Simplified heat and moisture model of passive regenerative drying bed and its parameter identification and verification method
By constructing a simplified thermal and humidity model of a passive regenerative drying bed and combining it with experimental and validation datasets, the problem of insufficient dynamic thermal and humidity coupling transfer characteristics of the passive regenerative drying bed under alternating day and night operating conditions was solved. This simplified the model and identified the parameters, improving the applicability of the model and the robustness of the parameters.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUAZHONG UNIV OF SCI & TECH
- Filing Date
- 2026-01-26
- Publication Date
- 2026-06-16
AI Technical Summary
In existing research and engineering applications, there is insufficient understanding of the dynamic heat and moisture coupling transfer characteristics of passive regeneration drying beds under alternating day and night operating conditions. There is a lack of unified and reusable modeling and verification paths, which limits the optimization of material selection and the assessment of system energy efficiency. Furthermore, existing models have large computational loads or insufficient generalization ability.
A simplified thermal and humidity model of a passive regenerative drying bed and its parameter identification and verification method are provided. By constructing a simplified thermal and humidity model, the thermal and humidity characteristics of the passive regenerative drying bed are collected and verified in real time using experimental and verification datasets, thereby reducing model complexity and improving the accuracy and applicability of parameter identification.
While retaining the key thermal-wet coupling mechanism, the model complexity is reduced, which facilitates parameter identification and engineering applications, improves the applicability and transferability of the model under different boundary conditions, and enhances the robustness and reliability of the parameters.
Smart Images
Figure CN122217656A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of performance testing of building dehumidification systems, specifically involving a simplified thermal and humidity model of a passive regeneration drying bed and its parameter identification and verification method. Background Technology
[0002] Indoor humidity significantly impacts human comfort and health. Excessive indoor humidity promotes the growth of microorganisms such as dust mites and bacteria, and may increase the volatilization of pollutants like VOCs and ozone from indoor building materials and furniture. It also negatively affects the lifespan of building envelopes, data preservation, and the quality of manufacturing processes. This problem is particularly pronounced in hot-summer, cold-winter regions of my country, where relative humidity is high year-round, averaging around 70%–80%, and reaching even higher levels during transitional seasons.
[0003] Current household dehumidification mainly relies on condensation dehumidification, which typically depends on the evaporator of the air conditioning system to lower the air temperature below the dew point, causing water vapor to condense and precipitate. Throughout the building's life cycle, heating and air conditioning account for a high proportion of energy consumption; the cooling capacity consumed by dehumidification during the air conditioning season accounts for approximately 30% to 50% of the total air conditioning energy consumption, and this proportion can be even higher during transitional seasons. At the same time, the demand for supply air temperature often leads to reheating processes, causing additional energy waste.
[0004] In recent years, passive building energy-saving technologies have attracted attention. Among them, passive regenerative dehumidifiers can dehumidify indoor air at night by adsorbing water vapor in the circulating air, and passively regenerate the desiccant during the day by using solar energy heating, and rely on the chimney effect to carry away the desorbed water vapor; the alternating day and night operation is expected to reduce indoor moisture load and reduce dehumidification energy consumption.
[0005] However, existing research and engineering applications still lack sufficient understanding of the dynamic heat and moisture coupling transfer characteristics of this type of passive regeneration drying bed under alternating day and night operating conditions. On the one hand, performance evaluation and analysis often focus on single operating conditions or static indicators, lacking a systematic dynamic heat and moisture coupling characterization oriented towards cyclic operating conditions. On the other hand, there is a lack of unified and reusable modeling and verification paths for the heat and moisture response laws under different boundary conditions such as different solar irradiance intensities and different ambient humidity, which in turn restricts the optimization of material selection and the evaluation of system energy efficiency.
[0006] Furthermore, from a modeling perspective, passive regenerative drying beds involve strong coupling of processes such as heat collection, heat transfer, heat absorption / desorption, and moisture transfer. Using high-precision models (e.g., CFD models) typically requires sophisticated boundary conditions, material parameters, and interface transfer parameters, resulting in significant computational demands. Conversely, some simplified models may weaken the heat-moisture coupling or lack clear parameter identification and independent verification procedures, leading to insufficient generalization and usability under different boundary conditions. Therefore, there is an urgent need for a simplified heat-moisture coupling transfer model and its experimental verification method that can characterize key heat-moisture coupling transfer processes with a small number of equivalent parameters while ensuring the necessary physical mechanisms, and can combine experimental data to complete parameter identification and verification. Summary of the Invention
[0007] The technical problem to be solved by this invention is that passive regenerative dryers have significant heat-humidity coupling, complex heat and humidity transfer paths, and difficulty in obtaining parameters and independent verification data support during the moisture absorption and regeneration conditions. The invention provides a simplified heat and humidity model for passive regenerative dryers and a method for parameter identification and verification. Through the process of model construction, multi-condition data acquisition, parameter identification, and independent data verification, a set of model parameters that can be used to calculate the changes of outlet air temperature and relative humidity and bed mass over time are obtained, and the model is verified.
[0008] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A simplified thermal and humidity model of a passive regenerative drying bed, based on a passive regenerative drying bed thermal and humidity characteristic testing device, the passive regenerative drying bed thermal and humidity characteristic testing device includes a passive regenerative drying bed, a solar radiation simulation component, an environmental chamber, a temperature and humidity air conditioner, a gas flow control component, a data acquisition component, and a main control module. The passive regenerative drying bed, the solar radiation simulation component, the gas flow control component, and the data acquisition component are respectively arranged in the environmental chamber. The passive regenerative drying bed is inclined. The solar radiation simulation component is arranged above the passive regenerative drying bed. The data acquisition component is arranged inside and below the passive regenerative drying bed. The gas flow control component is arranged at the higher end of the passive regenerative drying bed. The air inlet and air outlet of the temperature and humidity air conditioner are respectively connected to the environmental chamber and are used to maintain the air temperature and relative humidity in the environmental chamber within a preset range. The main control module is electrically connected to the data acquisition component.
