Condensing heat recovery air conditioner and feedforward and fuzzy compound constant temperature regulation method thereof

Abstract

The application discloses a condensing heat recovery air conditioner and a feedforward and fuzzy compound constant temperature regulation method thereof, and belongs to the technical field of air conditioning. The outlet of the compressor of the condensing heat recovery air conditioner is connected with a reheater and a condenser respectively; the reheater and the condenser are connected with an evaporator through an electronic expansion valve and a thermal expansion valve respectively; the evaporator is connected with the compressor; the electronic expansion valve is electrically connected with a temperature control module; and the temperature control module is electrically connected with an inlet air temperature sensor and an outlet air temperature sensor in an air conditioner air duct. The application has the beneficial effects that the optimization capacity of a genetic algorithm is utilized to realize efficient and automatic optimization solution of high-dimensional parameters; the introduction of feedforward control and fuzzy control effectively solves the problem of the dramatic fluctuation of outlet air temperature of the traditional fixed parameter PID control when facing the great change of set temperature and external strong disturbance, strengthens the real-time operation performance of the constant temperature air conditioner under complex conditions, and effectively improves the control quality of the supply air temperature.

Description

Technical Field

[0001] This invention relates to a condensing heat recovery air conditioner and its feedforward and fuzzy composite constant temperature control method, belonging to the field of air conditioning technology. Background Technology

[0002] In high-end manufacturing and cutting-edge technology fields, many critical processes have stringent requirements for the stability of the ambient temperature. Constant temperature air conditioning systems, which continuously provide controlled areas with a constant and uniformly distributed airflow, are core equipment for ensuring stable and reliable ambient temperature. With the rapid development of my country's electronics and information, biomedicine, precision instruments, data centers, aerospace, and precision manufacturing industries, the demand for constant temperature air conditioning is increasing daily. Against this backdrop, developing constant temperature air conditioning systems and their control methods with high precision, high efficiency, high stability, low maintenance costs, and low adverse effects is of great significance for promoting industrial upgrading and achieving low-carbon development.

[0003] The refrigeration process of a conventional air conditioning unit essentially utilizes the heat absorption effect of the evaporation phase change of the low-temperature, low-pressure refrigerant in the indoor evaporator. Heat from the space being cooled is first transferred to the refrigerant, which then releases heat through compression and condensation, transferring the heat to the outside. Directly dissipating the condensation heat generated during the air conditioning cycle causes two problems: firstly, it causes thermal pollution to the surrounding environment; secondly, it leads to a significant waste of low-grade heat energy. To ensure high precision and stability of the supply air temperature, a constant-temperature air conditioning system further refines the cooling and dehumidifying airflow exiting the evaporator through reheating (a process known as reheating). In engineering practice, a common reheating method involves installing an auxiliary electric heater in the air supply duct of the constant-temperature air conditioning system. This, combined with a silicon controlled rectifier (SCR) or solid-state relay to regulate the heat flux density of the heater, allows for precise control of the outlet air temperature. However, this undoubtedly increases the overall energy consumption of the constant-temperature air conditioning system significantly. Constant temperature air conditioning systems need to run continuously year-round, resulting in continuous condensation heat emissions and a huge cumulative energy consumption for electric heating, which is obviously uneconomical.

[0004] Developing a constant-temperature air conditioning technology with condensation heat recovery is an effective solution. This technology involves adding a condensation heat recovery unit (also called a reheater) within the air duct of the constant-temperature air conditioning system. The recovered condensation heat replaces the electric heater to reheat the treated air. The reheater and condenser are connected in parallel, with their inlets connected to the compressor outlet. Specifically, the compressor exhaust port connects to two parallel refrigerant working circuits. One branch's refrigerant flows to the reheater, while the other branch's flows to the condenser. Based on the real-time operating requirements of the constant-temperature air conditioning system, and using a refrigerant flow distribution and adjustment device, the flow rate of the high-temperature, high-pressure refrigerant to these two branches can be dynamically allocated according to the control system's regulation methods, thereby achieving precise control of the condensation heat recovery. The condensed refrigerant in each branch passes through its respective expansion valve, where it is throttled, cooled, and depressurized. The two streams then merge into a single main stream flowing to the evaporator. In the evaporator, it absorbs heat from the treated air, achieving cooling and dehumidification. The gaseous refrigerant flowing out of the evaporator is in a low-pressure superheated state. After being drawn into the compressor, it is compressed into a high-temperature, high-pressure gas and discharged from the compressor. This cycle continues.

