Refrigerating system optimization control method based on virtual cooling capacity

Abstract

The invention belongs to the technical field of energy-saving control of refrigerating systems, and particularly relates to a refrigerating system optimization control method based on virtual cooling capacity. The method comprises the steps that on-site historical data of the refrigerating system are collected and preprocessed; component mechanism models are established based on the composition of the refrigeration system, the component mechanism models comprise an evaporator mechanism model, a compressor mechanism model, a condenser mechanism model and a throttle valve model, all the component mechanism models are connected in series based on the law of conservation of mass, and undetermined coefficients are calculated through a genetic algorithm; in combination with a refrigeration system model and a particle swarm optimization algorithm, optimal operation control parameters under given virtual cooling capacity and weather parameters are calculated, so that the energy consumption of the refrigeration system is minimum; the optimal operation control parameters are issued to the equipment controller; data are collected again to calculate system model errors, and when the errors are too large, the steps are repeated. The problem that the refrigeration system optimization control method cannot be implemented under the cooling capacity data missing scene is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy-saving control of refrigeration systems, and in particular relates to a refrigeration system optimization control method based on virtual cooling capacity. Background Art

[0002] In recent years, the cold chain logistics industry has experienced rapid development, the market scale has continued to expand, the construction of national backbone cold chain logistics bases and cold chain facilities in production and sales areas has been steady, and the technical level of cold chain equipment has also been significantly improved. However, with the clear proposal of the "dual carbon" goal, the cold chain logistics industry is also facing the urgent task of energy conservation and emission reduction. This requirement has prompted the design of cold chain logistics cold storage to keep pace with the times and conform to the development trend of green and low carbon.

[0003] In this context, digital twin technology has attracted widespread attention due to its ability to significantly improve energy efficiency, reduce energy consumption and emissions. By introducing the digital twin model, accurate simulation and prediction of the cold storage operation status can be achieved, and then optimized control can be achieved to achieve the goal of energy saving and emission reduction.

[0004] However, most current refrigeration systems are not equipped with flow measurement devices in actual applications, which leads to a key problem: the lack of a key parameter for energy-saving control - refrigerant mass flow. During operation, the optimization control system needs to accurately calculate the refrigerant mass flow rate, based on which to evaluate the working status of the compressor and determine the heat exchange between the evaporator and the condenser. Therefore, how to accurately calculate the refrigeration capacity and then build an effective digital twin model for the cold storage to achieve optimization control and energy-saving goals has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above-mentioned problems in the prior art, the present invention provides a refrigeration system optimization control method based on virtual cooling capacity, which solves the problem that the refrigeration system optimization control method cannot be implemented in scenarios such as cold storage where cooling capacity data is missing.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] The present invention provides a refrigeration system optimization control method based on virtual cooling capacity, comprising the following steps:

[0008] Step 100: collecting on-site historical data of the refrigeration system and preprocessing the collected on-site historical data, wherein the on-site historical data includes weather parameters and operating parameters;

[0009] Step 200: Establish component mechanism models based on the composition of the refrigeration system, wherein the component mechanism models include an evaporator mechanism model, a compressor mechanism model, a condenser mechanism model, and a throttle valve model. All component mechanism models are connected in series based on the law of conservation of mass to form a refrigeration system model; obtain the correct compressor inlet refrigerant mass flow rate through iterative calculation, and calculate the compressor power; compare the calculated compressor power with the theoretical power, and optimize the coefficient to be determined;

[0010] Step 300: Combining the refrigeration system model and the particle swarm optimization algorithm, the optimal operation control parameters under given virtual cooling capacity and weather parameters are calculated to minimize the energy consumption of the refrigeration system;

[0011] Step 400: Execute step 100 again, use the newly generated training samples to calculate the model error in the refrigeration system mechanism model, and when the refrigeration system model error is greater than a preset threshold, repeat the above steps 200 to 400.

[0012] Furthermore, the compressor mechanism model is a semi-empirical thermodynamic model, and its calculation process includes an intake throttling calculation process, a leakage adiabatic mixing calculation process, an intake constant-pressure heating calculation process, a multi-party compression process, an exhaust constant-pressure cooling calculation process and an exhaust throttling calculation process; through iterative calculation, the unknown coefficients and mass flow rate inside the compressor are solved.

[0013] Furthermore, the condenser mechanism model includes: dividing the condenser heat exchange process into multiple micro-elements, and the heat exchange of each micro-element is calculated by heat exchange coefficient and refrigerant temperature; and calculating the refrigerant enthalpy value at the condenser outlet through the condenser heat exchange.

[0014] Furthermore, the throttle valve mechanism model: the inlet and outlet refrigerant enthalpy values ​​before and after the throttle valve are equal, the inlet refrigerant enthalpy value before the throttle valve is the outlet refrigerant enthalpy value of the condenser, and the outlet refrigerant enthalpy value after the throttle valve is the inlet refrigerant enthalpy value of the evaporator.

