State monitoring and adjustment mechanism of a cold therapy cabin refrigeration system

Abstract

This invention discloses a state monitoring and regulation mechanism for a cryotherapy chamber refrigeration system, comprising the following steps: establishing a model of the refrigeration capacity of the cryotherapy chamber refrigeration system and the heat load inside the cryotherapy chamber, obtaining historical information of the model, predicting future values, and thus determining the target predicted setpoint that needs to be adjusted in the future; this invention implements a modulation mechanism for controlling the cryotherapy chamber refrigeration system by constructing a prediction algorithm based on the heat load model of the chamber and the operating parameter model of the refrigeration system, enabling the cryotherapy chamber to operate safely, efficiently and stably. The prediction algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a period of time, adjusts the operating parameters of the system under different operating conditions, and introduces feedback correction through sensors to reduce prediction errors and fluctuation interference, thereby achieving optimal control and improving the overall energy efficiency of the refrigeration system.

Description

Technical Field

[0001] This invention relates to the field of refrigeration system technology, and specifically to a status monitoring and regulation mechanism for a cryotherapy chamber refrigeration system. Background Technology

[0002] When the refrigeration system provides cooling to the cryotherapy chamber, the heat load inside the chamber varies with factors such as ambient temperature, patient differences, chamber cooling temperature, and usage conditions, introducing uncertain variables into the control of the refrigeration system. This results in nonlinearity, hysteresis, and time-varying parameters in the refrigeration system. Hysteresis can cause severe overshoot during chamber temperature control, posing challenges to the control design of the refrigeration system. To ensure the safe, efficient, and stable operation of the cryotherapy chamber, a predictive algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a future period, adjusts the system's operating parameters under different conditions, and uses sensors to introduce feedback correction to reduce prediction errors and fluctuations, achieving optimal control and improving the overall energy efficiency of the refrigeration system. Summary of the Invention

[0003] The purpose of this invention is to provide a status monitoring and adjustment mechanism for a cryotherapy chamber refrigeration system, in order to solve the problems mentioned in the background art of predicting and optimizing variables of the refrigeration system over a period of time, adjusting the operating parameters of the system under different working conditions, and introducing feedback correction through sensors to reduce prediction errors and fluctuation interference, so as to achieve optimal control.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A status monitoring and regulation mechanism for a cryotherapy chamber refrigeration system includes the following steps;

[0006] S1; Cooling prediction; Establish a model of the cooling capacity of the cooling system and the heat load inside the cooling chamber, obtain historical information of the model, predict future values, and thus determine the target prediction setpoint that needs to be adjusted in the future;

[0007] S2; Rolling optimization; Continuous online optimization of the refrigeration system by constantly updating boundary conditions; The process involves optimizing the system state based on performance indicators at each sampling moment. The refrigeration system executes the setting instructions for the first time period only, adjusting the operating parameters in a timely manner. When the next sampling moment arrives, the rolling optimization time window moves forward. The refrigeration system adjustment is optimized by updating the prediction information in real time through rolling optimization to compensate for the poor treatment experience caused by the uncertainty of the heat load and cooling capacity of the refrigeration system in the cryotherapy chamber. Interval optimization is performed simultaneously with rolling optimization. The interval is used to represent the fluctuation range of the uncertainty variables between the heat load and cooling capacity of the refrigeration system in the cryotherapy chamber. The upper and lower limits selected when building the model include all possibilities within the range of the uncertainty variable setting, so as to characterize the comprehensive uncertainty variables. Under the influence of uncertainty factors, the heat load and cooling capacity of the refrigeration system in the cryotherapy chamber are difficult to predict accurately. The analysis of the fluctuation and intermittency of the operating parameters of the refrigeration system is insufficient, resulting in untimely adjustment of the refrigeration system operation during the optimization process and inability to effectively match the changing heat load requirements of the cryotherapy chamber. Based on the interval optimization modeling, the prediction error and fluctuation of the cooling capacity and heat load in the cryotherapy chamber can be fully summarized on the impact of the refrigeration system operating parameters.

[0008] S3; Predictive model adjustment; Based on the historical information of the refrigeration cycle and the heat load of the cryotherapy chamber obtained in step 1 above, the variables in the future period are predicted and optimized, the operating parameters of the system under different working conditions are adjusted, and feedback correction is introduced through sensors to reduce the interference of prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0009] The heat load inside the cryotherapy chamber in S1 includes the heat load of the chamber body, the heat leakage when entering and exiting the chamber, the heat leakage of the chamber space, the radiative heat exchange between the chamber body and the environment, and the cryotherapy load inside the cryotherapy chamber; the evaporator of the cryotherapy chamber refrigeration system absorbs the heat load inside the chamber and maintains the temperature inside the chamber at the treatment temperature.

[0010] The cooling capacity of the refrigeration system in S1 is obtained by analyzing the energy balance and refrigeration cycle of the cryotherapy chamber. The refrigerant enters the evaporator as a low-pressure, low-temperature two-phase flow, absorbs heat and transforms into superheated vapor. The vapor refrigerant is then compressed into high-temperature, high-pressure vapor by the compressor and enters the condenser. In the condenser, the refrigerant is cooled and becomes a two-phase flow, eventually transforming into a high-pressure liquid refrigerant. Subsequently, it is throttled by the electronic expansion valve, and the pressure drops to the evaporation pressure, transforming into a low-pressure, low-temperature two-phase flow to complete the refrigeration cycle.