[0005] With the introduction of a condensing heat recovery branch, the dynamic characteristics of the air conditioning system become more complex, requiring a more reliable control system. Currently, most air conditioning systems use Proportional-Integral-Derivative (PID) control to regulate the outlet air temperature in real time. PID controllers have a simple structure, a certain degree of robustness and adaptability, and can exhibit acceptable control quality under normal operating conditions by considering multiple factors. However, when the air conditioning system faces drastic changes in operating conditions, strong load disturbances, and extremely high control accuracy requirements, traditional PID control strategies often fail to achieve the control effects required by the user.

[0006] When a system encounters sudden load changes or operating mode switching, traditional PID control strategies often struggle to quickly and effectively balance dynamic response speed and system stability, frequently exhibiting problems such as excessive overshoot, prolonged settling time, or continuous oscillation. Specifically, when temperature deviations are large, an insufficient proportional gain setting will cause sluggish system response, while insufficient integral action will lead to steady-state error, and a large derivative action will produce significant fluctuations. Furthermore, its fixed parameters are ill-suited to handle strong disturbances such as large changes in ambient temperature or drastic fluctuations in inlet conditions, resulting in weak anti-interference capabilities and affecting the final temperature control quality. Therefore, for condensing heat recovery air conditioning systems, there is an urgent need to develop a constant temperature control method that is highly adaptable, precise, stable, and reliable under complex operating conditions. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this invention is to provide a condensing heat recovery air conditioner and its feedforward and fuzzy composite constant temperature control method.

[0008] The technical solution of the present invention includes: a condensing heat recovery air conditioner, comprising an evaporator, a reheater, and a fan located in an air conditioning duct, wherein the compressor outlet is connected to the reheater and the condenser respectively, the reheater and the condenser are connected to the evaporator through an electronic expansion valve and a thermal expansion valve respectively, the evaporator is connected to the compressor, the electronic expansion valve is electrically connected to a temperature control module, and the temperature control module is electrically connected to an inlet air temperature sensor and an outlet air temperature sensor in the air conditioning duct;

[0009] The temperature control module includes a feedforward control unit, a PID control unit, a fuzzy control unit, and a genetic algorithm optimization unit. The genetic algorithm optimization unit interacts with the fuzzy control unit to optimize the parameters of the fuzzy control unit. The feedforward control unit is electrically connected to the inlet air temperature sensor and the electronic expansion valve, respectively. The PID control unit is electrically connected to the inlet air temperature sensor, the outlet air temperature sensor, and the fuzzy control unit, respectively. The fuzzy control unit is electrically connected to the PID control unit and the genetic algorithm optimization unit.

[0010] The compressor is a positive displacement compressor, including piston, rotary, scroll, and screw types. The compressor's discharge port connects to two parallel refrigerant working circuits. One branch of the refrigerant flows to the reheater, while the other branch flows to the condenser. The condensed refrigerant in each branch passes through its respective expansion valve, where it is throttled, cooled, and depressurized. The two streams then merge into a single main stream flowing to the evaporator. In the evaporator, it absorbs heat from the air being processed again, achieving cooling and dehumidification. The gaseous refrigerant exiting the evaporator is in a low-pressure superheated state. After being drawn into the compressor, it is compressed into a high-temperature, high-pressure gas and discharged from the compressor. This cycle continues. The refrigerant type conforms to GB / T 7778 standard and includes hydrocarbons, hydrofluorocarbons (HFCs), hydrofluoroolefins (HFOs), hydrochlorofluoroolefins (HCFOs), hydrofluoroethers (HFEs), and other natural or synthetic refrigerants and their mixtures.

[0011] The condenser can be a finned tube, tank, shell-and-tube, plate, microchannel, or shell-and-tube heat exchanger. The reheater can be, but is not limited to, finned tube, wire tube, plate-fin, or tube-strip structures. The evaporator is of the same type as the reheater and is located within the air supply duct of the constant temperature air conditioner. Driven by a fan, the air being processed first passes through the evaporator for cooling and dehumidification, then flows through the reheater to be reheated to the preset target air supply temperature before being blown out. During the operation of the air conditioning system, the inlet air temperature and outlet air supply temperature are monitored in real time by temperature sensors. Based on the inlet air temperature conditions and the outlet air temperature setting requirements, the temperature control method described in this invention is used to dynamically adjust the opening of the electronic expansion valve in real time, dynamically distributing the refrigerant mass flow rate between the reheater branch and the condenser branch to accurately recover the required reheat heat, thereby achieving precise control of the air supply temperature.