[0015] Furthermore, the evaporator mechanism model: divides the evaporator into multiple micro-elements, calculates the heat exchange of each micro-element through the corresponding heat exchange coefficient and temperature; and calculates the refrigerant enthalpy at the evaporator outlet through the evaporator heat exchange.

[0016] Furthermore, the correct mass flow rate of the refrigerant at the compressor inlet is obtained through iterative calculation of the refrigerant enthalpy at the evaporator outlet and the compressor inlet. Combined with the compressor model, the actual power of the compressor is calculated and compared with the theoretical power, and the unknown coefficients are optimized through the genetic algorithm.

[0017] Furthermore, the suction throttling calculation process is simplified to an isentropic nozzle process:

[0018]

[0019] In the above formula, h1 represents the refrigerant specific enthalpy after suction throttling; h0 represents the compressor inlet specific enthalpy; represents the gas delivery mass flow rate of the compressor; d1 represents the refrigerant density after suction throttling; A su Indicates the suction throttling area;

[0020] The leakage adiabatic mixing calculation process is a mixing process of the leakage mass flow rate and the suction mass. The specific enthalpy of the mixed refrigerant is calculated as follows:

[0021]

[0022] In the above formula, Indicates the compressor leakage mass flow rate; Indicates the actual refrigerant mass flow rate entering the rotor compression chamber; h leak,in represents the specific enthalpy of the refrigerant at the entrance of the compressor leakage channel; h2 represents the specific enthalpy of the refrigerant after adiabatic mixing;

[0023] The calculation process of the suction constant pressure heating is as follows: the suction gas will be heated by the casing, the refrigerant temperature will increase, and the enthalpy value will increase. Through assumptions, the energy conservation law is finally used to iterate and obtain:

[0024]

[0025] In the above formula, Q 23 Indicates the intake air preheating heat transfer; AU suc Indicates the suction heat transfer coefficient, t w represents the average temperature of the casing; t2 represents the refrigerant temperature on the suction side; c p2 It represents the specific heat capacity of the refrigerant at constant pressure on the suction side; h3 represents the specific enthalpy of the refrigerant after suction preheating;

[0026] The compression process is divided into a multi-party compression process and a forced exhaust process:

[0027]

[0028] p4=p3·BVR α ;

[0029] In the above formula, BVR represents the compressor volume ratio; α represents the polynomial index; v4 represents the refrigerant specific volume after the internal compression ends; v3 represents the refrigerant specific volume before the internal compression begins; p4 represents the end pressure of the internal compression; p3 represents the refrigerant pressure after suction throttling;

[0030] The calculation process of exhaust constant pressure cooling and exhaust throttling is similar to the suction process. The leakage of the twin-screw compressor is evaluated by the virtual path connecting the suction and exhaust:

[0031]

[0032] In the above formula, Indicates the leakage of the compressor; d leak Indicates the refrigerant density at the entrance of the leakage channel; A leak Indicates the leakage area of ​​the compressor; h leak,in Represents the specific enthalpy of the refrigerant at the entrance of the leakage channel; h leak,out It represents the specific enthalpy of refrigerant at the outlet of the leakage channel;

[0033] Actual gas transmission mass flow rate:

[0034]

[0035] The model calculates the input power, mass flow rate and refrigerant outlet temperature through the compressor based on the operating conditions of the compressor suction and exhaust sides:

[0036]

[0037] Where t7 represents the refrigerant outlet temperature; f(n, load, p evp ,t evp ,p con ) represents the input parameter group of the compressor model; n represents the compressor speed, load represents the load position; p evp Indicates the evaporation pressure on the suction side, t evp Indicates the evaporation temperature on the suction side, p con Indicates the condensing pressure on the exhaust side.

[0038] Furthermore, the condenser mechanism model divides the heat exchange process into N sections, and the heat exchange capacity of each section is Q con for:

[0039]

[0040] Among them, K con,i represents the average heat transfer coefficient of each microelement, t con,i Represents the average temperature of the refrigerant in each microelement, t sr,i Indicates the average wind temperature of each microelement;

[0041] The enthalpy value h3 of the refrigerant at the condenser outlet is calculated by heat exchange, where h2 represents the enthalpy value of the refrigerant at the compressor outlet, calculated by the compressor model:

[0042]

[0043] In the above formula, Indicates the refrigerant mass flow rate of the compressor.

[0044] Furthermore, the evaporator mechanism model divides the heat exchange process into N sections, and the heat exchange capacity of each section is Q evp for:

[0045]

[0046] Among them, K evp,i Indicates the average heat transfer coefficient of each microelement, t evp,i Represents the average temperature of the refrigerant in each microelement, t sr,i represents the average temperature of cold water in each microelement; d represents the diameter of the heat exchange tube; l represents the length of the heat exchange tube;

[0047] Calculate the evaporator outlet enthalpy h by using the evaporator heat exchange 1' :

[0048]

[0049] In the above formula, Indicates the mass flow rate of refrigerant delivered by the compressor.