[0011] The model established in S3 is a control model, which analyzes all possibilities within the set range of uncertain variables, predicts the heat load in the cryotherapy chamber and the cooling capacity of the mass system, and the evaporator of the cryotherapy chamber's cooling system absorbs the heat load in the chamber to maintain the temperature in the chamber at the treatment temperature.

[0012] The highly complex nonlinear model obtained from the control model further yields the objective function of the finite time domain optimization problem. The main control system of the refrigeration system adjusts the operation at the next moment through the objective function.

[0013] Use a model with known parameters to design a controller that operates on the refrigeration system.

[0014] 1. Cooling capacity of the cryotherapy chamber's refrigeration system and heat load inside the cryotherapy chamber;

[0015] The cryotherapy chamber system's heat load and cooling capacity are controlled by the refrigerant, which enters the evaporator as a low-pressure, low-temperature two-phase flow, absorbing heat and transforming into superheated vapor. The vaporized refrigerant is then compressed into high-temperature, high-pressure vapor by the compressor and enters the condenser. In the condenser, the refrigerant is cooled, becoming a two-phase flow, and eventually transforming into a high-pressure liquid refrigerant. Subsequently, it is throttled by an electronic expansion valve, causing the pressure to drop to the evaporation pressure, transforming into a low-pressure, low-temperature two-phase flow to complete the refrigeration cycle.

[0016] The thermodynamics of the compressor and expansion valve are an order of magnitude faster than that of the heat exchangers (evaporator and condenser). Their mathematical model is as follows:

[0017]

[0018] h out,comp =η is [h is -h in,comp ]+h in,comp Formula 2

[0019]

[0020] Formulas 1 and 2 above are expressions for the refrigerant mass flow rate and specific enthalpy through the compressor, and Formula 3 is the refrigerant mass flow rate through the electronic expansion valve.

[0021] The mass flow rate of refrigerant through the compressor (kg·s) -1 N comp compressor speed (r·min) -1 V d Volumetric displacement m 3 ·r -1 η v Volumetric efficiency, ρ comp The density of the refrigerant in the compressor (kg·m) -3 ;

[0022] h out,comp Specific enthalpy of refrigerant at compressor outlet (J·kg) -1 η is Adiabatic efficiency, h is Specific enthalpy of refrigerant (J·kg)-1 h in,comp Specific enthalpy of inlet refrigerant in compressor (J·kg) -1 ;

[0023] Mass flow rate of refrigerant via expansion valve (kg·s) -1 C v Flow coefficient of electronic expansion valve, A s,v Electronic expansion valve cross-sectional area m 2 p v Refrigerant density (kg·m) via electronic expansion valve -3 P c Condensation pressure Pa, P e Evaporation pressure (Pa).

[0024] Based on the state of the refrigerant in the evaporator heat exchanger and condenser, the region can be divided into a subcooled region, a two-phase flow region, and a superheated region. The length of the two-phase flow region is l. e l c Using length change as the control objective, and employing lumped parameter modeling, applying the principles of mass and energy conservation, the dynamic model of the evaporator heat exchanger and condenser can be expressed as:

[0025]

[0026]

[0027] Formula 4: Refrigerant heat transfer equation in the two-phase region of the evaporator heat exchanger; Formula 5: Refrigerant vapor change rate in the evaporator heat exchanger; Formula 6: Energy balance of the tube wall in the evaporator heat exchanger; Formula 7: Refrigerant mass in the evaporator heat exchanger and condenser;

[0028] Formula 8: Refrigerant change rate in condenser; Formula 9: Energy balance of condenser tube wall.

[0029] h 1ge Enthalpy of two-phase flow in evaporator (J·kg) -1 , ρ le Evaporator two-phase flow density, A s,e Evaporator pipe cross-sectional area (m) 2 h ge Enthalpy of vapor phase in evaporator (J·kg) -1 h in,e Enthalpy of vapor phase in evaporator (J·kg) -1 ;

[0030] a ie Evaporator inlet heat transfer coefficient, D ie Evaporator inlet diameter; l e Length of the two-phase region of the evaporator; T we Evaporator tube wall temperature; T reEvaporator saturated with refrigerant;

[0031] C p Specific heat capacity (J·(kg·K)) -1 a oe External heat transfer coefficient of evaporator W·(m 2 ·K) -1 A oe Evaporator outer surface area (m) 2 ,T ae Evaporator ambient temperature; a ie Evaporator inlet heat transfer coefficient W·(m 2 ·K) -1 a iesh Heat transfer coefficient W·(m) inside the superheated zone of the evaporator 2 ·K) -1 , Average porosity of the two-phase region of the evaporator.

[0032] The subscripts e→c above refer to the condenser.