[0012] This invention also claims a feedforward and fuzzy composite constant temperature control method for a condensing heat recovery air conditioner, which is based on the aforementioned condensing heat recovery air conditioner and specifically includes the following steps:

[0013] Step 1: Based on the actual operation of the condensing heat recovery air conditioner, collect the inlet air temperature t under different operating conditions using inlet air temperature sensors and outlet air temperature sensors. in With outlet air temperature t out Data is collected and simultaneously matched with the electronic expansion valve opening data; based on the collected data, an inlet air temperature t is established. in Air outlet temperature t out The mathematical model relating to the opening degree of the electronic expansion valve serves as the core model of the feedforward controller, the general form of which is:

[0014] (1)

[0015] In the formula, EEV For the opening degree of the electronic expansion valve, α Here, f() represents the feedforward compensation coefficient, and f() represents the inlet air temperature t. in Air outlet temperature t out and feedforward compensation coefficient α The rule mapping to the expansion valve opening EEV, i.e., based on the parameter t in t out , α A function to calculate EEV;

[0016] Step 2: Based on the air outlet characteristics of the condensing heat recovery air conditioner, the fuzzy controller adopts a two-input, three-output structure, using temperature deviation... e and the rate of change of temperature deviation ec As the input variable of the fuzzy controller; the output variable is defined as the proportional coefficient in the PID controller.K p Integral Time T I and differential time T D The change in is expressed as Δ K p Δ T I and Δ T D ;

[0017] Step 3: Set the physical and fuzzy universes of the input and output variables respectively; use quantization factors. K e and K ec Establish the mapping relationship between input variables from the physical domain to the fuzzy domain, using a scaling factor. , and To achieve the inverse mapping of output variables from the fuzzy domain to the physical domain;

[0018] Step 4: Define fuzzy subsets of input and output variables, and determine the corresponding membership function for each subset;

[0019] Step 5: Based on the adjustment requirements of PID parameters at different operating stages, and combined with the dynamic characteristics of the constant temperature air conditioning system in actual operation, a system for real-time adjustment of the proportional gain is constructed. K p Integral Time T I and differential time T D A fuzzy control rule table; the rules are in the form of "if-Then";

[0020] Step 6: Use fuzzy inference to obtain the fuzzy set of output variables, and then use defuzzification method to defuzzify the obtained fuzzy set, transforming it into a specific value that can be directly applied to the PID controller, i.e., the output of the control system.

[0021] Step 7: Use a genetic algorithm to perform offline optimization of the parameters to be optimized in the control system;

[0022] Step 8: Assign the parameter values ​​optimized by the genetic algorithm to the fuzzy controller;

[0023] Step 9: Based on the requirements for outlet air temperature control, the output value of the control system is converted into a pulse signal via an analog current signal to drive the stepper motor of the electronic expansion valve to control the valve opening, thereby precisely regulating the refrigerant flow into the reheater to achieve on-demand quantitative reheating of the supply air and ultimately achieving precise control of the outlet air temperature.

[0024] The membership functions in step four include triangular membership functions, Gaussian membership functions, trapezoidal membership functions, or combinations thereof.

[0025] Step six employs the Mamdani method for fuzzy inference, followed by the centroid method for defuzzification calculation, to obtain specific values ​​that can be directly applied to the PID controller. The output of the control system is:

[0026] (2)

[0027] (3)

[0028] In equation (2), e ( t (Set temperature) t set With air outlet temperature t out The difference, in equation (3), K p0 , T I0 and T D0 They are respectively K p , T I and T D The initial value of .

[0029] Genetic algorithms are used to process the above. K p0 , T I0 , T D0 , K e , K ec , , and A total of 8 parameters were optimized offline. The genetic algorithm described is an optimization algorithm that simulates natural selection and genetic mechanisms. It solves for the optimal or near-optimal solution by simulating selection, crossover, and mutation operations in the biological evolution process. The specific method is as follows:

[0030] Generate initial size of N population P By selecting an appropriate encoding method, the chromosomes of each individual in the population are determined by... D The genome composition is as follows:

[0031] (4)

[0032] (5)

[0033] In the formula C i Chromosomes x ij Genes in chromosomes;

[0034] The fitness function is used to evaluate the quality of each individual. This fitness function is constructed based on the performance indicators of the control system, and its general form is:

[0035] (6)

[0036] In equation (6), f () indicates that based on chromosomes C i The rules for calculating the fitness of a genetic algorithm are based on the input parameters. C i Mapping to the fitness of the genetic algorithm F i ;

[0037] Based on an individual's fitness value, select superior individuals from the current population to pass on to the next generation;

[0038] The crossover algorithm selects individuals based on their crossover probability. p c Perform a selection operation to produce new offspring individuals; use a selection mutation algorithm with mutation probabilities. p m Perform mutation operations on all genes in the population to maintain population diversity; repeat selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness requirement is met, and output the optimized parameters.