[0009] The solar radiation simulation component is used to simulate the sun's light emission and radiation onto the passive regeneration drying bed under regeneration conditions;
[0010] The temperature and humidity air conditioner is used to regulate and maintain a constant air temperature and relative humidity inside the environmental chamber.
[0011] The gas flow control component is used to adjust the airflow channel of the passive regeneration drying bed according to the regeneration or moisture absorption conditions.
[0012] The data acquisition component is used to collect experimental and verification datasets in real time corresponding to the boundary conditions of the passive regeneration drying bed under different moisture absorption and regeneration conditions.
[0013] The main control module is used to construct a simplified thermal and humidity model of the passive regeneration drying bed, identify target parameters of the simplified thermal and humidity model based on the experimental dataset, and verify the simplified thermal and humidity model after target parameter identification based on the verification dataset.
[0014] The simplified thermal and humidity model is specifically as follows:
[0015]
[0016]
[0017]
[0018]
[0019] Among them, T amb The ambient temperature is equal to the air temperature at the inlet of the airflow channel of the passive regeneration dryer; T p1 T p2 T p3 These are the temperature nodes for each sub-region of the frame; T d1 T d2 T d3 These represent the temperature nodes of each sub-region of the desiccant layer; R1, R2, R3, R4, and R5 represent the equivalent thermal resistance between two adjacent temperature nodes; T a Q represents the node representing the average air temperature along the airflow channel. solar Q represents the equivalent heat gain of solar radiation on the upper surface of the frame. sorp,1 Q sorp,2 Q sorp,3 The heat source for the adsorption / desorption of the desiccant layer; Q a Sensible heat gained from air; T out The air temperature at the outlet of the airflow channel; c is the air mass flow rate; p,a Δh is the specific heat capacity of air at constant pressure. sorp This is the adsorption enthalpy of water vapor on the desiccant, taken as a positive value;
[0020]
[0021]
[0022]
[0023] in, Y is the dry air mass flow rate; in With Y out These represent the air moisture content at the inlet and outlet of the airflow channel of the passive regeneration dryer, respectively; q m,i M represents the net moisture transfer between the i-th desiccant node and the air; s,i Y represents the effective dry mass of the desiccant at the i-th desiccant node. eq,i Y represents the interfacial equilibrium moisture content, determined by both the desiccant moisture content and temperature; W0 is the saturated adsorption capacity parameter; W1, W2, and W3 are the dry basis moisture content state variables of each sub-region of the desiccant layer, respectively. eq,1 Y eq,2 Y eq,3 These represent the interface equilibrium moisture content nodes corresponding to the dry basis moisture content state variables W1, W2, and W3, respectively; E is the characteristic energy parameter; n is the exponential parameter; R... g p is the gas constant. ws (T) represents the partial pressure of saturated water vapor at air temperature T; p v,eq,i To and (W) i , T d,i The corresponding equilibrium water vapor partial pressure; p atm Atmospheric pressure.
[0024] Based on the above technical solution, the present invention can be further improved as follows:
[0025] Further: The passive regeneration drying bed includes a frame and an insulation layer. The frame is hollow inside, and the inner walls of the frame are coated with desiccant layers, forming airflow channels within the frame. The top of the frame faces the solar radiation simulation component, and the insulation layer is located at the bottom of the frame.
[0026] Further: The data acquisition component includes an electronic scale, an anemometer, an inlet wireless temperature and humidity sensor, and an outlet wireless temperature and humidity sensor. The passive regeneration drying bed is tilted and mounted on the electronic scale. The anemometer is mounted in the airflow channel and used to measure the airflow velocity. The inlet wireless temperature and humidity sensor and the outlet wireless temperature and humidity sensor are respectively mounted at the inlet and outlet of the airflow channel of the passive regeneration drying bed. The electronic scale, anemometer, inlet wireless temperature and humidity sensor, and outlet wireless temperature and humidity sensor are electrically connected to the main control module.
[0027] Further: The gas flow control component includes an airflow switching valve, an air duct, and a fan with adjustable speed. The air duct is located at the higher end of the passive regeneration drying bed. The airflow switching valve is located at the connection between the air duct and the passive regeneration drying bed, and the airflow switching valve can switch the airflow channel so that the passive regeneration drying bed is connected to the air duct, or the passive regeneration drying bed is directly connected to the environmental chamber. The fan is located inside the air duct.
[0028] Furthermore, the solar radiation simulation component includes a lamp array and a controller. The lamp array is positioned above the passive regeneration drying bed, and the controller is electrically connected to the lamp array and can continuously adjust the radiation intensity of the lamp array.
[0029] Further: The main control module constructs a simplified thermal and humidity model of the passive regeneration drying bed, identifies target parameters of the simplified thermal and humidity model based on the experimental dataset, and verifies the simplified thermal and humidity model after target parameter identification based on the verification dataset. The specific implementation of this is as follows:
[0030] A simplified thermo-humidity model of a passive regeneration drying bed was constructed.
[0031] Based on the data acquisition component, experimental datasets and verification datasets corresponding to different boundary conditions under the moisture absorption and regeneration conditions are collected in real time. The experimental datasets and verification datasets include the mass of the passive regeneration drying bed, the control temperature and relative humidity at the outlet of the airflow channel of the passive regeneration drying bed; the boundary conditions include one or more of the following: air temperature and relative humidity at the inlet of the airflow channel, airflow velocity, and solar radiation intensity.
[0032] The target parameters in the simplified thermal and humidity model are identified using the experimental dataset.
[0033] The verification dataset is input into the simplified thermal and humidity model to calculate the air temperature, relative humidity, and mass of the passive regeneration dryer at the airflow channel outlet. The simplified thermal and humidity model is then verified.
