Low-temperature dehumidification drying system and method based on Internet of Things acquisition and dynamic control

Abstract

According to the low-temperature dehumidification drying system and method based on Internet of Things collection and dynamic control, a to-be-dried product production line is arranged in a drying chamber, a modular circulating drying device can be modularly installed in an original drying chamber according to needs, installation is more flexible and convenient, the construction cost is lower, a drying medium can be recycled, and the drying efficiency is improved. Effective recovery of waste heat is achieved, and energy consumption is reduced. The water content of a to-be-dried product and the speed, temperature and humidity of a drying medium passing through the to-be-dried product are collected in real time through the Internet of Things collecting unit, data collected by the Internet of Things collecting unit are input into the drying prediction model in real time, a dynamic control instruction is generated, and the modular circulating drying device is controlled to operate. Therefore, dynamic control over the drying process is achieved, and the drying efficiency and the drying effect are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of food, biological products, and drug drying, and particularly to a low-temperature dehumidification drying system and method based on Internet of Things collection and dynamic control. Background Art

[0002] Heat pump dryers are widely used in the processing of food, biological products, drugs, herbs, textiles, paper products, chemicals, beauty products, etc. In the food processing field, they are used for drying and preserving fruits and vegetables. These systems utilize advanced heat pump technology to effectively dehydrate agricultural products while maintaining product quality. Industrial-scale heat pump dryers are used for efficient dehydration of fruits and vegetables, maintaining their nutritional value, flavor, and quality, while extending their shelf life.

[0003] Heat pump dryers use a closed-loop system to circulate low-temperature, low-humidity air over the surface of fruits and vegetables, extracting moisture through evaporation. This process operates at a lower temperature (usually 30 - 60 °C), which helps to maintain the nutritional value, flavor, color, and texture of agricultural products compared to traditional dryers. By controlling temperature, humidity, and air flow, heat pump dryers minimize thermal degradation, oxidation, and other processes that damage quality, resulting in dried fruits and vegetables with better appearance, taste, and nutritional retention, making them more appealing to consumers. Heat pumps recover and reuse energy from the drying air, significantly reducing energy consumption compared to traditional drying methods. This makes the process cost-effective and environmentally friendly in large-scale operations.

[0004] Existing industrial-scale heat pump dryers involve complex mechanical and electrical systems, including compressors, heat exchangers, and refrigerants, requiring professional maintenance. Regular maintenance and repair costs are high, and trained technicians are needed, increasing operating expenses. Also, due to their large footprint, existing industrial-scale heat pump dryers require a large amount of space for installation, limiting their use in scenarios with limited space. More importantly, currently existing industrial-scale heat pump dryers can only operate according to preset drying parameters and cannot be adjusted in real time according to the actual moisture content, resulting in unstable drying effects of products.

[0005] Therefore, there is an urgent need for a low-temperature dehumidification drying system and method based on Internet of Things collection and dynamic control that can solve the above problems. Summary of the Invention

[0006] The object of the present invention is to provide a low-temperature dehumidification drying system and method based on Internet of Things collection and dynamic control to solve the problems existing in the above-mentioned prior art.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] The present invention provides a low-temperature dehumidification drying system based on Internet of Things (IoT) collection and dynamic control, including:

[0009] A drying chamber, in which a production line of products to be dried is provided;

[0010] A modular circulating drying device, which is installed in the drying chamber and correspondingly arranged at the side of the production line of products to be dried, and is used for low-temperature dehumidification drying of the products to be dried through a drying medium;

[0011] An IoT collection unit, which is arranged on the production line of products to be dried and the modular circulating drying device, and is used for collecting the moisture content of the products to be dried, as well as the speed, temperature and humidity of the drying medium after passing through the products to be dried;

[0012] A control platform, which establishes communication with the modular circulating drying device and the IoT collection unit, and has a built-in drying prediction model, and is used for inputting the data collected by the IoT collection unit into the drying prediction model in real time to generate dynamic control instructions to control the operation of the modular circulating drying device.

[0013] Preferably, the modular circulating drying device includes an air outlet pipe and a return air pipe, both of which are connected to a processing chamber. The air outlet pipe is arranged below the production line of products to be dried, and air outlet openings are evenly distributed on its side. The return air pipe is arranged above the production line of products to be dried, and air suction openings are evenly distributed on its side. The air suction openings are arranged opposite to the air outlet openings.