[0039] With the goal of optimizing the performance of the control system, the fitness function shown below is used to evaluate the performance of each individual:

[0040] (7)

[0041] In equation (7), t For time; e ( t (Set temperature) t set With air outlet temperature t out The difference; A constant value is added to prevent the denominator from being zero;

[0042] The roulette wheel method is used to select superior individuals from the current population to pass on to the next generation. The roulette wheel process is as follows:

[0043] Calculate the probability of selection p i :

[0044] (8)

[0045] In the formula, F i For the first i Fitness of each chromosome;

[0046] Calculate cumulative probability q n :

[0047] (9)

[0048] generate N A random number between [0, 1] r k ( k = 1,2,..., N For each random number r k Find satisfaction q n-1 < r k ≤ q n Individuals are selected as the chosen individuals;

[0049] The crossover method employs a simulated binary crossover algorithm to generate new offspring individuals; first, individuals in the population are randomly paired; then, for each paired parent... P 1j and P 2j ( j =1,2,…, D According to crossover probability p c Perform a crossover operation; the offspring produced after the crossover. c 1j , c 2j for:

[0050] (10)

[0051] in, β For the crossover operator, the following formula is used for calculation:

[0052] (11)

[0053] In the formula, To simulate the distribution index of binary crossover, uj A random number between [0, 1];

[0054] The mutation method employs a multinomial mutation algorithm, which modifies the genes of all individuals in the population. x ij According to mutation probability p m Perform mutation operations to maintain population diversity; the mutated genes for:

[0055] (12)

[0056] In the formula, U j , L j Let the maximum and minimum values ​​of the parameter to be optimized be denoted as . δ ij For mutation operators, the following formula is used for calculation:

[0057] (13)

[0058] in, The distribution index of the multinomial variation. v ij A random number between [0, 1];

[0059] Repeat the selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness requirement is met, and output the optimized parameters.

[0060] The beneficial effects of this invention include:

[0061] (1) The introduction of the feedforward controller effectively solves the problem of severe fluctuations in outlet air temperature when faced with large changes in set temperature and strong external disturbances in traditional fixed-parameter PID control, effectively improving the control quality of supply air temperature. (2) The introduction of fuzzy control solves the problem that fixed PID parameters are difficult to adapt to complex and variable working conditions, ensuring the real-time operation performance of the constant temperature air conditioning system under various conditions. (3) The optimization capability of genetic algorithm is used to realize efficient and automated optimization solution of high-dimensional parameters. (4) Based on the precise refrigerant flow control capability, under the premise of ensuring stable and accurate outlet air temperature of the air conditioning system, the condensation heat is maximized to replace traditional electric heating, effectively reducing system energy consumption and electricity costs, and improving the overall energy efficiency level and carbon emission reduction effect. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the structure of a condensing heat recovery air conditioning system.

[0063] Figure 2This is a schematic diagram of the temperature control logic of a condensing heat recovery air conditioning system based on feedforward and fuzzy composite control and genetic algorithm optimization.

[0064] Figure 3 Input variables e and ec The membership function graph;

[0065] Figure 4 For the output variable Δ K p Δ T I and Δ T D The membership function graph;

[0066] Figure 5 This is a flowchart of a genetic algorithm;

[0067] Figure 6 Comparison of experimental results for outlet air temperature under the condition of sinusoidal fluctuation of inlet air with outlet air temperature set at 22℃;

[0068] Figure 7 Comparison chart of statistical results of outlet air temperature under the condition of sinusoidal fluctuation of inlet air temperature set at 22℃;

[0069] Figure 8 A comparison chart of experimental results when the outlet air temperature is increased from 21℃ to 22℃.

[0070] In the diagram: 1. Compressor; 2. Outlet air temperature sensor; 3. Reheater; 4. Evaporator; 5. Electronic expansion valve; 6. Thermal expansion valve; 7. Condenser; 8. Fan; 9. Inlet air temperature sensor; 10. Air conditioning duct. Detailed Implementation

[0071] To clearly illustrate the objectives and technical solutions of this invention, the following detailed description, in conjunction with specific embodiments and accompanying drawings, further clarifies the invention. It should be understood that the specific embodiments described herein are merely illustrative and do not constitute a limitation on the scope of protection of this invention.

[0072] Example 1

[0073] In this embodiment, the rated cooling capacity of the condensing heat recovery air conditioner is 2750 W, as shown in the attached figure. Figure 1 As shown, the system mainly includes: compressor 1, outlet air temperature sensor 2, reheater 3, evaporator 4, electronic expansion valve 5, thermal expansion valve 6, condenser 7, fan 8, inlet air temperature sensor 9, and air conditioning duct 10.