[0034] Further: The specific implementation of the simplified thermo-humidity model for constructing the passive regeneration drying bed is as follows:
[0035] The framework of the passive regeneration drying bed is divided into sub-regions with temperature nodes T. p1 T p2 T p3 The desiccant layer is divided into sub-regions with temperature nodes T. d1 T d2 T d3 And an air temperature node T is set in the airflow channel. aAnd set equivalent thermal resistances R1, R2, R3, R4, and R5 between two adjacent temperature nodes;
[0036] The desiccant layer is divided into three state variables: dry basis moisture content W1, W2, and W3, which correspond to the interface equilibrium moisture content node Y, respectively. eq,1 Y eq,2 Y eq,3 An air humidity node Y is set in the airflow channel of the passive regeneration drying bed. a And at the air humidity node Y a With the interface equilibrium moisture content node Y eq,1 Y eq,2 Y eq,3 Equivalent moisture transfer resistance R is set between them respectively. m,1 R m,2 R m,3 .
[0037] The present invention also provides a method for parameter identification and verification of a simplified thermo-humidity model based on the aforementioned passive regeneration drying bed, comprising the following steps:
[0038] Based on the data acquisition component, experimental datasets and verification datasets corresponding to different boundary conditions under the moisture absorption and regeneration conditions are collected in real time. The experimental datasets and verification datasets include the mass of the passive regeneration drying bed, the air temperature and relative humidity at the outlet of the airflow channel of the passive regeneration drying bed; the boundary conditions include one or more of the following: air temperature and relative humidity at the inlet of the airflow channel, airflow velocity, and solar radiation intensity.
[0039] The target parameters in the simplified thermal and humidity model are identified using the experimental dataset.
[0040] Based on the target parameters, the verification dataset is input into the simplified thermal and humidity model to calculate the air temperature, relative humidity, and mass of the passive regeneration dryer at the airflow channel outlet. The simplified thermal and humidity model is then verified.
[0041] Based on the above technical solution, the present invention can be further improved as follows:
[0042] Further: Identifying the target parameters in the simplified thermo-humidity model using the experimental and validation datasets specifically includes the following steps:
[0043] The boundary conditions of the experimental dataset are input into the simplified thermal and humidity model to obtain the corresponding calculated mass value of the passive regeneration drying bed, the calculated air temperature at the outlet of the airflow channel of the passive regeneration drying bed, and the calculated relative humidity value.
[0044] An objective function is established using the calculated mass values of the passive regeneration dryer, the calculated air temperature at the outlet of the airflow channel of the passive regeneration dryer, the calculated relative humidity values, and the experimental dataset:
[0045]
[0046] Among them, t k The sampling time sequence is N; the number of sampling points is w. T w RH w m These are the weighting coefficients for air temperature, relative humidity, and mass, respectively. This is the calculated air temperature at the outlet of the airflow channel in the thermal-humidity coupling model. The measured air temperature at the outlet of the airflow channel in the thermal-humidity coupling model is given. This refers to the calculated relative humidity at the outlet of the airflow channel in the thermal-humidity coupling model. This represents the measured relative humidity at the outlet of the airflow channel in the thermal-humidity coupling model.
[0047] The target parameters in the simplified thermal and moisture model are determined by minimizing the objective function. These target parameters include equivalent heat transfer resistances R1, R2, R3, R4, and R5, and equivalent moisture transfer resistance R. m,1 R m,2 R m,3 .
[0048] Further: The verification of the simplified thermal and humidity model specifically includes the following steps:
[0049] The calculated values of air temperature, relative humidity, and mass of the passive regeneration dryer at the airflow channel outlet were matched one-to-one with the measured values of air temperature, relative humidity, and mass of the passive regeneration dryer at the airflow channel outlet in the validation dataset, according to the same time series, and the outlet air temperature T was calculated respectively. out (t), outlet relative humidity RH out The mean absolute error (MAE) of the mass m(t) of the passive regeneration dryer is calculated using the following formula:
[0050]
[0051] Where y is T out (t), RH out (t) or m(t), where N is the number of sampling points in the validation dataset;
[0052] When all the mean absolute errors meet the preset threshold, the prediction accuracy of the simplified thermal and humidity model is determined to meet the requirements.
[0053] The simplified thermo-humidity model of the passive regeneration drying bed of the present invention has the following beneficial effects:
[0054] 1. The heat and moisture transfer process of the passive regenerative drying bed is characterized by a limited number of temperature nodes, moisture content state variables, and equivalent thermal resistance / humidity resistance. This reduces the model complexity while retaining the key heat-moisture coupling mechanism, making it easier for parameter identification and engineering applications.
[0055] 2. Parameter identification aims to fit the changes in outlet air temperature, relative humidity and bed mass, so that the identification results can simultaneously constrain the thermal process and the wet process, thereby improving the applicability and consistency of the model under moisture absorption and regeneration conditions.
[0056] 3. Model validation is performed using a validation dataset that is independent of the experimental dataset used for parameter identification. The output results are compared and evaluated without changing the identified parameters, which is beneficial for objectively verifying the model's prediction accuracy.
[0057] 4. By repeatedly obtaining experimental and validation datasets under multiple different boundary conditions and using them for parameter identification and validation, the range of variation of boundary conditions such as inlet air temperature and relative humidity, airflow speed and solar radiation intensity can be covered, thereby enhancing the robustness and transferability of model parameters. Attached Figure Description
[0058] Figure 1 This is a schematic diagram of the passive regeneration drying bed thermal and moisture characteristics testing device according to an embodiment of the present invention under moisture absorption conditions;
[0059] Figure 2 This is a schematic diagram of the passive regeneration drying bed thermal and humidity characteristic testing device according to an embodiment of the present invention under regeneration conditions;
[0060] Figure 3 This is a simplified thermal and moisture model of a passive regenerative drying bed according to an embodiment of the present invention, showing the node division and equivalent heat / moisture transfer diagram.