[0050] Furthermore, the correct compressor inlet refrigerant mass flow rate is iterated by calculating the evaporator outlet refrigerant enthalpy and the compressor inlet refrigerant enthalpy. Calculate the compressor power using the compressor model:

[0051]

[0052] P loss,1 =a tl,1 ·P i ;

[0053] P loss,2 =a tl,2 μV sw ·ω 2 ;

[0054] P loss,3 =a tl,3 ·m loss ·Δh loss ;

[0055] P com =P i +P loss,1 +P loss,2 +P loss,3 ;

[0056] Among them, P i Indicates the compression power in the compressor; P loss,1 Indicates the torque loss during the operation of the compressor; P loss,2 Indicates the viscous loss during the operation of the compressor; P loss,3Indicates the additional power loss during partial load operation; P com Indicates the total input power of the compressor; w i represents the internal compression work, calculated by the multi-party compression process; a tl,1 Represents the torque friction loss coefficient; a tl,2 represents the viscous friction loss coefficient, μ represents the viscosity of the lubricating oil, and ω represents the compressor speed; a tl,3 Indicates the partial load loss factor, m loss Indicates the refrigerant mass flow rate that is bypassed during partial load operation; Δh loss Indicates the theoretical enthalpy loss of bypassed refrigerant during partial load operation; calculates the difference with the actual power, and determines the coefficients to be tested through genetic algorithms and field data, including the leakage area A leak , inlet and outlet throttling area, inlet and outlet heat transfer coefficient, loss coefficient a tl,1 、a tl,2 、a tl,3 , M is the data sample size:

[0057]

[0058] Among them, err represents the difference between the compressor power and the actual power, P com,i Indicates the calculated power of the compressor, P real,i Indicates actual power.

[0059] Furthermore, in step 300, by using the particle swarm optimization algorithm, by giving the evaporation pressure p evp , compressor inlet temperature t0, refrigerant inlet temperature t sp By adjusting the compressor speed n and load, the condensing temperature t con The minimum power consumption P of the system is:

[0060] [P,n,load,t con ]=f(p evp ,t0,t sp ).

[0061] Compared with the prior art, the present invention has the following technical effects:

[0062] The control method of the present invention is applied to a refrigeration system that is not equipped with a flow measurement device. Based on the semi-empirical model coefficient correction method, the mass flow rate flowing through the compressor is calculated, thereby modeling the refrigeration system, solving the problem that the refrigeration system optimization control method cannot be implemented in a scenario where cooling capacity data is missing. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A refrigeration system diagram of a water cooling unit implemented in the present invention;

[0065] Figure 2 This is a schematic diagram of the working process of the screw refrigeration compressor of the present invention;

[0066] Figure 3 A flow chart of a refrigeration system model implemented in the present invention;

[0067] Figure 4 Flow chart of the implementation of the present invention. DETAILED DESCRIPTION

[0068] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. The specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by technicians in the technical field of the present invention.

[0069] The internal structure of the refrigeration system includes a compressor, a condenser, a throttle valve and an evaporator; the compressor inhales the refrigerant vapor from the evaporator and compresses it into a high-pressure gas, and because the compressor consumes a certain amount of mechanical energy during the compression process, and the mechanical energy is converted into heat energy in this process, the temperature of the refrigerant vapor increases and the refrigerant vapor is in an overheated state; the high-temperature refrigerant vapor discharged from the compressor releases heat in the condenser and transfers the heat to the surrounding water or air, thereby gradually condensing the refrigerant vapor into liquid; the refrigerant liquid coming out of the condenser is decompressed to the evaporation pressure through the throttle valve; after the liquid refrigerant passes through the throttle valve, due to the reduction in pressure, it begins to boil and vaporize and absorb heat, thereby achieving the purpose of refrigeration.

[0070] like Figure 1 As shown, the specific example of the present invention is a water cooling unit, wherein P e is the compressor input power, Q e is the cooling load at the evaporation end, Q c is the heat load at the condensing end, T water,in is the cold water inlet temperature, T water,out is the cold water outlet temperature, pref,in is the compressor suction pressure, t ref,in is the compressor suction temperature, p ref,out is the exhaust pressure of the compressor. However, there is no flow measurement device in this refrigeration system, so it is impossible to collect or calculate the mass flow of the working fluid flowing through the system or the refrigeration load required at the evaporation end in a certain period of time through on-site data collection.

[0071] In one embodiment of the present invention, referring to Figure 1-Figure 4 , provides a refrigeration system optimization control method based on virtual cooling capacity, comprising the following steps:

[0072] Step 100: collecting on-site historical data of the refrigeration system and preprocessing the collected on-site historical data, wherein the on-site historical data includes weather parameters and operating parameters;

[0073] Step 200: Establish component mechanism models based on the composition of the refrigeration system, wherein the component mechanism models include an evaporator mechanism model, a compressor mechanism model, a condenser mechanism model, and a throttle valve model. All component mechanism models are connected in series based on the law of conservation of mass to form a refrigeration system model; obtain the correct compressor inlet refrigerant mass flow rate through iterative calculation, and calculate the compressor power; compare the calculated compressor power with the theoretical power, and optimize the coefficient to be determined;

[0074] Step 300: Combine the refrigeration system model and the particle swarm optimization algorithm to calculate the optimal operation control parameters under the given virtual cooling capacity and weather parameters to minimize the energy consumption of the refrigeration system; and send the optimal operation control parameters to the device controller;

[0075] Step 400: Execute step 100 again, use the newly generated training samples to calculate the model error in the refrigeration system mechanism model, and when the refrigeration system model error is greater than a preset threshold, repeat the above steps 200 to 400.