[0074] In this embodiment, compressor 1 is a vertical scroll compressor with an adjustable gas delivery range of 0.9~9.3 cm. 3 / rev, its outlet connects to two parallel branches. Branch one connects to reheater 3, forming a condensation heat recovery branch; branch two connects to condenser 7, forming a conventional condensation branch. The reheater 3 is a finned tube heat exchanger, and the heat exchange method is: refrigerant flows inside the tubes, and air flows outside the tubes and between the fins (rated air volume 1440 m³ / s). 3 The refrigerant is a ternary mixture composed of R32 / R125 / R1234yf, with a mass ratio of 0.68 / 0.02 / 0.30. The condenser 7 is a tank-type heat exchanger, with heat exchange in the form of cooling water flowing inside the tubes (rated water temperature 20℃, flow rate 16 L / min) and refrigerant flowing in the shell side. The reheater 3 and evaporator 4 (external dimensions 460 mm × 100 mm × 380 mm) are placed in the air conditioning duct 10. Driven by the fan 8, the air to be processed first flows through the evaporator 4 for cooling and dehumidification, and then flows through the reheater 3 for reheating to the set target outlet air temperature.

[0075] The high-temperature, high-pressure refrigerant discharged from compressor 1 is divided into two streams, which enter the heat recovery branch containing reheater 3 and the condensation branch containing condenser 7, respectively. The flow distribution of the two refrigerant streams is based on real-time data collected by inlet air temperature sensor 9 and outlet air temperature sensor 2, and is precisely regulated by electronic expansion valve 5 (stepper motor, maximum adjustable step count of 480 steps) based on the control method described in this invention. After releasing condensation heat, the two refrigerant streams are throttled and depressurized by electronic expansion valve 5 and thermostatic expansion valve 6, respectively, before merging and entering evaporator 4. In evaporator 4, the refrigerant absorbs heat from the air being processed, transforming into superheated vapor, which is then drawn into compressor 1 and compressed again to a high-temperature, high-pressure state. This completes the entire refrigerant working cycle.

[0076] In this embodiment, the inlet air temperature is unstable, exhibiting a sinusoidal fluctuation with a central value of 24℃, a fluctuation period of 30 minutes, and an amplitude of 1℃. Under these inlet air conditions, the outlet air temperature of the condensing heat recovery air conditioner is set to 22℃±0.08℃.

[0077] The proposed optimization control method based on feedforward and fuzzy composite control combined with genetic algorithm is suitable for applications with... Figure 1 The control principle and effect of the outlet air temperature of the condensing heat recovery air conditioning system are as follows:

[0078] The working logic of the optimization control method based on feedforward and fuzzy composite control combined with genetic algorithm is shown in the appendix. Figure 2 As shown, it includes 9 key steps, which are detailed in this embodiment as follows:

[0079] Step 1: Collect inlet and outlet air temperature data under different operating conditions using temperature sensors, and simultaneously record the opening data of the electronic expansion valve. Based on the collected data, establish a mathematical model relating the inlet and outlet air temperatures to the electronic expansion valve opening as the core model of the feedforward controller. The feedforward controller used in this embodiment is as follows: feedforward compensation coefficient... α =1:

[0080] (14)

[0081] Step two: Establish a fuzzy controller with a two-input, three-output structure, based on temperature deviation. e and the rate of change of temperature deviation ec As the input variable of the fuzzy controller; the output variable is defined as the proportional coefficient in the PID controller. K p Integral Time T I and differential time T D The change in is expressed as Δ K p Δ T I and Δ T D .

[0082] Step 3, adjust the temperature deviation e The physical domain is set as e =[-0.6, 0.6] (unit: °C), rate of temperature change ec The physical domain is set as ec =[-3, 3] (unit: ℃ / min). The fuzzy universe of discourse of the input variables is uniformly discretized into seven levels: {-3, -2, -1, 0, 1, 2, 3}; the output variable Δ K p Δ T I and Δ T D The physical domains are set to [-5, 5], [-0.5, 0.5] (unit: min) and [-0.03, 0.03] (unit: min), respectively. The fuzzy domains of the three output variables are all defined with seven levels: {-3, -2, -1, 0, 1, 2, 3}.

[0083] Step 4: Set the fuzzy subsets of both the input and output variables to {NB(negative large, -3), NM(negative medium, -2), NS(negative small, -1), ZO(zero, 0), PS(positive small, 1), PM(positive medium, 2), PB(positive large, 3)}. The membership functions for all input and output variables in the fuzzy controller are a mixture of ZMF, SMF, and triangular membership functions. The membership functions for the input and output variables are shown in the attached table. Figure 3 and attached Figure 4 As shown.