[0014] Preferably, a heat pump condenser and a heater are provided in the processing chamber. The heat pump condenser and the heater are arranged opposite to each other, and a processing channel is provided between them. One end of the processing channel is connected to the air outlet pipe through a variable-frequency blower, and the other end of the processing channel is connected to the return air pipe through a variable-frequency air suction fan. The heat pump condenser, the heater, the variable-frequency blower and the variable-frequency air suction fan are all in communication with the control platform.

[0015] Preferably, a pressure balance plate is provided in the air outlet pipe.

[0016] Preferably, a flexible bellows is provided between the air suction opening and the return air pipe.

[0017] Preferably, the Internet of Things acquisition unit includes a moisture meter, an air velocity sensor, a temperature sensor, and a humidity sensor. The moisture meter is disposed on the surface of the production line of the product to be dried, and the air velocity sensor, the temperature sensor, and the humidity sensor are all disposed on the air suction port. The moisture meter, the air velocity sensor, the temperature sensor, and the humidity sensor are all wirelessly connected to the control platform through a wireless communication module.

[0018] The present invention also provides a low-temperature dehumidification drying method based on Internet of Things acquisition and dynamic control, including the following steps:

[0019] S1. System initialization, inputting initial operation parameters through the control platform;

[0020] S2. The control platform controls the modular cyclic drying device to dry the product to be dried on the production line of the product to be dried according to the initial operation parameters;

[0021] S3. The Internet of Things acquisition unit real-time collects the moisture content of the product to be dried, the air velocity, temperature, and humidity at the air suction port, and uploads them to the control platform;

[0022] S4. The control platform inputs the data collected by the Internet of Things acquisition unit into the drying prediction model. The drying prediction model calculates the optimal air velocity, optimal temperature, and optimal humidity of the drying medium, and generates dynamic control instructions to control the operation of the modular cyclic drying device.

[0023] Preferably, in step S2, the drying method is:

[0024] S21. The variable-frequency suction fan operates to suck cold air into the processing chamber;

[0025] S22. The heat pump condenser heats the cold air, and the heater heats and dehumidifies the cold air;

[0026] S23. The variable-frequency blower operates to convey the heated and dehumidified dry hot air to the air outlet pipe, and dries the product to be dried through the air outlet;

[0027] S24. The dried wet cold air is recycled by the air suction port.

[0028] Preferably, in step S4, the construction method of the drying prediction model includes:

[0029] S41. Data acquisition, collecting the initial moisture content, hot air temperature, hot air humidity, hot air velocity, and drying time data of the material in the historical drying process, and storing the collected data in the database;

[0030] S42. Data processing: Clean the collected data, check for missing values in the data. If the number of missing values is small and has little impact on the overall data, directly delete the records containing missing values; if there are many missing values, use the interpolation method to fill them; identify outliers in the data through the anomaly detection algorithm, and delete, replace or perform numerical conversion on the outliers; detect and remove duplicate records in the data; convert the data into a distribution with a mean of 0 and a standard deviation of 1, and the formula is: x' = (x - μ) / σ, where x is the original data, μ is the mean of the data, and σ is the standard deviation of the data; perform feature processing on the normalized data;

[0031] S43. Use the ModifiedPage model to construct a drying prediction model:

[0032] MR = exp[-k·t n ;

[0033] where MR is the moisture ratio; k is the drying constant,

[0034] k = -0.00047H + 0.00022T - 0.00025;

[0035] n is the drying index,

[0036] n = -0.0011H 2 + 0.0360H - 0.00016T 2 + 0.0133T + 0.3774;

[0037] H is the material thickness, T is the hot air temperature; t is the drying time;

[0038] S44. Model verification and optimization: Verify the prediction effect of the model through experimental data, adjust the model according to the verification results, and introduce machine learning algorithms to further improve the prediction accuracy.