[0061] The attached diagram lists the components represented by each number as follows:
[0062] 1. Passive regeneration drying bed; 2. Solar radiation simulation component; 3. Environmental chamber; 4. Temperature and humidity air conditioner; 5. Gas flow control component; 6. Data acquisition component; 7. Support frame; 8. Main control module.
[0063] 101. Frame; 102. Desiccant layer; 103. Airflow channel; 104. Insulation layer; 201. Light array; 202. Controller; 501. Airflow switching valve; 502. Air duct; 503. Fan; 601. Electronic scale; 603. Anemometer; 604. Inlet wireless temperature and humidity sensor; 605. Outlet wireless temperature and humidity sensor. Detailed Implementation
[0064] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.
[0065] like Figure 1 and Figure 2 As shown, a passive regenerative drying bed thermal and humidity characteristic testing device includes a passive regenerative drying bed 1, a solar radiation simulation component 2, an environmental chamber 3, a temperature and humidity air conditioner 4, a gas flow control component 5, a data acquisition component 6, and a main control module 8. The passive regenerative drying bed 1, the solar radiation simulation component 2, the gas flow control component 5, and the data acquisition component 6 are respectively arranged in the environmental chamber 3. The passive regenerative drying bed 1 is inclined. The solar radiation simulation component 2 is arranged above the passive regenerative drying bed 1. The data acquisition component 6 is arranged inside and below the passive regenerative drying bed 1. The gas flow control component 5 is arranged at the higher end of the passive regenerative drying bed 1. The air inlet and air outlet of the temperature and humidity air conditioner 4 are respectively connected to the environmental chamber 3 and are used to maintain the air temperature and relative humidity in the environmental chamber 3 within a preset range. The main control module 8 is electrically connected to the data acquisition component 6.
[0066] The solar radiation simulation component 2 is used to simulate the sun's light emission and radiate it to the passive regeneration drying bed 1 under regeneration conditions;
[0067] The temperature and humidity air conditioner 4 is used to regulate and maintain a constant air temperature and relative humidity in the environmental chamber 3.
[0068] The gas flow control component 5 is used to adjust the airflow channel of the passive regeneration drying bed 1 according to the regeneration or moisture absorption conditions.
[0069] The data acquisition component 6 is used to collect experimental datasets and verification datasets corresponding to different boundary conditions of the passive regeneration drying bed 1 under moisture absorption and regeneration conditions in real time.
[0070] The main control module 8 is used to construct a simplified thermal and humidity model of the passive regeneration drying bed, identify target parameters of the simplified thermal and humidity model based on the experimental dataset, and verify the simplified thermal and humidity model after target parameter identification based on the verification dataset.
[0071] In one or more embodiments of the present invention, the passive regeneration drying bed 1 is placed inside the environmental chamber 3, which is connected to the temperature and humidity air conditioner 4 to maintain constant temperature and humidity boundary conditions within the environmental chamber 3. The passive regeneration drying bed 1 includes a frame 101 and a heat insulation layer 104. The frame 101 is hollow inside, and its inner walls are coated with desiccant layers 102, forming airflow channels 103 within the frame 101. The top of the frame 101 faces the solar radiation simulation component 2, and the heat insulation layer 104 is located at the bottom of the frame 101 to reduce heat transfer between the passive regeneration drying bed 1 and the environment, reduce interference from bottom heat dissipation on the drying bed's heat and humidity process, and improve the controllability and repeatability of experimental boundary conditions. Here, the desiccant layer 102 is formed by filling with silica gel desiccant.
[0072] In one or more embodiments of the present invention, the data acquisition component 6 includes an electronic scale 601, an anemometer 603, an inlet wireless temperature and humidity sensor 604, and an outlet wireless temperature and humidity sensor 605. The passive regeneration drying bed 1 is inclinedly disposed on the electronic scale 601. The anemometer 603 is disposed in the airflow channel 103 and is used to measure the airflow velocity. The inlet wireless temperature and humidity sensor 604 and the outlet wireless temperature and humidity sensor 605 are respectively disposed at the inlet and outlet of the airflow channel 103 of the passive regeneration drying bed 1 and are used to measure the air temperature and relative humidity at the inlet and outlet. The electronic scale 601, the anemometer 603, the inlet wireless temperature and humidity sensor 604, and the outlet wireless temperature and humidity sensor 605 are electrically connected to the main control module 8.
[0073] In practice, the passive regeneration drying bed 1 is installed on the support 7 at a preset tilt angle. The support 7 is placed on the electronic scale 601, which is electrically connected to the main control module 8 to realize continuous acquisition of the total mass of the passive regeneration drying bed 1.
[0074] In one or more embodiments of the present invention, the gas flow control component 5 is vertically disposed below the top of the passive regeneration drying bed 1, and includes an airflow switching valve 501, an air duct 502, and a fan 503 with adjustable speed. The air duct 502 is disposed at the higher end of the passive regeneration drying bed 1, and the airflow switching valve 501 is disposed at the connection between the air duct 502 and the passive regeneration drying bed 1. The airflow switching valve 501 can switch the airflow channel so that the passive regeneration drying bed 1 is connected to the air duct 502, or the passive regeneration drying bed 1 is directly connected to the environmental chamber 3. The fan 503 is disposed inside the air duct 502.
[0075] In one or more embodiments of the present invention, the solar radiation simulation component 2 is disposed directly above the passive regeneration drying bed 1, and includes a lamp array 201 and a controller 202. The lamp array 201 is disposed above the passive regeneration drying bed 1, and the controller 202 is electrically connected to the lamp array 201 and can continuously adjust the radiation intensity of the lamp array 201. Here, the light source arrangement of the lamp array 201 is preferably a hexagonal array, and the controller 202 is a silicon controlled rectifier (SCR) voltage regulator for controlling the brightness of the lamp array.