[0076] The following is a detailed explanation of each of the above steps:

[0077] Step 100: Collecting on-site historical data of the refrigeration system and preprocessing the collected on-site historical data, wherein the on-site historical data includes weather parameters and operating parameters.

[0078] As an example, this step may include the following steps:

[0079] Step 110: Collecting on-site historical data of the refrigeration system, where the on-site historical data includes weather parameters and operating parameters.

[0080] The operating parameters include pressure, temperature, compressor power, load and frequency data, etc. Only pressure sensors and temperature sensors are installed in the refrigeration system, and compressor power, load and frequency data are collected on the compressor side, without collecting the mass flow of the working fluid flowing through the system or the flow on the refrigerant side.

[0081] Pressure sensors are used to monitor the pressure on the high and low pressure sides of the refrigeration system. Temperature sensors are used to measure the temperature of key parts of the refrigeration system, such as the evaporator outlet, condenser inlet, etc. Temperature data is very important to ensure that the system can provide the required cooling capacity and monitor the energy efficiency ratio of the system.

[0082] Compressor power, load and frequency can reflect the working status and efficiency of the compressor. Power data can reveal the energy consumption of the compressor, while load and frequency data help to understand the workload and adjustment capacity of the compressor.

[0083] Step 120: pre-processing the collected field historical data as training samples for the subsequent refrigeration system model; wherein the pre-processing includes data noise reduction, data filtering, data repair, data reduction and steady-state processing.

[0084] Data denoising aims to reduce random errors and noise in the collected field historical data to improve the accuracy and reliability of the data. Data denoising can be done by wavelet threshold denoising and empirical mode decomposition. Wavelet transform can decompose the signal into components of different frequencies; by setting a threshold, the wavelet coefficients below the threshold can be filtered out to remove noise. Empirical mode decomposition is an adaptive signal decomposition method that can decompose complex signals into a series of intrinsic mode functions (IMFs); by selectively removing certain IMFs, noise suppression can be achieved.

[0085] Data filtering aims to remove outliers and data points that do not meet expectations. Data filtering can be done using the Mahalanobis distance method and the k-nearest neighbor method. The Mahalanobis distance takes into account the correlation between data and can more accurately identify outliers that are inconsistent with the overall data distribution; by setting a threshold, points that exceed the threshold can be filtered out. The k-nearest neighbor method filters based on the similarity between data points; for each data point, its distance to the nearest k neighbors is calculated; if the distance of a point is significantly greater than that of other points, it may be considered an outlier and filtered out.

[0086] Data repair aims to fill missing values ​​or repair damaged data. Data repair methods include linear interpolation and expectation maximization. Linear interpolation estimates missing values ​​by connecting straight lines between known data points. The expectation maximization algorithm (EM algorithm) is used to estimate the parameters of the model in the presence of missing data; by continuously iteratively updating parameters and missing values, data repair can be achieved.

[0087] Data reduction aims to reduce the dimensionality and complexity of data while retaining as much information as possible. Data reduction methods include Latin hypercube sampling and Z-score normalization. Latin hypercube sampling is a method for generating multidimensional uniformly distributed samples; this method can reduce the amount of data while retaining important features of the data. Z-score normalization normalizes the data by calculating the standard deviation multiple of each data point from the mean. This method makes the data have a uniform scale, which is convenient for subsequent processing and analysis.

[0088] Steady-state processing aims to ensure that the data is stable in time, that is, the statistical characteristics of the data remain unchanged over a certain period of time. This is particularly important for time series analysis and forecasting.

[0089] Step 200: Establish component mechanism models based on the composition of the refrigeration system, wherein the component mechanism models include an evaporator mechanism model, a compressor mechanism model, a condenser mechanism model, and a throttle valve model. All component mechanism models are connected in series based on the law of conservation of mass to form a refrigeration system model; obtain the correct compressor inlet refrigerant mass flow rate through iterative calculation, and calculate the compressor power; compare the calculated compressor power with the theoretical power, and optimize the unknown coefficients.

[0090] As an example, this step may include the following steps:

[0091] Step 210: Establish a compressor mechanism model, which is a semi-empirical model of a twin-screw compressor. Its calculation process can be divided into an intake throttling calculation process, a leakage adiabatic mixing calculation process, an intake constant-pressure heating calculation process, a multi-party compression process, an exhaust constant-pressure cooling calculation process and an exhaust throttling calculation process; through iterative calculation, solve the unknown coefficients and mass flow rate inside the compressor.