[0033] The air-side heat transfer coefficient α of the evaporator and condenser oe (a oc ) and condenser air-cooled fan speed N c The correlation can be fitted as follows:

[0034]

[0035] a oc : External heat transfer coefficient of condenser W·(m 2 ·K) -1 N c : Condenser fan speed, Empirical coefficient.

[0036] The cryotherapy chamber can be considered as a controlled, enclosed physical space, and its heat load Q0 includes: chamber body heat load Q1, heat leakage when entering or leaving the chamber Q2, heat leakage in the chamber space Q3, heat exchange between the chamber and the environment through radiation Q4, and cryotherapy load within the cryotherapy chamber Q5.

[0037] 1) Cabin heat load Q1

[0038] Q1=K·A·a(t w -t n )

[0039]

[0040] K is the heat transfer coefficient of the cabin structure; A is the surface area of ​​the cabin structure; t w The ambient temperature outside the cabin; t n Temperature inside the cabin;

[0041] a w The heat transfer coefficient on the outside of the cabin; a n λ is the heat transfer coefficient inside the cabin; λ is the thermal conductivity of the cabin's insulation material; δ is the thickness of the insulation material.

[0042] 2) Heat loss when opening the hatch for entering or exiting the cabin (Q2)

[0043] Assuming that each time the cryotherapy chamber opens and closes, the cold air inside is completely replaced by warm air from the outside when the patient enters or exits the chamber, the heat loss during the opening and closing of the chamber can be calculated as Q2:

[0044]

[0045] Δh=h AT,AH -h CBT,CBH Formula 13

[0046] V CB The volume of the cryotherapy chamber is m 3 n represents the number of patients entering and exiting the cryotherapy chamber per hour, with a maximum of 3; v a Let be the specific volume of air.

[0047] Δh is the temperature and humidity difference when the ambient air from outside the cabin enters the room and reaches the specified cryotherapy temperature. h AT,AH Enthalpy of the ambient air outside the cabin at temperature AT and humidity AH h CBT,CBH Enthalpy of cabin air temperature CBT and humidity CBH

[0048] 3) Heat loss in the cabin space Q3

[0049] Gaps at the connections and joints of the various structural components of the cabin can cause cold air to leak out. Since the size and location of these gaps are random events, this portion of the leaked cold air cannot be calculated. Therefore, we take 10%–20% of the cooling load Q1 lost by the cryotherapy cabin; in this calculation, we use 15%.

[0050] Q3=0.15×Q1 Formula 14

[0051] 4) Heat exchange between the cabin and the environment via radiation Q4

[0052]

[0053] ε is the emissivity of the outer surface of the cabin; C0 is the blackbody emissivity; take... A ow t is the outer surface area of ​​the box. a For ambient temperature, t ow This refers to the outer surface temperature of the enclosure.

[0054] 5) Cryotherapy load Q5 in the cryotherapy chamber

[0055] The cryotherapy load within the cryotherapy chamber is the heat load that the patient's body dissipates into the chamber during treatment. Patients obtain the energy necessary for survival through metabolism; in a static state, most of this energy is used to maintain a constant body temperature, and then the heat is dissipated into the cryotherapy chamber.

[0056] Only when the patient's heat production equals the body's heat dissipation can the body temperature be maintained within a certain range. This can be described using the heat balance equation:

[0057] H = MWFCE

[0058] M represents the patient's metabolic rate. W represents the mechanical work performed by the patient. F represents the radiative heat exchange between the patient and the cabin environment. C represents the convective heat exchange between the patient and the cabin environment. E represents the amount of heat lost through sweat evaporation and respiration. H represents the accumulated heat in the human body.

[0059] a. The metabolic rate M of the above-mentioned patients refers to the heat generated per unit time by the human body surface area, which is usually determined by the amount of human activity and can be determined by the amount of oxygen consumed by the human body during respiration:

[0060]

[0061] R B Respiratory entropy; A D The surface area of ​​the human body (m) 2 ; The volume of oxygen consumed per unit time

[0062] b. The above W represents the power of the patient's muscle tissue, which can be calculated using mechanical efficiency η:

[0063]

[0064] The empirical value η ranges from 0 to 0.2. Since the patient does not have mechanical movement inside the cryo-fluid chamber, it can be approximated as W = 0.

[0065] c. E above represents the heat dissipation caused by evaporation from the patient's skin surface, which consists of sensory heat dissipation and non-sensory heat dissipation. Sensory heat dissipation is used in high-temperature environments, while non-sensory heat dissipation is the main type used in the low-temperature environment of the cryotherapy chamber.

[0066] E = 3.05 × 10 -3 ×254t sk -3335-P a ) Formula 18

[0067] t sk Skin temperature; P a The water vapor pressure (kPa) inside the cryotherapy chamber where the patient is located.

[0068] d. The above F represents the radiative heat loss from the patient and the cabin:

[0069] F = A P εσ(T sk 4 -T a 4 ) / A D Formula 19

[0070] A P =f ef f cl A D Formula 20

[0071] A P ε represents the effective radiation area of ​​the patient's clothing; ε represents the average emissivity of the outer surface of the patient's clothing; T represents... sk The average temperature of the patient's body surface; T a For ambient temperature; f ef f is the effective radiation area coefficient for the patient. cl This refers to the area coefficient of the patient's clothing.

