claim 4, characterized in that, Following S1054, the following is also included: S1055: If the current iteration count does not reach the maximum iteration count and the convergence accuracy of the parameters to be optimized is not met, repeat S1052-S1054.

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