[0084] Step 5: Based on the adjustment requirements of PID parameters at different operating stages of the air conditioning system, a system for real-time adjustment of the proportional gain is constructed. K p Integral Time T I and differential time T D The fuzzy control rule table is shown in Table 1:

[0085] Table 1 Fuzzy Rule Table

[0086]

[0087] Step 6: Use the Mamdani method for fuzzy inference and the centroid method for defuzzification calculation. Based on equations (2) and (3), obtain the specific values ​​that can be directly applied to the PID controller.

[0088] Step 7, according to the appendix Figure 5 The genetic algorithm flow shown is for... K p0 , T I0 , T D0 , K e , K ec , , and A total of 8 parameters were optimized. Specific implementation details are as follows:

[0089] The main initialization parameters of the genetic algorithm are shown in Table 2. As shown in equations (4) to (6), the encoding method uses real number encoding, and each chromosome is a vector composed of 8 parameters to be optimized. The fitness function is used to evaluate the quality of each individual. Within the parameter search range defined in Table 2, a random number of elements of size 1 are generated. N The initial population was 50.

[0090] Table 2 Genetic Algorithm Initialization Parameters

[0091]

[0092] With the goal of optimizing the performance of the control system, the fitness function shown in (7) is used in this example to evaluate the merits of each individual.

[0093] This example uses the roulette wheel method to select superior individuals from the current population to pass on to the next generation. The roulette wheel process is as follows:

[0094] Based on equation (8), the selection probability is calculated. p i :

[0095] Based on equation (9), calculate the cumulative probability. q n :

[0096] generate N A random number between [0, 1] r k ( k = 1,2,..., N For each random number r k Find satisfaction q n-1 < r k ≤ q n Individuals are selected as individuals.

[0097] In this embodiment, the crossover method employs a simulated binary crossover algorithm to generate new offspring individuals. First, individuals in the population are randomly paired. Then, for each paired parent... P 1j and P 2j ( j =1,2,…, D According to crossover probability p c When a crossover operation is performed, the offspring produced after the crossover... c 1j , c 2j The calculation is shown in equation (10).

[0098] In this embodiment, the mutation method employs a multinomial mutation algorithm, which modifies the genes of all individuals in the population. x ij According to mutation probability p m Mutation operations are performed to maintain population diversity. The mutated gene... and mutation operators δ ij Calculate according to formulas (12) and (13) respectively;

[0099] Repeat the selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness requirement is met, and output the optimized parameters.

[0100] Step eight: Output the parameter values ​​optimized by the genetic algorithm to the fuzzy controller. Figure 2 The principle of temperature control logic for condensing heat recovery air conditioning based on a feedforward fuzzy control method optimized by genetic algorithm is shown.

[0101] Step nine: Based on the outlet air temperature control requirements, the analog current signal (4~20 mA) output by the PID controller is processed by a dedicated pulse conversion module to generate a corresponding pulse sequence signal. The stepper motor driving the electronic expansion valve precisely adjusts the opening of the electronic expansion valve, realizing dynamic control of the refrigerant flow into the reheater. Based on the above steps, the system can accurately reheat the supply airflow through the evaporator according to real-time temperature control requirements, ultimately ensuring that the outlet air temperature remains stable within the set target range.

[0102] The control method based on feedforward and fuzzy composite control combined with genetic algorithm optimization described in this invention exhibits excellent performance in terms of anti-interference and control accuracy when applied to a condensing heat recovery air conditioning system. Feedforward control can compensate for measurable disturbances such as inlet air temperature in advance, while fuzzy control endows the system with the ability to handle uncertainties and nonlinear factors. Furthermore, the genetic algorithm performs global optimization of the composite controller parameters, effectively achieving a synergistic improvement in system performance. (See attached diagram) Figure 6 and 7 The comparative test results show that, under the condition of sinusoidal fluctuation in the inlet air temperature described in Example 1, the new control method proposed in this invention can make the outlet air temperature distribution of the condensing heat recovery air conditioner more concentrated and significantly reduce fluctuations. Specifically, 92% of the data points fall within the set temperature ±0.04℃ range, compared to only 72% in traditional PID control, representing a 27% improvement. Furthermore, after implementing the new control method described in this invention, the standard deviation of the constant temperature air conditioner's outlet air temperature decreased from 0.0352℃ in conventional PID control to 0.0267℃, a reduction of 24%; the maximum temperature deviation also decreased from 0.08℃ to 0.06℃, a reduction of 25%. The above comparative test results demonstrate that the control method based on feedforward and fuzzy composite control combined with genetic algorithm optimization is applicable to condensing heat recovery air conditioning systems, exhibiting good anti-interference capabilities and steady-state control accuracy.