[0092] Reference Figure 2 , 0~1 is the suction throttling calculation process, 1~2 is the leakage adiabatic mixing calculation process, 2~3 is the suction constant pressure heating calculation process, 3~5 is the multi-party compression process, 5~6 is the exhaust constant pressure cooling calculation process, 7~8 is the exhaust throttling calculation process.

[0093] The suction throttling calculation process can be simplified to the isentropic nozzle process:

[0094]

[0095] In the above formula, h1 represents the refrigerant specific enthalpy after suction throttling; h0 represents the compressor inlet specific enthalpy; represents the gas delivery mass flow rate of the compressor; d1 represents the refrigerant density after suction throttling; A su Indicates the suction throttling area.

[0096] The leakage adiabatic mixing calculation process is a mixing process of the leakage mass flow rate and the suction mass. The specific enthalpy of the mixed refrigerant is calculated as follows:

[0097]

[0098] In the above formula, Indicates the compressor leakage mass flow rate; Indicates the actual refrigerant mass flow rate entering the rotor compression chamber; h leak,in It represents the specific enthalpy of refrigerant at the entrance of the compressor leakage channel; h2 represents the specific enthalpy of refrigerant after adiabatic mixing.

[0099] The calculation process of the suction constant pressure heating is as follows: the suction gas will be heated by the casing, the refrigerant temperature will increase, and the enthalpy value will increase. Through assumptions, the energy conservation law is finally used to iterate and obtain:

[0100]

[0101] In the above formula, Q 23 Indicates the intake air preheating heat transfer; AU suc Indicates the suction heat transfer coefficient, t w represents the average temperature of the casing; t2 represents the refrigerant temperature on the suction side; c p2 It represents the specific heat capacity of refrigerant at constant pressure on the suction side; h3 represents the specific enthalpy of the refrigerant after suction preheating.

[0102] The multi-way compression process can be divided into a multi-way compression process and a forced exhaust process:

[0103]

[0104] p4=p3·BVR α ;

[0105] In the above formula, BVR represents the compressor internal volume ratio; α represents the polynomial index; v4 represents the refrigerant specific volume after the end of internal compression; v3 represents the refrigerant specific volume before the start of internal compression; p4 represents the end pressure of internal compression; p3 represents the refrigerant pressure after suction throttling.

[0106] The calculation process of the exhaust constant pressure cooling and the exhaust throttling is similar to the suction process. The leakage of the twin-screw compressor is evaluated by the virtual path connecting the suction and exhaust:

[0107]

[0108] In the above formula, Indicates the leakage of the compressor; d leak Indicates the refrigerant density at the entrance of the leakage channel; A leak Indicates the leakage area of ​​the compressor; hleak,in Represents the specific enthalpy of the refrigerant at the entrance of the leakage channel; h leak,out Represents the specific enthalpy of refrigerant at the outlet of the leakage channel.

[0109] Actual gas transmission mass flow rate:

[0110]

[0111] The model calculates the input power, mass flow rate and refrigerant outlet temperature through the compressor based on the operating conditions of the compressor suction and exhaust sides:

[0112]

[0113] Where t7 represents the refrigerant outlet temperature; f(n, load, p evp ,t evp ,p con ) represents the input parameter group of the compressor model; n represents the compressor speed, load represents the load position; p evp Indicates the evaporation pressure on the suction side, t evp Indicates the evaporation temperature on the suction side, p con Indicates the condensing pressure on the exhaust side.

[0114] Step 220: Establish a condenser mechanism model, which is a discrete model of an air-cooled condenser; divide the condenser heat exchange process into multiple micro-elements, and the heat exchange of each micro-element is calculated by the heat exchange coefficient and the refrigerant temperature; calculate the refrigerant enthalpy at the condenser outlet through the condenser heat exchange.

[0115] The heat transfer process is divided into N sections, and the heat transfer amount of each section is Q con for:

[0116]

[0117] Among them, K con,i represents the average heat transfer coefficient of each microelement, t con,i Represents the average temperature of the refrigerant in each microelement, t sr,i represents the average temperature of each micro-element wind; d represents the diameter of the heat exchange tube; l represents the length of the heat exchange tube. The enthalpy value h3 of the refrigerant at the condenser outlet can be calculated by the heat exchange amount, where h2 is the enthalpy value at the compressor outlet, calculated by the compressor model:

[0118]

[0119] Step 230: Establish a throttle valve mechanism model: the enthalpy values ​​of the inlet and outlet refrigerants before and after the throttle valve are equal.

[0120] The throttle valve mechanism model is an isenthalpic model, and the inlet and outlet enthalpies before and after the throttle valve are equal:

[0121] h3=h4;

[0122] In the above formula, h4 represents the refrigerant enthalpy after the throttle valve.

[0123] Step 240: Establish an evaporator mechanism model, which is a discrete model of a shell and tube evaporator; divide the evaporator into multiple micro-elements, and calculate the heat exchange of each micro-element through the corresponding heat exchange coefficient and temperature; and calculate the refrigerant enthalpy value at the evaporator outlet through the evaporator heat exchange.