[0072] e. The above C refers to convective heat exchange between the patient and the air inside the cabin:

[0073] C = h c f cl (T sk -T a ) Formula 21

[0074] T sk The average temperature of the patient's body surface; T a For ambient temperature; f cl h is the area coefficient of the patient's clothing. c is the convective heat transfer coefficient.

[0075] When the patient's body surface temperature remains constant, the body's accumulated heat H→0, and we have:

[0076] H = MWFCE

[0077] MWFCE=0

[0078] Furthermore, since the patient experiences virtually no mechanical movement within the cryotherapy chamber, taking W=0, we have:

[0079] MFCE=0

[0080] Therefore, all the metabolic waste produced by the patient is lost into the cryotherapy chamber. That is, when the temperature inside the cryotherapy chamber is constant at the treatment temperature, we have:

[0081] Q5 = M = F + C + E

[0082] The heat load Q0 inside the cabin is the same as the cooling load of the refrigeration system. Therefore, Q0 = Q1 + Q2 + Q3 + Q4 + Q5.

[0083] To simplify the calculations, all surfaces inside the cryotherapy chamber are considered flat. Therefore, the temperature equilibrium of the air inside the chamber is:

[0084]

[0085] Refrigeration system cooling load:

[0086]

[0087] a oe Evaporator outlet heat transfer coefficient W·(m 2 ·K) -1 A oe Evaporator outer surface area (m) 2 ;T ne Surface temperature inside the cryotherapy chamber; T we Temperature of the outer surface of the cryotherapy chamber.

[0088] 2. Control Model

[0089] The evaporator of the cryotherapy chamber's cooling system absorbs the heat load inside the chamber, maintaining the temperature at the treatment temperature. The air temperature inside the chamber is T. a Evaporator pressure P e Evaporator two-phase length l e Evaporator tube wall equivalent temperature T we Evaporator pressure P c Equivalent temperature T of condenser tube wall wc As the state variable of the system, the state vector of the control model is:

[0090] X = [l e ,P e ,T we ,P c ,T wc ,T a ] Formula 24

[0091] The system input is the compressor speed N. comp Electronic expansion valve opening A s,v Condenser fan frequency N c Control input vector:

[0092] U = [N] comp As,v N c ] Formula 25

[0093] The output of the above control model is the cabin temperature T. a Combining formulas 1 to 23 above, the control model is introduced into the state space:

[0094]

[0095] 3. Predictive model adjustment mechanism

[0096] The model in Formula 26 above is a highly complex nonlinear model. To linearize it, under steady-state conditions inside the chamber, the heat load of the cryotherapy chamber is constant, and its linear discrete model is as follows:

[0097]

[0098] u=A(x0,u0)x+B(x0,u0)u Formula 27

[0099]

[0100] x0 and u0 are the values ​​of the state vector and control input vector at the steady-state working fluid point, respectively; x and u are the smaller dynamic deviations from x0 and u0, respectively.

[0101] For the variable T of the cryotherapy chamber's refrigeration system a The objective function of the finite-time optimization problem is to be as close as possible to the set value, and to achieve high overall system energy efficiency, as shown in the attached figure of the above abstract. At each sampling time, the objective function is:

[0102]

[0103] Where N is the prediction range, M is the rolling duration, u is the control variable to be optimized, and e(k) is the difference between the system output and the output reference: e(k) = y(k) - r(k).

[0104] By establishing a refrigeration cycle and a heat load model for the cryotherapy chamber, variables can be predicted and optimized for a period of time in the future. The operating parameters of the system under different working conditions can be adjusted, and feedback correction can be introduced through sensors to reduce prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0105] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0106] This invention establishes a modulation mechanism for controlling the refrigeration system of a cryotherapy chamber by constructing a predictive algorithm based on the heat load model and the operating parameter model of the refrigeration system. This enables the cryotherapy chamber to operate safely, efficiently, and stably. The predictive algorithm models the heat load on the refrigeration system, predicts and optimizes variables over a future period, adjusts the operating parameters of the system under different operating conditions, and introduces feedback correction through sensors to reduce prediction errors and fluctuations, thereby achieving optimal control and improving the overall energy efficiency of the refrigeration system. Attached Figure Description

[0107] Figure 1 This is a flowchart illustrating the status monitoring and adjustment mechanism of the present invention;

[0108] Figure 2 This is a schematic diagram illustrating the scrolling duration of the present invention;

[0109] Figure 3 This is a simplified schematic diagram of the heat load and cooling energy flow of the cryotherapy chamber system of the present invention;

[0110] Figure 4 This is a circuit diagram of the refrigeration system of the cryotherapy chamber of the present invention. Detailed Implementation

[0111] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0112] A status monitoring and regulation mechanism for a cryotherapy chamber refrigeration system includes the following steps;

[0113] S1; Cooling prediction; Establish a model of the cooling capacity of the cooling system and the heat load inside the cooling chamber, obtain historical information of the model, predict future values, and thus determine the target prediction setpoint that needs to be adjusted in the future;