[0039] The present invention has achieved the following beneficial technical effects compared with the prior art:

[0040] A low-temperature dehumidification drying system and method based on Internet of Things collection and dynamic control provided by the present invention. A production line of products to be dried is provided in the drying chamber. The modular circulating drying device can be modularly installed as needed in the original drying chamber, with more flexible and convenient installation, lower construction cost, and can recycle the drying medium, realizing effective recovery of waste heat and reducing energy consumption; through the Internet of Things collection unit, the water content of the products to be dried, as well as the speed, temperature, and humidity of the drying medium after passing through the products to be dried are collected in real time, and the data collected by the Internet of Things collection unit is input into the drying prediction model in real time to generate dynamic control instructions to control the operation of the modular circulating drying device, thereby realizing the dynamic control of the drying process and effectively improving the drying efficiency and drying effect. Brief Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic structural diagram of a modular circulating drying device in a low-temperature dehumidifying and drying system based on Internet of Things collection and dynamic control provided by the present invention;

[0043] In the figure: 1: air outlet duct, 2: return air duct, 3: treatment chamber, 4: air outlet, 5: air suction port, 6: heat pump condenser, 7: heater, 8: treatment channel, 9: variable-frequency blower, 10: variable-frequency suction fan, 11: pressure balance plate, 12: flexible corrugated pipe. Detailed Embodiments

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] The purpose of the present invention is to provide a low-temperature dehumidifying and drying system and method based on Internet of Things collection and dynamic control to solve the problems existing in the prior art.

[0046] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0047] Embodiment 1:

[0048] This embodiment provides a low-temperature dehumidifying and drying system based on Internet of Things collection and dynamic control, as Figure 1 shown, including:

[0049] A drying chamber, in which a production line of products to be dried is provided;

[0050] A modular circulating drying device, which is installed in the drying chamber and is correspondingly arranged on the side of the production line of products to be dried for low-temperature dehumidifying and drying of the products to be dried through a drying medium;

[0051] The Internet of Things acquisition unit is arranged on the production line of the product to be dried and the modular circulating drying device, and is used to collect the water content of the product to be dried, as well as the speed, temperature, and humidity of the drying medium after passing through the product to be dried;

[0052] The control platform establishes communication with the modular circulating drying device and the Internet of Things acquisition unit. It has a built-in drying prediction model, which is used to input the data collected by the Internet of Things acquisition unit into the drying prediction model in real time, generate dynamic control instructions, and control the operation of the modular circulating drying device.

[0053] By adopting the above technical solution, the water content of the product to be dried, as well as the speed, temperature, and humidity of the drying medium after passing through the product to be dried, are collected in real time by the Internet of Things acquisition unit, and the data collected by the Internet of Things acquisition unit are input into the drying prediction model in real time to generate dynamic control instructions to control the operation of the modular circulating drying device, thereby realizing the dynamic control of the drying process and effectively improving the drying efficiency and drying effect.

[0054] As an implementation method, the modular circulating drying device includes an air outlet duct 1 and a return air duct 2, both of which are connected to the treatment chamber 3. The air outlet duct 1 is arranged below the production line of the product to be dried, and air outlet openings 4 are evenly distributed on its side. The return air duct 2 is arranged above the production line of the product to be dried, and air suction openings 5 are evenly distributed on its side. The air suction openings 5 are arranged opposite to the air outlet openings 4.

[0055] By adopting the above technical solution, by constructing a circulating air path structure, the recovery of drying waste heat is realized, thereby reducing energy consumption.

[0056] As an implementation method, a heat pump condenser 6 and a heater 7 are arranged in the treatment chamber 3. The heat pump condenser 6 and the heater 7 are arranged opposite to each other, and a treatment channel 8 is arranged between them. One end of the treatment channel 8 is connected to the air outlet duct 1 through a variable-frequency blower 9, and the other end of the treatment channel 8 is connected to the return air duct 2 through a variable-frequency air suction fan 10. The heat pump condenser 6, the heater 7, the variable-frequency blower 9, and the variable-frequency air suction fan 10 are all in communication with the control platform.

[0057] By adopting the above technical solution, the heat pump condenser 6 and the heater 7 are used to heat and dehumidify the drying medium, thereby ensuring the drying effect.

[0058] As an implementation method, a pressure balance plate 11 is arranged in the air outlet duct 1.

[0059] By adopting the above technical solution, the path of the drying medium can be extended, so that the pressures at the multiple air outlet openings 4 are the same, avoiding uneven wind force caused by pressure imbalance.

[0060] As an implementation method, a flexible corrugated pipe 12 is arranged between the air suction opening 5 and the return air duct 2.

[0061] By adopting the above technical solution, it is convenient to adjust the length and direction, thus ensuring the recycling effect.

[0062] As an implementation manner, the Internet of Things acquisition unit includes a moisture meter, a wind speed sensor, a temperature sensor, and a humidity sensor. The moisture meter is arranged on the surface of the production line of the product to be dried, and the wind speed sensor, the temperature sensor, and the humidity sensor are all arranged on the air suction port 5. The moisture meter, the wind speed sensor, the temperature sensor, and the humidity sensor are all wirelessly connected to the control platform through a wireless communication module.