[0124] By dividing the heat transfer process into N sections, the heat transfer amount of each section is Q evp for:

[0125]

[0126] Among them, K evp,i represents the average heat transfer coefficient of each microelement, t evp,i is the average temperature of the refrigerant in each microelement, t sr,i is the average temperature of cold water in each microelement, d is the diameter of the heat exchange tube, and l is the length of the heat exchange tube.

[0127] The enthalpy value of the refrigerant at the evaporator outlet can be calculated by the evaporator heat exchange 1' :

[0128]

[0129] In the above formula, Indicates the mass flow rate of refrigerant delivered by the compressor.

[0130] Step 250: Obtain the correct mass flow rate of the compressor inlet refrigerant by iteratively calculating the refrigerant enthalpy value at the evaporator outlet and the refrigerant enthalpy value at the compressor inlet; calculate the actual power of the compressor in combination with the compressor model, and compare it with the theoretical power, and optimize the unknown coefficients through the genetic algorithm.

[0131] By calculating the refrigerant enthalpy h at the evaporator outlet 1' Iterate the correct mass flow rate with the compressor inlet refrigerant enthalpy h1 Calculate the compressor power using the compressor model:

[0132]

[0133] P loss,1 =a tl,1 ·P i ;

[0134] P loss,2 =a tl,2 μV sw ·ω 2 ;

[0135] P loss,3 =a tl,3 ·m loss ·Δh loss ;

[0136] P com =P i +P loss,1 +P loss,2 +P loss,3 ;

[0137] Among them, P i Indicates the compression power in the compressor; P loss,1 Indicates the torque loss during the operation of the compressor; P loss,2 Indicates the viscous loss during the operation of the compressor; P loss,3 Indicates the additional power loss during partial load operation; P com Indicates the total input power of the compressor; w i represents the internal compression work, calculated by the multi-party compression process; a tl,1 Represents the torque friction loss coefficient; a tl,2 represents the viscous friction loss coefficient, μ represents the viscosity of the lubricating oil, and ω represents the compressor speed; a tl,3 Indicates the partial load loss factor, m loss Indicates the refrigerant mass flow rate that is bypassed during partial load operation; Δh loss It represents the theoretical enthalpy loss of bypassed refrigerant during part load operation.

[0138] Calculate the difference between the predicted and actual power values, and determine the coefficients to be measured through genetic algorithms and field data. Specifically, first, the possible range of values ​​of each coefficient is limited based on engineering experience, and then the initial parameter group is randomly generated in the feasible domain through genetic algorithms. Based on the initial parameter group, the compressor power prediction is completed. Evaluate the difference between the predicted value and the actual value: if the accuracy requirements are met or the number of iterations exceeds the upper limit, the currently obtained coefficient value is output; if the accuracy requirements are not met, the genetic algorithm is used to adjust the parameter value, and the above process is repeated to finally complete the determination of the coefficients to be measured.

[0139] Including leakage area A leak , inlet and outlet throttling area, inlet and outlet heat transfer coefficient, loss coefficient a tl,1 、a tl,2 、a tl,3 Etc., M is the data sample size:

[0140]

[0141] Among them, err represents the difference between the compressor power and the actual power, P com,i Indicates the calculated power of the compressor, Preal,i Indicates actual power.

[0142] Step 300: Combine the refrigeration system model and the particle swarm optimization algorithm to calculate the optimal operation control parameters under given virtual cooling capacity and weather parameters to minimize the energy consumption of the refrigeration system; and send the optimal operation control parameters to the device controller.

[0143] Particle swarm optimization is an optimization technology based on swarm intelligence. It simulates the foraging behavior of bird flocks and seeks the optimal solution to the problem through information sharing and collaboration among individuals in the flock. The compressor speed and load are adjusted by the particle swarm optimization algorithm to minimize the system power consumption at a given condensing temperature, including:

[0144] (1) Define the optimization problem:

[0145] The goal is to minimize the system power consumption P; the variables are the compressor speed n and load; the constraint is the evaporation pressure p evp , compressor inlet temperature t0, coolant inlet temperature t sp And the average cooling load over the past period of time:

[0146] [P,n,load,t con ]=f(p evp ,t0,t sp ,Q evp ).

[0147] (2) Particle swarm optimization algorithm initialization:

[0148] Particle swarm: Initialize a group of particles, each particle represents a set of compressor speed and load;

[0149] Speed: Assign a random speed to each particle, which determines the direction and step size of the particle's movement in the search space;

[0150] Position: The position of a particle corresponds to a point in the search space, i.e., a specific set of compressor speed and load values;

[0151] Individual best position: record the best position found by each particle so far, that is, the compressor speed and load corresponding to the minimum power consumption;

[0152] Global best position: records the best position found so far in the entire particle swarm.

[0153] (3) Iteration process:

[0154] Evaluation: Calculate the objective function value of each particle, i.e., power consumption, and update its individual best position and global best position.

[0155] Update speed: Update the speed of each particle according to the current speed, individual best position and global best position. Three parameters are involved: inertia weight, cognitive learning factor and social learning factor.

[0156] Update Position: Updates the position of each particle according to the new velocity.