[0114] S2; Rolling optimization; Continuous online optimization of the refrigeration system by constantly updating boundary conditions; The process involves optimizing the system state based on performance indicators at each sampling moment. The refrigeration system only executes the setting instructions for the first time period each time, adjusting the operating parameters of the refrigeration system in a timely manner; When the next sampling moment arrives, the rolling optimization time window moves forward; The refrigeration system adjustment is optimized by updating the prediction information in real time through rolling optimization, so as to compensate for the poor treatment experience caused by the uncertainty of the heat load and the cooling capacity of the refrigeration system in the cryotherapy chamber. S3; Range optimization is performed simultaneously with rolling optimization; The range is used to represent the fluctuation range of the uncertainty variables between the heat load and the cooling capacity of the refrigeration system in the cryotherapy chamber; The upper and lower limits selected when building the model include all possibilities within the range of the uncertainty variables to represent the comprehensive uncertainty variables. Under the influence of uncertainty factors, the heat load and the cooling capacity of the refrigeration system in the cryotherapy chamber are difficult to predict accurately, and the analysis of the fluctuation and intermittency of the operating parameters of the refrigeration system is insufficient, resulting in the untimely adjustment of the refrigeration system operation during the optimization process and its inability to effectively match the changing needs of the heat load in the cryotherapy chamber. Based on the range optimization modeling, the prediction error and fluctuation of the cooling capacity of the refrigeration system and the heat load in the cryotherapy chamber can be fully summarized on the impact of the refrigeration system operating parameters.

[0115] S3; Predictive model adjustment; Based on the historical information of the refrigeration cycle and the heat load of the cryotherapy chamber obtained in step 1 above, the variables in the future period are predicted and optimized, the operating parameters of the system under different working conditions are adjusted, and feedback correction is introduced through sensors to reduce the interference of prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0116] The heat load inside the cryotherapy chamber in S1 includes the heat load of the chamber body, the heat leakage when entering and exiting the chamber, the heat leakage of the chamber space, the radiative heat exchange between the chamber body and the environment, and the cryotherapy load inside the cryotherapy chamber; the evaporator of the cryotherapy chamber refrigeration system absorbs the heat load inside the chamber and maintains the temperature inside the chamber at the treatment temperature.

[0117] The cooling capacity of the refrigeration system in S1 is obtained by analyzing the energy balance and refrigeration cycle of the cryotherapy chamber. The refrigerant enters the evaporator as a low-pressure, low-temperature two-phase flow, absorbs heat and transforms into superheated vapor. The vapor refrigerant is then compressed into high-temperature, high-pressure vapor by the compressor and enters the condenser. In the condenser, the refrigerant is cooled and becomes a two-phase flow, eventually transforming into a high-pressure liquid refrigerant. Subsequently, it is throttled by the electronic expansion valve, and the pressure drops to the evaporation pressure, transforming into a low-pressure, low-temperature two-phase flow to complete the refrigeration cycle.

[0118] The model established in S3 is a control model that analyzes all possibilities within the set range of uncertain variables, predicts the heat load in the cryotherapy chamber and the cooling capacity of the mass system, and the evaporator of the cryotherapy chamber's cooling system absorbs the heat load in the chamber, maintaining the temperature inside the chamber at the treatment temperature.

[0119] The highly complex nonlinear model obtained from the control model further yields the objective function of the finite time domain optimization problem. The main control system of the refrigeration system adjusts the operation at the next moment through the objective function.

[0120] 1. Cooling capacity of the cryotherapy chamber's refrigeration system and heat load inside the cryotherapy chamber;

[0121] The cryotherapy chamber system's heat load and cooling capacity are controlled by the refrigerant, which enters the evaporator as a low-pressure, low-temperature two-phase flow, absorbing heat and transforming into superheated vapor. The vaporized refrigerant is then compressed into high-temperature, high-pressure vapor by the compressor and enters the condenser. In the condenser, the refrigerant is cooled, becoming a two-phase flow, and eventually transforming into a high-pressure liquid refrigerant. Subsequently, it is throttled by an electronic expansion valve, causing the pressure to drop to the evaporation pressure, transforming into a low-pressure, low-temperature two-phase flow to complete the refrigeration cycle.

[0122] The thermodynamics of the compressor and expansion valve are an order of magnitude faster than that of the heat exchangers (evaporator and condenser). Their mathematical model is as follows:

[0123]

[0124] h out,comp =η is [h is -h in,comp ]+h in,comp Formula 2

[0125]

[0126] Formulas 1 and 2 above are expressions for the refrigerant mass flow rate and specific enthalpy through the compressor, and Formula 3 is the refrigerant mass flow rate through the electronic expansion valve.

[0127] The mass flow rate of refrigerant through the compressor (kg·) -1 N comp compressor speed (r·min) -1 V d Volumetric displacement m 3 ·r -1 η v Volumetric efficiency, ρ comp The density of the refrigerant in the compressor (kg·m) -3 ;

[0128] h out,comp Specific enthalpy of refrigerant at compressor outlet (J·kg) -1 η is Adiabatic efficiency, h is Specific enthalpy of refrigerant (J·kg) -1 h in,comp Specific enthalpy of inlet refrigerant in compressor (J·kg) -1 ;

[0129] Mass flow rate of refrigerant via expansion valve (kg·s) -1 C v Flow coefficient of electronic expansion valve, A s,v Electronic expansion valve cross-sectional area m 2 p v Refrigerant density (kg·m) via electronic expansion valve -3 P c Condensation pressure Pa, P e Evaporation pressure (Pa).