[0076] In one or more embodiments of the present invention, the main control module 8 constructs a simplified thermal and humidity model of the passive regeneration drying bed, identifies target parameters of the simplified thermal and humidity model based on the experimental dataset, and verifies the simplified thermal and humidity model after target parameter identification based on the verification dataset.
[0077] A simplified thermo-humidity model of a passive regeneration drying bed was constructed.
[0078] Based on the data acquisition component 6, experimental datasets and verification datasets corresponding to different boundary conditions under the moisture absorption and regeneration conditions are collected in real time. Both the experimental datasets and verification datasets include the mass m(t) of the passive regeneration drying bed 1 and the air temperature T at the outlet of the airflow channel 103 of the passive regeneration drying bed 1. out (t) and relative humidity RH out (t); the boundary conditions include the air temperature T at the inlet of the airflow channel 103. in (t) and relative humidity RH in (t), airflow velocity u(t), and solar radiation intensity Q solar One or more of the following;
[0079] The target parameters in the simplified thermal-humidity model are identified using the experimental dataset. These target parameters include at least the equivalent heat transfer resistances R1, R2, R3, R4, and R5, and the equivalent moisture transfer resistance R. m,1 R m,2 R m,3 ;
[0080] The verification dataset is input into the simplified thermal and humidity model. Without changing the identified target parameters, the calculated values of air temperature, relative humidity and mass of the passive regeneration drying bed 1 at the outlet of the airflow channel 103 are obtained, and the simplified thermal and humidity model is verified.
[0081] It should be noted that, in one or more embodiments of the present invention, the data acquisition component 6 acquires experimental datasets and verification datasets corresponding to different boundary conditions under moisture absorption and regeneration conditions in real time, including the following processes:
[0082] like Figure 1 As shown, under moisture absorption conditions:
[0083] (i) Turn on the temperature and humidity air conditioner 4 to keep the air temperature and relative humidity in the environmental chamber 3 constant;
[0084] (ii) Adjust the airflow switching valve 501 to connect the passive regeneration drying bed 1 with the air duct 502, control the fan 503 to work, and control the solar radiation simulation component 2 to not work;
[0085] (iii) The readings of the electronic scale 601, the inlet air temperature and relative humidity, the outlet air temperature and relative humidity, and the airflow velocity in the airflow channel 103 are recorded synchronously by the data acquisition component 6.
[0086] (iv) By adjusting the set value of the temperature and humidity air conditioner 4 or the rotation speed of the fan 503, at least one boundary condition is changed, the boundary condition including at least one or more of the inlet air temperature and relative humidity, and airflow speed, and the above (i)-(iii) are repeated to obtain multiple sets of experimental datasets and verification datasets under different boundary conditions.
[0087] like Figure 2 As shown, in the regeneration condition:
[0088] (i) Turn on the temperature and humidity air conditioner 4 to keep the air temperature and relative humidity in the environmental chamber 3 constant;
[0089] (ii) Adjust the airflow switching valve 501 to directly connect the passive regeneration drying bed 1 with the environmental chamber 3, control the fan 503 to not work, and control the solar radiation simulation component 2 to work with the radiation intensity constant at Qsolar;
[0090] (iii) The readings of the electronic scale 601, the air temperature and relative humidity at the inlet, the air temperature and relative humidity at the outlet, and the airflow velocity in the airflow channel 103 are recorded synchronously by the data acquisition component 6.
[0091] (iv) By adjusting the set value of the constant temperature and humidity air conditioner 4, the rotation speed of the fan 503, or the brightness of the solar radiation simulation component 2, at least one boundary condition is changed, the boundary condition including at least one or more of the inlet air temperature and relative humidity, airflow speed and solar radiation intensity, and the above (i)-(iii) are repeated to obtain multiple sets of experimental datasets and verification datasets under different boundary conditions.
[0092] like Figure 3 As shown, in one or more embodiments of the present invention, the specific implementation of the simplified thermo-humidity model for constructing the passive regeneration drying bed is as follows:
[0093] The frame 101 of the passive regeneration drying bed 1 is divided into sub-region temperature nodes T. p1 T p2 T p3 The desiccant layer 102 is divided into sub-regions with temperature nodes T. d1 T d2 T d3 And an air temperature node T is set in the airflow channel 103. a And set equivalent thermal resistances R1, R2, R3, R4, and R5 between two adjacent temperature nodes;
[0094] The desiccant layer 102 is divided into dry basis moisture content state variables W1, W2, and W3, which correspond to the interface equilibrium moisture content node Y, respectively. eq,1 Y eq,2 Y eq,3 An air humidity node Y is provided in the airflow channel 103 of the passive regeneration drying bed 1. a And at the air humidity node Y a With the interface equilibrium moisture content node Y eq,1 Y eq,2 Y eq,3 Equivalent moisture transfer resistance R is set between them respectively. m,1 R m,2 R m,3 .