[0157] Check constraints: Make sure the new position satisfies all given constraints, such as evaporation pressure, compressor inlet temperature, refrigerant inlet temperature, etc. If not, make necessary adjustments or reinitialize the particle.

[0158] Iteration: Repeat the above steps until a predetermined number of iterations is reached or a certain stopping criterion is met, such as the improvement in power consumption is less than a certain threshold.

[0159] (4) Result output:

[0160] Output the global optimal position, that is, the compressor speed and load value corresponding to the minimum power consumption; output the system power consumption at this time.

[0161] Step 400: Execute step 100 again, use the newly generated training samples to calculate the model error in the refrigeration system mechanism model, and when the refrigeration system model error is greater than a preset threshold, repeat the above steps 200 to 400.

[0162] The data is collected after each optimization and compared with the error of the program calculation result. When the error is too large, the coefficient to be tested is retrained to ensure the accuracy of the program.

[0163] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A refrigeration system optimization control method based on virtual cooling capacity, characterized in that: The following steps are involved: Step 100: collecting on-site historical data of the refrigeration system and preprocessing the collected on-site historical data, wherein the on-site historical data includes weather parameters and operating parameters; Step 200: Establish component mechanism models based on the composition of the refrigeration system, wherein the component mechanism models include an evaporator mechanism model, a compressor mechanism model, a condenser mechanism model, and a throttle valve model. All component mechanism models are connected in series based on the law of conservation of mass to form a refrigeration system model; obtain the correct compressor inlet refrigerant mass flow rate through iterative calculation, and calculate the compressor power; compare the calculated compressor power with the theoretical power, and optimize the coefficient to be determined; Step 300: Combining the refrigeration system model and the particle swarm optimization algorithm, the optimal operation control parameters under given virtual cooling capacity and weather parameters are calculated to minimize the energy consumption of the refrigeration system; Step 400: Execute step 100 again, use the newly generated training samples to calculate the model error in the refrigeration system mechanism model, and when the refrigeration system model error is greater than a preset threshold, repeat the above steps 200 to 400.

2. The refrigeration system optimization control method based on virtual cooling capacity according to claim 1 is characterized in that: The compressor mechanism model: its calculation process includes suction throttling calculation process, leakage adiabatic mixing calculation process, suction constant pressure heating calculation process, multi-party compression process, exhaust constant pressure cooling calculation process and exhaust throttling calculation process; through iterative calculation, the unknown coefficients and mass flow rate inside the compressor are solved.

3. The refrigeration system optimization control method based on virtual cooling capacity according to claim 2 is characterized in that: The condenser mechanism model includes: dividing the condenser heat exchange process into multiple micro-elements, and the heat exchange amount of each micro-element is calculated by heat exchange coefficient and refrigerant temperature; and calculating the refrigerant enthalpy value at the condenser outlet through the condenser heat exchange amount.

4. The refrigeration system optimization control method based on virtual cooling capacity according to claim 3 is characterized in that: The throttle valve mechanism model: the inlet and outlet refrigerant enthalpy values ​​before and after the throttle valve are equal, the inlet refrigerant enthalpy value before the throttle valve is the outlet refrigerant enthalpy value of the condenser, and the outlet refrigerant enthalpy value after the throttle valve is the inlet refrigerant enthalpy value of the evaporator.

5. The refrigeration system optimization control method based on virtual cooling capacity according to claim 4 is characterized in that: The evaporator mechanism model: the evaporator is divided into multiple micro-elements, and the heat exchange capacity of each micro-element is calculated by the corresponding heat exchange coefficient and temperature; and the refrigerant enthalpy value at the evaporator outlet is calculated by the evaporator heat exchange capacity.