[0130] Based on the state of the refrigerant in the evaporator heat exchanger and condenser, the region can be divided into a subcooled region, a two-phase flow region, and a superheated region. The length of the two-phase flow region is l. e l c Using length change as the control objective, and employing lumped parameter modeling, applying the principles of mass and energy conservation, the dynamic model of the evaporator heat exchanger and condenser can be expressed as:

[0131]

[0132]

[0133] Formula 4: Refrigerant heat transfer equation in the two-phase region of the evaporator heat exchanger; Formula 5: Refrigerant vapor change rate in the evaporator heat exchanger; Formula 6: Energy balance of the tube wall in the evaporator heat exchanger; Formula 7: Refrigerant mass in the evaporator heat exchanger and condenser;

[0134] Formula 8: Refrigerant change rate in condenser; Formula 9: Energy balance of condenser tube wall.

[0135] h 1ge Enthalpy of two-phase flow in evaporator (J·kg) -1 , ρ le Evaporator two-phase flow density, A s,e Evaporator pipe cross-sectional area (m) 2 h ge Enthalpy of vapor phase in evaporator (J·kg) -1 h in,e Enthalpy of vapor phase in evaporator (J·kg) -1 ;

[0136] a ie Evaporator inlet heat transfer coefficient, D ie Evaporator inlet diameter; l e Length of the two-phase region of the evaporator; T we Evaporator tube wall temperature; T re Evaporator saturated with refrigerant;

[0137] C pSpecific heat capacity (J·(kg·K)) -1 a oe External heat transfer coefficient of evaporator W·(m 2 ·K) -1 A oe Evaporator outer surface area (m) 2 ,T ae Evaporator ambient temperature; a ie Evaporator inlet heat transfer coefficient W·(m 2 ·K) -1 a iesh Heat transfer coefficient W·(m) inside the superheated zone of the evaporator 2 ·K) -1 , Average porosity of the two-phase region of the evaporator.

[0138] The subscripts e→c above refer to the condenser.

[0139] The air-side heat transfer coefficient α of the evaporator and condenser oe (a oc ) and condenser air-cooled fan speed N c The correlation can be fitted as follows:

[0140]

[0141] a oc : External heat transfer coefficient of condenser W·(m 2 ·K) -1 N c : Condenser fan speed, Empirical coefficient.

[0142] The cryotherapy chamber can be considered as a controlled, enclosed physical space, and its heat load Q0 includes: chamber body heat load Q1, heat leakage when entering or leaving the chamber Q2, heat leakage in the chamber space Q3, heat exchange between the chamber and the environment through radiation Q4, and cryotherapy load within the cryotherapy chamber Q5.

[0143] 1) Cabin heat load Q1

[0144] Q1=K·A·a(t w -t n )

[0145]

[0146] K is the heat transfer coefficient of the cabin structure; A is the surface area of ​​the cabin structure; t w The ambient temperature outside the cabin; t n Temperature inside the cabin;

[0147] a w The heat transfer coefficient on the outside of the cabin; a nλ is the heat transfer coefficient inside the cabin; λ is the thermal conductivity of the cabin's insulation material; δ is the thickness of the insulation material.

[0148] 2) Heat loss when opening the hatch for entering or exiting the cabin (Q2)

[0149] Assuming that each time the cryotherapy chamber opens and closes, the cold air inside is completely replaced by warm air from the outside when the patient enters or exits the chamber, the heat loss during the opening and closing of the chamber can be calculated as Q2:

[0150]

[0151] Δh=h AT,AH -h CBT,CBH Formula 13

[0152] V CB The volume of the cryotherapy chamber is m 3 n represents the number of patients entering and exiting the cryotherapy chamber per hour, with a maximum of 3; v a Let m be the specific volume of air. 3 / kg.

[0153] Δh represents the temperature and humidity difference (kJ / kg) when the ambient air from outside the cabin enters the room and reaches the specified cryotherapy temperature; AT,AH Specific enthalpy (kJ / kg) for ambient air temperature AT and humidity AH outside the cabin; h CBT,CBH , where is the specific enthalpy (kJ / kg) at cabin air temperature CBT and humidity CBH.

[0154] 3) Heat loss in the cabin space Q3

[0155] Gaps at the connections and joints of the various structural components of the cabin can cause cold air to leak out. Since the size and location of these gaps are random events, this portion of the leaked cold air cannot be calculated. Therefore, we take 10%–20% of the cooling load Q1 lost by the cryotherapy cabin; in this calculation, we use 15%.

[0156] Q3=0.15×Q1 Formula 14

[0157] 4) Heat exchange between the cabin and the environment via radiation Q4

[0158]

[0159] ε is the emissivity of the outer surface of the cabin; C0 is the blackbody emissivity; take C0 = 5.67 W / (m²). 2 K 4 A ow t is the outer surface area of ​​the box. a For ambient temperature, t ow This refers to the outer surface temperature of the enclosure.