[0095] This invention also provides a simplified thermal and humidity model for a passive regeneration dryer. Based on the thermal and humidity characteristic testing device for the passive regeneration dryer, the simplified thermal and humidity model is specifically as follows:
[0096]
[0097]
[0098]
[0099]
[0100] Among them, T amb The ambient temperature is equal to the air temperature at the inlet of the airflow channel 103 of the passive regeneration drying bed 1; T p1 T p2 T p3 These are the temperature nodes for each sub-region of frame 101; T d1 T d2 T d3 These represent the temperature nodes of each sub-region of the desiccant layer 102; R1, R2, R3, R4, and R5 represent the equivalent thermal resistance between two adjacent temperature nodes; T a Q represents the node representing the average air temperature along the airflow channel 103.solar Q represents the equivalent heat gain from solar radiation on the upper surface of frame 101. sorp,1 Q sorp,2 Q sorp,3 The heat source for the absorption / desorption of the desiccant layer 102; Q a Sensible heat gained from air; T out The air temperature at the outlet of airflow channel 103; c is the air mass flow rate; p,a Δh is the specific heat capacity of air at constant pressure. sorp This is the adsorption enthalpy of water vapor on the desiccant, taken as a positive value;
[0101]
[0102]
[0103]
[0104] in, Y is the dry air mass flow rate; in With Y out The moisture content of the air at the inlet and outlet of the airflow channel 103 of the passive regeneration drying bed 1 are respectively; q m,i M represents the net moisture transfer between the i-th desiccant node and the air; s,i Y represents the effective dry mass of the desiccant at the i-th desiccant node. eq,i W0 is the interfacial equilibrium moisture content determined by both the moisture content of the desiccant and temperature; W1, W2, and W3 are the saturated adsorption capacity parameters; W1, W2, and W3 are the dry basis moisture content state variables of each sub-region of the desiccant layer 102, respectively. eq,1 Y eq,2 Y eq,3 These represent the interface equilibrium moisture content nodes corresponding to the dry basis moisture content state variables W1, W2, and W3, respectively; E is the characteristic energy parameter; n is the exponential parameter; R... g p is the gas constant. ws (T) represents the partial pressure of saturated water vapor at air temperature T; p v,eq,i To and (W) i , T d,i The corresponding equilibrium water vapor partial pressure; p atm Atmospheric pressure.
[0105] The present invention also provides a method for parameter identification and verification of a simplified thermo-humidity model based on the aforementioned passive regeneration drying bed, comprising the following steps:
[0106] S1: Based on the data acquisition component 6, experimental datasets and verification datasets corresponding to different boundary conditions under the moisture absorption and regeneration conditions are collected in real time. Both the experimental datasets and verification datasets include the mass m(t) of the passive regeneration drying bed 1 and the air temperature T at the outlet of the airflow channel 103 of the passive regeneration drying bed 1. out (t) and relative humidity RH out (t); the boundary conditions include the air temperature T at the inlet of the airflow channel 103. in (t) and relative humidity RH in (t), airflow velocity u(t), and solar radiation intensity Q solar One or more of the following;
[0107] It should be noted that the experimental dataset and validation dataset are datasets repeatedly obtained under multiple sets of different boundary conditions; both parameter identification and model validation are based on multiple sets of data. Specifically, parameter identification uses at least two sets of experimental datasets with different boundary conditions, and model validation uses at least one set of validation datasets that are independent of the experimental datasets used for parameter identification and have different boundary conditions. The boundary conditions include at least the inlet air temperature T. in (t) and relative humidity RH in (t), airflow velocity u(t), and solar radiation intensity Q solar One or more of them.
[0108] S2: Identify the target parameters in the simplified thermal and moisture model using the experimental dataset. The target parameters include at least the equivalent heat transfer resistances R1, R2, R3, R4, and R5, and the equivalent moisture transfer resistance R. m,1 R m,2 R m,3 ;
[0109] S3: Based on the target parameters, input the verification dataset into the simplified thermal and humidity model to calculate the calculated air temperature, relative humidity and mass of the passive regeneration drying bed 1 at the outlet of the airflow channel 103. Then, verify the simplified thermal and humidity model.
[0110] In one or more embodiments of the present invention, identifying the target parameters in the simplified thermal-humidity model using the experimental dataset and the validation dataset specifically includes the following steps:
[0111] S21: Input the boundary conditions of the experimental dataset into the simplified thermal and humidity model to obtain the corresponding calculated mass value of the passive regeneration drying bed 1, the calculated air temperature at the outlet of the airflow channel 103 of the passive regeneration drying bed 1, and the calculated relative humidity value.
[0112] S22: Establish an objective function using the calculated mass value of the passive regeneration drying bed 1, the calculated air temperature at the outlet of the airflow channel 103 of the passive regeneration drying bed 1, the calculated relative humidity value, and the experimental dataset:
[0113]
[0114] Among them, t k The sampling time sequence is N; the number of sampling points is w. T w RH w m These are the weighting coefficients for air temperature, relative humidity, and mass, respectively. This is the calculated air temperature at the outlet of airflow channel 103 in the thermal-humidity coupling model. This represents the measured air temperature at the outlet of airflow channel 103 in the thermal-humidity coupling model. This is the calculated relative humidity value at the outlet of airflow channel 103 in the thermal-humidity coupling model. This is the measured relative humidity at the outlet of airflow channel 103 in the thermal-humidity coupling model;
[0115] S23: Determine the target parameters in the simplified thermal and moisture model using the minimum value of the objective function as the objective. The target parameters include equivalent heat transfer resistances R1, R2, R3, R4, and R5, and equivalent moisture transfer resistance R. m,1 R m,2 R m,3 .
[0116] In one or more embodiments of the present invention, the verification of the simplified thermal and humidity model specifically includes the following steps:
[0117] S31: Match the calculated air temperature, relative humidity, and mass of the passive regeneration dryer 1 at the outlet of airflow channel 103 with the measured air temperature, relative humidity, and mass of the passive regeneration dryer 1 in the verification dataset according to the same time series, and calculate the outlet air temperature T. out (t), outlet relative humidity RH out The mean absolute error (MAE) of the mass m(t) of the passive regeneration drying bed 1 is calculated using the following formula:
[0118]
[0119] Where y is T out (t), RH out (t) or m(t), where N is the number of sampling points in the validation dataset;
[0120] S32: When all the mean absolute errors meet the preset threshold, it is determined that the prediction accuracy of the simplified thermal and humidity model meets the requirements.