[0103] Example 2

[0104] In this embodiment, the control method based on feedforward and fuzzy composite control combined with genetic algorithm optimization used in the condensing heat recovery air conditioning system, as well as the constant temperature air conditioning system, are consistent with those described in Embodiment 1, and will not be repeated here. The difference between Embodiment 2 and Embodiment 1 lies in the different measured operating conditions. In Embodiment 2, the inlet air temperature parameters of the condensing heat recovery air conditioning system do not exhibit sinusoidal fluctuations, but are maintained at a dry-bulb temperature of 24°C and a wet-bulb temperature of 15°C. Furthermore, the target outlet air temperature of the constant temperature air conditioning system is required to be increased from 21°C to 22°C. The measured results are shown in the attached figure. Figure 8 As shown.

[0105] In Example 2, compared with the conventional PID control method, the condensing heat recovery air conditioner and its feedforward and fuzzy composite constant temperature control method proposed in this invention show significant advantages in the dynamic performance of controlling the outlet air temperature of the condensing heat recovery air conditioner system. From the perspective of overshoot, under the new control method described in this invention, the overshoot of the constant temperature air conditioner outlet air temperature is reduced from 0.17℃ in conventional PID control to 0.13℃, a reduction of 23%. From the perspective of settling time, the new control method described in this invention requires 219 s to set, compared to 291 s in conventional PID control, a reduction of 24%. After reaching steady state, the average steady-state error of the new control method described in this invention is 0.008℃, a reduction of 50% compared to 0.016℃ in conventional PID control. This comparative experimental result shows that when faced with changes in the outlet air temperature setpoint, the control method based on feedforward and fuzzy composite control and genetic algorithm optimization can achieve the new outlet air temperature setpoint requirements with a faster and smoother adjustment process.

[0106] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A feedforward and fuzzy composite constant temperature control method for condensing heat recovery air conditioning, characterized in that, Based on a condensing heat recovery air conditioner, the condensing heat recovery air conditioner includes an evaporator, a reheater, and a fan located in the air conditioning duct. The compressor outlet is connected to the reheater and the condenser respectively. The reheater and the condenser are connected to the evaporator through an electronic expansion valve and a thermal expansion valve respectively. The evaporator is connected to the compressor. The electronic expansion valve is electrically connected to a temperature control module. The temperature control module is electrically connected to an inlet air temperature sensor and an outlet air temperature sensor in the air conditioning duct. The temperature control module includes a feedforward control unit, a PID control unit, a fuzzy control unit, and a genetic algorithm optimization unit. The genetic algorithm optimization unit interacts with the fuzzy control unit to optimize the parameters of the fuzzy control unit. The feedforward control unit is electrically connected to the inlet air temperature sensor and the electronic expansion valve, respectively. The PID control unit is electrically connected to the inlet air temperature sensor, the outlet air temperature sensor, and the fuzzy control unit, respectively. The fuzzy control unit is electrically connected to the PID control unit and the genetic algorithm optimization unit. The constant temperature control method specifically includes the following steps: Step 1: Based on the actual operation of the condensing heat recovery air conditioner, collect the inlet air temperature t under different operating conditions using inlet air temperature sensors and outlet air temperature sensors. in With outlet air temperature t out Data is collected and simultaneously matched with the electronic expansion valve opening data; based on the collected data, an inlet air temperature t is established. in Air outlet temperature t out The mathematical model relating to the opening degree of the electronic expansion valve serves as the core model of the feedforward controller, the general form of which is: （1） In the formula, EEV For the opening degree of the electronic expansion valve, α Here, f is the feedforward compensation coefficient, and f() is the inlet air temperature t. in Air outlet temperature t out and feedforward compensation coefficient α The rule mapping to the expansion valve opening EEV, i.e., based on the parameter t in t out , α A function to calculate EEV; Step 2: Based on the air outlet characteristics of the condensing heat recovery air conditioner, the fuzzy controller adopts a two-input, three-output structure, using temperature deviation... e and the rate of change of temperature deviation ec As an input variable for the fuzzy controller; The output variable is defined as the proportional coefficient in the PID controller. K p Integral Time T I and differential time T D The change in is expressed as Δ K p Δ T I and Δ T D ; Step 3: Set the physical and fuzzy universes of the input and output variables respectively; use quantization factors. K e and K ec Establish the mapping relationship between input variables from the physical domain to the fuzzy domain, using a scaling factor. , and To achieve the inverse mapping of output variables from the fuzzy domain to the physical domain; Step 4: Define fuzzy subsets of input and output variables, and determine the corresponding membership function for each subset; Step 5: Based on the adjustment requirements of PID parameters at different operating stages, and combined with the dynamic characteristics of the constant temperature air conditioning system in actual operation, a system for real-time adjustment of the proportional gain is constructed. K p Integral Time T I and differential time T D A fuzzy control rule table; the rules are in the form of "if-Then"; Step 6: Use fuzzy inference to obtain the fuzzy set of output variables, and then use defuzzification method to defuzzify the obtained fuzzy set, transforming it into a specific value that can be directly applied to the PID controller, i.e., the output of the control system. Step 7: Use a genetic algorithm to perform offline optimization of the parameters to be optimized in the control system; Step 8: Assign the parameter values ​​optimized by the genetic algorithm to the fuzzy controller; Step 9: Based on the requirements for outlet air temperature control, the output value of the control system is converted into a pulse signal via an analog current signal to drive the stepper motor of the electronic expansion valve to control the valve opening, thereby precisely regulating the refrigerant flow into the reheater to achieve on-demand quantitative reheating of the supply air and ultimately achieving precise control of the outlet air temperature.