[0160] 5) Cryotherapy load Q5 in the cryotherapy chamber

[0161] The cryotherapy load within the cryotherapy chamber is the heat load that the patient's body dissipates into the chamber during treatment. Patients obtain the energy necessary for survival through metabolism; in a static state, most of this energy is used to maintain a constant body temperature, and then the heat is dissipated into the cryotherapy chamber.

[0162] Only when the patient's heat production equals the body's heat dissipation can the body temperature be maintained within a certain range. This can be described using the heat balance equation:

[0163] H = MWFCE

[0164] M represents the patient's metabolic rate (W / m²). 2 W represents the mechanical work performed by the patient (in W / m). 2 F represents the radiative heat exchange between the patient and the cabin environment (W / m²). 2 C represents the convective heat transfer between the patient and the cabin environment (W / m²). 2 E represents the heat loss through sweat evaporation and respiration (W / m²). 2 H represents the accumulated heat of the human body in W / m³. 2 .

[0165] a. The metabolic rate M of the above-mentioned patients refers to the heat generated per unit time by the human body surface area, which is usually determined by the amount of human activity and can be determined by the amount of oxygen consumed by the human body during respiration:

[0166]

[0167] R B Respiratory entropy; A D The surface area of ​​the human body (m) 2 ; This represents the volume of oxygen consumed per unit time (mL / s).

[0168] b. The above W represents the power of the patient's muscle tissue, which can be calculated using mechanical efficiency η:

[0169]

[0170] The empirical value η ranges from 0 to 0.2. Since the patient does not have mechanical movement inside the cryo-fluid chamber, it can be approximated as W = 0.

[0171] c. E above represents the heat dissipation caused by evaporation from the patient's skin surface, which consists of sensory heat dissipation and non-sensory heat dissipation. Sensory heat dissipation is used in high-temperature environments, while non-sensory heat dissipation is the main type used in the low-temperature environment of the cryotherapy chamber.

[0172] E = 3.05 × 10 -3 ×(254t sk -3335-P a ) Formula 18

[0173] t sk Skin temperature; P a The water vapor pressure (kPa) inside the cryotherapy chamber where the patient is located.

[0174] d. The above F represents the radiative heat loss from the patient and the cabin:

[0175] F = A P εσ(T sk 4 -T a 4 ) / A D Formula 19

[0176] A P =f ef f cl A D Formula 20

[0177] A P ε represents the effective radiation area of ​​the patient's clothing; ε represents the average emissivity of the outer surface of the patient's clothing; T represents... sk The average temperature of the patient's body surface; T a For ambient temperature; f ef f is the effective radiation area coefficient for the patient. cl This refers to the area coefficient of the patient's clothing.

[0178] e. The above C refers to convective heat exchange between the patient and the air inside the cabin:

[0179] C = h c f cl (T sk -T a ) Formula 21

[0180] T sk The average temperature of the patient's body surface; T a For ambient temperature; f cl h is the area coefficient of the patient's clothing. c is the convective heat transfer coefficient.

[0181] When the patient's body surface temperature remains constant, the body's accumulated heat H→0, and we have:

[0182] H = MWFCE

[0183] MWFCE=0

[0184] Furthermore, since the patient experiences virtually no mechanical movement within the cryotherapy chamber, taking W=0, we have:

[0185] MFCE=0

[0186] Therefore, all the metabolic waste produced by the patient is lost into the cryotherapy chamber. That is, when the temperature inside the cryotherapy chamber is constant at the treatment temperature, we have:

[0187] Q5 = M = F + C + E

[0188] The heat load Q0 inside the cabin is the same as the cooling load of the refrigeration system. Therefore, Q0 = Q1 + Q2 + Q3 + Q4 + Q5.

[0189] To simplify the calculations, all surfaces inside the cryotherapy chamber are considered flat. Therefore, the temperature equilibrium of the air inside the chamber is:

[0190]

[0191] Refrigeration system cooling load:

[0192]

[0193] a oe Evaporator outlet heat transfer coefficient W·(m 2 ·K) -1 A oe Evaporator outer surface area (m) 2 ;T ne Surface temperature inside the cryotherapy chamber; T we Temperature of the outer surface of the cryotherapy chamber.

[0194] 2. Control Model

[0195] like Figure 3 As shown, the evaporator of the cryotherapy chamber's cooling system absorbs the heat load inside the chamber, maintaining the temperature inside the chamber at the treatment temperature. The air temperature inside the chamber is T. a Evaporator pressure P e Evaporator two-phase length l e Evaporator tube wall equivalent temperature T we Evaporator pressure P c Equivalent temperature T of condenser tube wall wc As the state variable of the system, the state vector of the control model is:

[0196] X = [l e ,P e ,T we ,P c ,T wc ,T a ] Formula 24

[0197] The system input is the compressor speed N. comp Electronic expansion valve opening A s,v Condenser fan frequency N c Control input vector:

[0198] U = [N]comp A s,v N c ] Formula 25

[0199] The output of the above control model is the cabin temperature T. a Combining formulas 1 to 23 above, the control model is introduced into the state space:

[0200]

[0201] 3. Predictive model adjustment mechanism

[0202] The model in Formula 26 above is a highly complex nonlinear model. To linearize it, under steady-state conditions inside the chamber, the heat load of the cryotherapy chamber is constant, and its linear discrete model is as follows:

[0203]

[0204] u=A(x0,u0)x+B(x0,u0)u Formula 27

[0205]

[0206] x0 and u0 are the values ​​of the state vector and control input vector at the steady-state working fluid point, respectively; x and u are the smaller dynamic deviations from x0 and u0, respectively.