[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A simplified thermo-humidity model of a passive regeneration dryer, based on a test device for the thermo-humidity characteristics of a passive regeneration dryer, characterized in that: The passive regeneration drying bed thermal and humidity characteristic testing device includes a passive regeneration drying bed (1), a solar radiation simulation component (2), an environmental chamber (3), a temperature and humidity air conditioner (4), a gas flow control component (5), a data acquisition component (6), and a main control module (8). The passive regeneration drying bed (1), the solar radiation simulation component (2), the gas flow control component (5), and the data acquisition component (6) are respectively installed in the environmental chamber (3). The passive regeneration drying bed (1) is inclined. The solar radiation simulation component (2) is installed above the passive regeneration drying bed (1). The data acquisition component (6) is installed inside and below the passive regeneration drying bed (1). The gas flow control component (5) is installed at the higher end of the passive regeneration drying bed (1). The air inlet and air outlet of the temperature and humidity air conditioner (4) are respectively connected to the environmental chamber (3) and are used to maintain the air temperature and relative humidity in the environmental chamber (3) within a preset range. The main control module (8) is electrically connected to the data acquisition component (6). The solar radiation simulation component (2) is used to simulate the sun's luminescence and radiate it to the passive regeneration drying bed (1) under regeneration conditions. The temperature and humidity air conditioner (4) is used to regulate and maintain the air temperature and relative humidity in the environmental chamber (3) at a constant level; The gas flow control component (5) is used to adjust the airflow channel of the passive regeneration drying bed (1) according to the regeneration or moisture absorption conditions. The data acquisition component (6) is used to collect experimental datasets and verification datasets corresponding to different boundary conditions of the passive regeneration drying bed (1) under moisture absorption and regeneration conditions in real time. The main control module (8) is used to construct a simplified thermal and humidity model of the passive regeneration drying bed, identify target parameters of the simplified thermal and humidity model based on the experimental dataset, and verify the simplified thermal and humidity model after the target parameters are identified based on the verification dataset. The simplified thermal and humidity model is specifically as follows: ； ； ； ； Among them, T amb The air temperature is the ambient temperature and is equal to the air temperature at the inlet of the airflow channel (103) of the passive regeneration drying bed (1); T p1 T p2 T p3 These are the temperature nodes of each sub-region of the frame (101); T d1 T d2 T d3 These are the temperature nodes of each sub-region of the desiccant layer (102); R1, R2, R3, R4, and R5 are the equivalent thermal resistances between two adjacent temperature nodes; T a Q represents the node of average air temperature along the airflow channel (103); solar Q represents the equivalent heat gain of solar radiation on the upper surface of the frame (101); sorp,1 Q sorp,2 Q sorp,3 The heat source term for the adsorption / desorption of the desiccant layer (102); Q a Sensible heat gained from air; T out The air temperature at the outlet of the airflow channel (103); Air mass flow rate; Δh is the specific heat capacity of air at constant pressure. sorp This is the adsorption enthalpy of water vapor on the desiccant, taken as a positive value; ； ； ； in, Y is the dry air mass flow rate; in With Y out The moisture content of the air at the inlet and outlet of the airflow channel (103) of the passive regeneration drying bed (1) are respectively; q m,i M represents the net moisture transfer between the i-th desiccant node and the air; s,i Y represents the effective dry mass of the desiccant at the i-th desiccant node. eq,i Y represents the interfacial equilibrium moisture content determined by both the moisture content of the desiccant and temperature; W0 is the saturated adsorption capacity parameter; W1, W2, and W3 are the dry basis moisture content state variables of each sub-region of the desiccant layer (102), respectively. eq,1 Y eq,2 Y eq,3 These represent the interface equilibrium moisture content nodes corresponding to the dry basis moisture content state variables W1, W2, and W3, respectively; E is the characteristic energy parameter; n is the exponential parameter; R... g p is the gas constant. ws (T) represents the partial pressure of saturated water vapor at air temperature T; p v,eq,i To and (W) i , T d,i The corresponding equilibrium water vapor partial pressure; p atm Atmospheric pressure.
2. The simplified thermo-humidity model of the passive regeneration dryer according to claim 1, characterized in that: The passive regeneration drying bed (1) includes a frame (101) and a heat insulation layer (104). The frame (101) is hollow inside. The inner walls of the frame (101) are coated with desiccant layers (102) and airflow channels (103) are formed inside the frame (101). The top of the frame (101) is located on the side facing the solar radiation simulation component (2), and the heat insulation layer (104) is located at the bottom of the frame (101).
3. The simplified thermo-humidity model of the passive regeneration drying bed according to claim 2, characterized in that: The data acquisition component (6) includes an electronic scale (601), an anemometer (603), an inlet wireless temperature and humidity sensor (604), and an outlet wireless temperature and humidity sensor (605). The passive regeneration drying bed (1) is tilted on the electronic scale (601). The anemometer (603) is set in the airflow channel (103) and used to measure the airflow speed. The inlet wireless temperature and humidity sensor (604) and the outlet wireless temperature and humidity sensor (605) are respectively set at the inlet and outlet of the airflow channel (103) of the passive regeneration drying bed (1). The electronic scale (601), the anemometer (603), the inlet wireless temperature and humidity sensor (604), and the outlet wireless temperature and humidity sensor (605) are electrically connected to the main control module (8).
4. The simplified thermo-humidity model of the passive regeneration dryer according to claim 1, characterized in that: The gas flow control component (5) includes an airflow switching valve (501), an air duct (502), and a fan with adjustable speed (503). The air duct (502) is located at the higher end of the passive regeneration drying bed (1). The airflow switching valve (501) is located at the connection between the air duct (502) and the passive regeneration drying bed (1). The airflow switching valve (501) can switch the airflow channel so that the passive regeneration drying bed (1) is connected to the air duct (502), or the passive regeneration drying bed (1) is directly connected to the environmental chamber (3). The fan (503) is located inside the air duct (502).