6. The refrigeration system optimization control method based on virtual cooling capacity according to claim 2 is characterized in that: The suction throttling calculation process is simplified to the isentropic nozzle process: In the above formula, h1 represents the specific enthalpy of the refrigerant after suction throttling; h0 represents the compressor inlet enthalpy; represents the gas delivery mass flow rate of the compressor; d1 represents the refrigerant density after suction throttling; A su Indicates the suction throttling area; The leakage adiabatic mixing calculation process is a mixing process of the leakage mass flow rate and the suction mass. The specific enthalpy of the mixed refrigerant is calculated as follows: In the above formula, Indicates the compressor leakage mass flow rate; Indicates the actual refrigerant mass flow rate entering the rotor compression chamber; h leak,in represents the specific enthalpy of the refrigerant at the inlet of the compressor leakage channel; h2 represents the specific enthalpy of the refrigerant after adiabatic mixing; The calculation process of the suction constant pressure heating is as follows: the suction gas will be heated by the casing, the refrigerant temperature will increase, and the enthalpy value will increase. Through assumptions, the energy conservation law is finally used to iterate and obtain: In the above formula, Q 23 Indicates the amount of heat exchanged by the refrigerant being preheated by the casing; AU suc Indicates the suction heat transfer coefficient, t w represents the casing temperature; t2 represents the refrigerant temperature on the suction side; c p2 Indicates the specific constant pressure heat capacity of the refrigerant on the suction side; m tot It represents the actual refrigerant mass flow rate entering the rotor compression chamber; h3 represents the specific enthalpy of the refrigerant after suction preheating; The multi-party compression process is divided into a multi-party compression process and a forced exhaust process: p4=p3·BVR α ; In the above formula, BVR represents the compressor volume ratio; α represents the polynomial index; v4 represents the refrigerant specific volume after the internal compression process ends; v3 represents the refrigerant specific volume before the internal compression process begins; p4 represents the internal compression end pressure; p3 represents the refrigerant pressure after suction throttling; The calculation process of the exhaust constant pressure cooling and the exhaust throttling is similar to the suction process; the leakage of the twin-screw compressor is evaluated by the virtual path connecting the suction and exhaust: In the above formula, Indicates the compressor leakage mass flow rate; d leak Indicates the refrigerant density at the entrance of the leakage channel; A leak Indicates the compressor leakage channel area; h leak,in Represents the specific enthalpy of the refrigerant at the entrance of the compressor leakage channel; h leak,out It represents the specific enthalpy of refrigerant at the outlet of the compressor leakage channel; Actual gas transmission mass flow rate: The model calculates the input power, mass flow rate and refrigerant outlet temperature through the compressor based on the operating conditions of the compressor suction and exhaust sides: Where t7 represents the refrigerant outlet temperature; f(n, load, p evp ,p con ) represents the input parameter group of the compressor model; n represents the compressor speed, load represents the load position; p evp Indicates the evaporation pressure on the suction side, t evp Indicates the evaporation temperature on the suction side, p con Indicates the condensing pressure on the exhaust side.

7. The refrigeration system optimization control method based on virtual cooling capacity according to claim 3 is characterized in that: The condenser mechanism model divides the heat exchange process into N sections, and the heat exchange amount of each section is Q con for: Among them, K con,i represents the average heat transfer coefficient of each microelement, t con,i Represents the average temperature of the refrigerant in each microelement, t sr,i Indicates the average temperature of the coolant in each microelement; The enthalpy value h3 of the refrigerant at the condenser outlet is calculated by heat exchange, where h2 represents the enthalpy value of the refrigerant at the compressor outlet, calculated by the compressor model: In the above formula, Indicates the refrigerant mass flow rate of the compressor.

8. The refrigeration system optimization control method based on virtual cooling capacity according to claim 4 is characterized in that: The evaporator mechanism model divides the heat exchange process into N sections, and the heat exchange amount of each section is Q evp for: Among them, K evp,i represents the average heat transfer coefficient of each microelement, t evp,i Represents the average temperature of the refrigerant in each microelement, t sr,i represents the average temperature of each micro-element wind; d represents the diameter of the heat exchange tube; l represents the length of the heat exchange tube; Calculate the evaporator outlet enthalpy h by using the evaporator heat exchange 1' : In the above formula, Indicates the mass flow rate of refrigerant delivered by the compressor.

9. The refrigeration system optimization control method based on virtual cooling capacity according to claim 5, characterized in that: The correct compressor inlet refrigerant mass flow rate is iterated by calculating the evaporator outlet refrigerant enthalpy and the compressor inlet refrigerant enthalpy Calculate compressor power: P loss,1 =a tl,1 ·P i ; P loss,2 =a tl,2 ·m·V sw ·oh 2 ; P loss,3 =a tl,3 ·m loss ·Δh loss ; P com =P i +P loss,1 +P loss,2 +P loss,3 ; Among them, P i Indicates the compression power in the compressor; P loss,1 Indicates the torque loss during the operation of the compressor; P loss,2 Indicates the viscous loss during the operation of the compressor; P loss,3 Indicates the additional power loss during partial load operation; P com Indicates the total input power of the compressor; w i represents the internal compression work, calculated by the multi-party compression process; a tl,1 Represents the torque friction loss coefficient; a tl,2 represents the viscous friction loss coefficient, μ represents the viscosity of the lubricating oil, and ω represents the compressor speed; a tl,3 represents the partial load loss factor, m loss Indicates the refrigerant mass flow rate that is bypassed during partial load operation; Δh loss It represents the theoretical enthalpy loss of bypassed refrigerant during part load operation; Calculate the difference between the compressor power and the actual power, and determine the coefficients to be tested, including the leakage area A, through genetic algorithms and field data. leak , inlet and outlet throttling area, inlet and outlet heat transfer coefficient, loss coefficient a tl,1 、a tl,2 、a tl,3 , M is the data sample size: Among them, err represents the difference between the compressor power and the actual power, P com,i Indicates the calculated power of the compressor, P real,i Indicates actual power.

10. The refrigeration system optimization control method based on virtual cooling capacity according to claim 1, characterized in that: In step 300, the particle swarm optimization algorithm is used to obtain the given evaporation pressure p evp , compressor inlet temperature t0, refrigerant inlet temperature t sp By adjusting the compressor speed n and load, it is found that at a certain condensing temperature t con The minimum power consumption P of the system is: [P,n,load,t con ]=f(p evp ,t0,t sp )。

Images