[0207] For the variable T of the cryotherapy chamber's refrigeration system a The objective function of the finite-time optimization problem is to be as close as possible to the set value, and to achieve high overall system energy efficiency, as shown in the attached figure of the above abstract. At each sampling time, the objective function is:

[0208]

[0209] Where N is the prediction range, i.e. the rolling duration M in 2.1 above; u is the control variable to be optimized, and e(k) is the difference between the system output and the output reference: e(k) = y(k) - r(k).

[0210] By establishing a refrigeration cycle and a heat load model for the cryotherapy chamber, variables can be predicted and optimized for a period of time in the future. The operating parameters of the system under different working conditions can be adjusted, and feedback correction can be introduced through sensors to reduce prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

[0211] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the present invention without departing from its novel spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A status monitoring and regulation mechanism for a cryotherapy chamber refrigeration system, characterized in that, Includes the following steps; S1; Cooling prediction; Establish a model of the operating parameters of the refrigeration system of the cryotherapy chamber and a model of the heat load inside the cryotherapy chamber. The main operating parameter of the refrigeration system of the cryotherapy chamber is the cooling capacity of the refrigeration system. By obtaining historical information from the model, future values ​​are predicted, thereby determining the target prediction setpoint that needs to be adjusted in the future. S2； Rolling optimization involves continuously updating boundary conditions for the refrigeration system and performing online optimization. The process involves optimizing the system state based on performance indicators at each sampling time. The refrigeration system executes the setting instructions for the first time period only, adjusting its operating parameters promptly. The rolling optimization time window shifts forward at the next sampling time. Simultaneously, interval optimization is performed. Intervals are used to represent the fluctuation range of uncertainties between the heat load in the cryotherapy chamber and the refrigeration capacity of the refrigeration system. The upper and lower limits selected when building the model encompass all possibilities within the defined interval range of uncertainties to characterize comprehensive uncertainties. S3; Predictive model adjustment; Based on the historical information of the refrigeration cycle and the heat load of the cryotherapy chamber obtained in step 1 above, the variables in the future period are predicted and optimized, the operating parameters of the system under different working conditions are adjusted, and feedback correction is introduced through sensors to reduce the interference of prediction errors and fluctuations, so as to achieve optimal control and improve the overall energy efficiency of the refrigeration system.

2. The status monitoring and adjustment mechanism of the cryotherapy chamber refrigeration system according to claim 1, characterized in that, The heat load inside the cryotherapy chamber in S1 includes the heat load of the chamber body, the heat leakage when entering and exiting the chamber, the heat leakage of the chamber space, the radiative heat exchange between the chamber body and the environment, and the cryotherapy load inside the cryotherapy chamber; the evaporator of the cryotherapy chamber refrigeration system absorbs the heat load inside the chamber and maintains the temperature inside the chamber at the treatment temperature.

3. The status monitoring and adjustment mechanism of the cryotherapy chamber refrigeration system according to claim 1, characterized in that, The cooling capacity of the refrigeration system in S1 is obtained by analyzing the energy balance and refrigeration cycle of the cryotherapy chamber. The refrigerant enters the evaporator in a low-pressure, low-temperature two-phase flow, absorbs heat and is transformed into superheated vapor. The vapor refrigerant is compressed into high-temperature, high-pressure vapor by the compressor and enters the condenser. In the condenser, the refrigerant is cooled and becomes a two-phase flow, and finally becomes a high-pressure liquid refrigerant. Subsequently, the pressure drops to the evaporation pressure after being throttled by the electronic expansion valve, transforming into a low-pressure, low-temperature two-phase flow to complete the refrigeration cycle.

4. The status monitoring and adjustment mechanism of the cryotherapy chamber refrigeration system according to claim 1, characterized in that, The model established in S3 is a control model, which analyzes all possibilities within the set range of uncertain variables, predicts the heat load in the cryotherapy chamber and the cooling capacity of the mass system, and the evaporator of the cryotherapy chamber's cooling system absorbs the heat load in the chamber to maintain the temperature in the chamber at the treatment temperature.

5. The status monitoring and adjustment mechanism of the cryotherapy chamber refrigeration system according to claim 4, characterized in that, The highly complex nonlinear model obtained from the control model further yields the objective function of the finite time domain optimization problem. The main control system of the refrigeration system adjusts the operation at the next moment through the objective function.

6. The status monitoring and adjustment mechanism of the cryotherapy chamber refrigeration system according to claim 5, characterized in that, Use a model with known parameters to design a controller that acts on the refrigeration system.

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