2. The constant temperature control method according to claim 1, characterized in that, The membership functions in step four include triangular membership functions, Gaussian membership functions, trapezoidal membership functions, or combinations thereof.

3. The constant temperature control method according to claim 1, characterized in that, Step six employs the Mamdani method for fuzzy inference, followed by the centroid method for defuzzification calculation, to obtain specific values ​​that can be directly applied to the PID controller. The output of the control system is: (2) (3) In equation (2), e ( t (Set temperature) t set With air outlet temperature t out The difference, in equation (3), K p0 , T I0 and T D0 They are respectively K p , T I and T D The initial value of .

4. The constant temperature control method according to claim 3, characterized in that, Genetic algorithms are used to process the above. K p0 , T I0 , T D0 , K e , K ec , , and A total of 8 parameters were optimized offline. The genetic algorithm described is an optimization algorithm that simulates natural selection and genetic mechanisms. It solves for the optimal or near-optimal solution by simulating selection, crossover, and mutation operations in the biological evolution process. The specific method is as follows: Generate initial size of N population P By selecting an appropriate encoding method, the chromosomes of each individual in the population are determined by... D The genome composition is as follows: (4) (5) In the formula C i Chromosomes x ij Genes in chromosomes; The fitness function is used to evaluate the quality of each individual. This fitness function is constructed based on the performance indicators of the control system, and its general form is: (6) In equation (6), f ( ) indicates that based on chromosomes C i The rules for calculating the fitness of a genetic algorithm are based on the input parameters. C i Mapping to the fitness of the genetic algorithm F i ; Based on an individual's fitness value, select superior individuals from the current population to pass on to the next generation; The crossover algorithm selects individuals based on their crossover probability. p c Perform a selection operation to produce new offspring individuals; select a mutation algorithm based on the mutation probability. p m Perform mutation operations on all genes in the population to maintain population diversity; repeat selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness requirement is met, and output the optimized parameters.

5. The constant temperature control method according to claim 4, characterized in that, With the goal of optimizing the performance of the control system, the fitness function shown below is used to evaluate the performance of each individual: （7） In equation (7), t For time; e ( t (Set temperature) t set With air outlet temperature t out The difference; A constant value is added to prevent the denominator from being zero; The roulette wheel method is used to select superior individuals from the current population to pass on to the next generation. The roulette wheel process is as follows: Calculate the probability of selection p i : (8) In equation (8), F i For the first i Fitness of each chromosome; Calculate cumulative probability q n : (9) generate N A random number between [0, 1] r k ( k = 1,2,..., N For each random number r k Find satisfaction q n-1 < r k ≤ q n Individuals are selected as the chosen individuals; The crossover method employs a simulated binary crossover algorithm to generate new offspring individuals; first, individuals in the population are randomly paired; then, for each paired parent... P 1j and P 2j ( j =1,2,…, D According to crossover probability p c Perform a crossover operation; the offspring produced after the crossover. c 1j , c 2j for: (10) in, β For the crossover operator, the following formula is used for calculation: (11) In the formula, To simulate the distribution index of binary crossover, u j A random number between [0, 1]; The mutation method employs a multinomial mutation algorithm, which modifies the genes of all individuals in the population. x ij According to mutation probability p m Perform mutation operations to maintain population diversity; the mutated genes for: (12) In the formula, U j , L j Let the maximum and minimum values ​​of the parameter to be optimized be denoted as . δ ij For mutation operators, the following formula is used for calculation: (13) in, The distribution index of the multinomial variation. v ij A random number between [0, 1]; Repeat the selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness requirement is met, and output the optimized parameters.

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