5. The simplified thermo-humidity model of the passive regeneration dryer according to claim 1, characterized in that: The solar radiation simulation component (2) includes a lamp array (201) and a controller (202). The lamp array (201) is positioned above the passive regeneration drying bed (1). The controller (202) is electrically connected to the lamp array (201) and can continuously adjust the radiation intensity of the lamp array (201).
6. The simplified thermo-humidity model of the passive regeneration dryer according to claim 1, characterized in that: The main control module (8) constructs a simplified thermal and humidity model of the passive regeneration drying bed, identifies target parameters of the simplified thermal and humidity model based on the experimental dataset, and verifies the simplified thermal and humidity model after target parameter identification based on the verification dataset. The specific implementation of this is as follows: A simplified thermo-humidity model of a passive regeneration drying bed was constructed. Based on the data acquisition component (6), experimental datasets and verification datasets corresponding to different boundary conditions under the moisture absorption and regeneration conditions are collected in real time. The experimental datasets and verification datasets include the mass of the passive regeneration drying bed (1), the air temperature and relative humidity at the outlet of the airflow channel (103) of the passive regeneration drying bed (1); the boundary conditions include one or more of the following: air temperature and relative humidity at the inlet of the airflow channel (103), airflow velocity, and solar radiation intensity. The target parameters in the simplified thermal and humidity model are identified using the experimental dataset. The verification dataset is input into the simplified thermal and humidity model to calculate the air temperature, relative humidity and mass of the passive regeneration drying bed (1) at the outlet of the airflow channel (103). The simplified thermal and humidity model is then verified.
7. The simplified thermo-humidity model of the passive regeneration dryer according to claim 6, characterized in that: The specific implementation of the simplified thermo-humidity model for constructing the passive regeneration drying bed is as follows: The frame (101) of the passive regeneration drying bed (1) is divided into sub-region temperature nodes T. p1 T p2 T p3 The desiccant layer (102) is divided into sub-regions with temperature nodes T. d1 T d2 T d3 And an air temperature node T is set in the airflow channel (103). a And set equivalent thermal resistances R1, R2, R3, R4, and R5 between two adjacent temperature nodes; The desiccant layer (102) is divided into dry basis moisture content state variables W1, W2, and W3, which correspond to the interface equilibrium moisture content node Y, respectively. eq,1 Y eq,2 Y eq,3 An air humidity node Y is set in the airflow channel (103) of the passive regeneration drying bed (1). a And at the air humidity node Y a With the interface equilibrium moisture content node Y eq,1 Y eq,2 Y eq,3 Equivalent moisture transfer resistance R is set between them respectively. m,1 R m,2 R m,3 .
8. A method for parameter identification and verification of a simplified thermo-humidity model of a passive regeneration dryer as described in any one of claims 1-7, characterized in that, Includes the following steps: Based on the data acquisition component (6), experimental datasets and verification datasets corresponding to different boundary conditions under the moisture absorption and regeneration conditions are collected in real time. The experimental datasets and verification datasets include the mass of the passive regeneration drying bed (1), the air temperature and relative humidity at the outlet of the airflow channel (103) of the passive regeneration drying bed (1); the boundary conditions include one or more of the following: air temperature and relative humidity at the inlet of the airflow channel (103), airflow velocity, and solar radiation intensity. The target parameters in the simplified thermal and humidity model are identified using the experimental dataset. Based on the target parameters, the verification dataset is input into the simplified thermal and humidity model to calculate the air temperature, relative humidity and mass at the outlet of the airflow channel (103) of the passive regeneration drying bed (1), and to verify the simplified thermal and humidity model.
9. The verification method for the passive regeneration drying bed thermal and humidity characteristic testing device according to claim 8, characterized in that: Identifying the target parameters in the simplified thermo-hygroscopic model using the experimental and validation datasets specifically includes the following steps: The boundary conditions of the experimental dataset are input into the simplified thermal and humidity model to obtain the corresponding calculated mass value of the passive regeneration drying bed (1), the calculated air temperature at the outlet of the airflow channel (103) of the passive regeneration drying bed (1), and the calculated relative humidity value. An objective function is established using the calculated mass value of the passive regeneration drying bed (1), the calculated air temperature at the outlet of the airflow channel (103) of the passive regeneration drying bed (1), the calculated relative humidity value, and the experimental dataset: ； Among them, t k The sampling time sequence is N; the number of sampling points is w. T w RH w m These are the weighting coefficients for air temperature, relative humidity, and mass, respectively. This is the calculated air temperature at the outlet of the airflow channel (103) in the thermal-humidity coupling model. This represents the measured air temperature at the outlet of the airflow channel (103) in the thermal-humidity coupling model. This represents the calculated relative humidity at the outlet of the airflow channel (103) in the thermal-humidity coupling model. The measured relative humidity at the outlet of the airflow channel (103) in the thermal-humidity coupling model; The target parameters in the simplified thermal and moisture model are determined by minimizing the objective function. These target parameters include equivalent heat transfer resistances R1, R2, R3, R4, and R5, and equivalent moisture transfer resistance R. m,1 R m,2 R m,3 .
10. The verification method for the passive regeneration drying bed thermal and humidity characteristic testing device according to claim 9, characterized in that: The verification of the simplified thermal and humidity model specifically includes the following steps: The calculated values of air temperature, relative humidity, and mass of the passive regeneration dryer (1) at the outlet of the airflow channel (103) are matched one-to-one with the measured values of air temperature, relative humidity, and mass of the passive regeneration dryer (1) at the outlet of the airflow channel (103) in the verification data according to the same time series, and the outlet air temperature T is calculated respectively. out (t), outlet relative humidity RH out The mean absolute error (MAE) of the mass m(t) of the passive regeneration drying bed (1) and the mass m(t) is calculated using the following formula: ； Where y is T out (t), RH out (t) or m(t), where N is the number of sampling points in the validation dataset; When all the mean absolute errors meet the preset threshold, the prediction accuracy of the simplified thermal and humidity model is determined to meet the requirements.