[0063] By adopting the above technical solution, it is possible to collect and monitor the drying state in real time, thus ensuring the stability and reliability of the drying process.

[0064] This embodiment also provides a low-temperature dehumidification drying method based on Internet of Things acquisition and dynamic control, including the following steps:

[0065] S1. System initialization, inputting initial operation parameters through the control platform;

[0066] S2. The control platform controls the modular cyclic drying device to dry the product to be dried on the production line of the product to be dried according to the initial operation parameters; the drying method is:

[0067] S21. The variable-frequency suction fan 10 operates to suck cold air into the processing chamber 3;

[0068] S22. The heat pump condenser 6 heats the cold air, and the heater 7 heats and dehumidifies the cold air;

[0069] S23. The variable-frequency blower 9 operates to convey the heated and dehumidified dry hot air to the air outlet duct 1, and dry the product to be dried through the air outlet 4;

[0070] S24. The dried wet cold air is recycled by the air suction port 5;

[0071] S3. The Internet of Things acquisition unit collects the moisture content of the product to be dried, the wind speed, temperature, and humidity at the air suction port 5 in real time, and uploads them to the control platform;

[0072] S4. The control platform inputs the data collected by the Internet of Things acquisition unit into the drying prediction model. The drying prediction model calculates the optimal wind speed, optimal temperature, and optimal humidity of the drying medium, and generates dynamic control instructions to control the operation of the modular cyclic drying device; the construction method of the drying prediction model includes:

[0073] S41. Data acquisition, collecting the initial moisture content, hot air temperature, hot air humidity, hot air wind speed, and drying time data of the material in the historical drying process, and storing the collected data in the database;

[0074] S42. Data processing: Clean the collected data, check for missing values in the data. If the number of missing values is small and has little impact on the overall data, directly delete the records containing missing values; if there are many missing values, use the interpolation method to fill them. Identify outliers in the data through outlier detection algorithms, and delete, replace, or perform numerical conversion on the outliers. Detect and remove duplicate records in the data. Convert the data to a distribution with a mean of 0 and a standard deviation of 1, with the formula: x' = (x - μ) / σ, where x is the original data, μ is the mean of the data, and σ is the standard deviation of the data. Perform feature processing on the normalized data;

[0075] S43. Use the ModifiedPage model to construct a drying prediction model:

[0076] MR = exp[-k·t n ;

[0077] where MR is the moisture ratio; k is the drying constant,

[0078] k = -0.00047H + 0.00022T - 0.00025;

[0079] n is the drying index,

[0080] n = -0.0011H 2 + 0.0360H - 0.00016T 2 + 0.0133T + 0.3774;

[0081] H is the material thickness, T is the hot air temperature; t is the drying time;

[0082] S44. Model verification and optimization: Verify the prediction effect of the model through experimental data, adjust the model according to the verification results, and introduce machine learning algorithms to further improve the prediction accuracy.

[0083] The present invention uses specific examples to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A low-temperature dehumidification and drying system based on IoT data collection and dynamic control, characterized by: include: A drying chamber, wherein a production line of products to be dried is arranged in the drying chamber; A modular circulation drying device, which is installed in the drying chamber and correspondingly arranged on the side of the production line of the product to be dried, and is used for low-temperature dehumidification and drying of the product to be dried through a drying medium; An Internet of Things collection unit, which is arranged on the production line of the product to be dried and the modular circulation drying device, and is used to collect the water content of the product to be dried and the speed, temperature and humidity of the drying medium after passing through the product to be dried; A control platform establishes communication with the modular circulation drying device and the Internet of Things acquisition unit, and has a built-in drying prediction model for inputting the data collected by the Internet of Things acquisition unit into the drying prediction model in real time, generating dynamic control instructions, and controlling the operation of the modular circulation drying device.

2. The low-temperature dehumidification and drying system based on Internet of Things data collection and dynamic control according to claim 1 is characterized in that: The modular circulation drying device comprises an air outlet duct (1) and an air return duct (2), both of which are connected to a processing chamber (3); the air outlet duct (1) is arranged below the production line of the product to be dried, and has air outlets (4) evenly distributed on its side; the return air duct (2) is arranged above the production line of the product to be dried, and has air suction ports (5) evenly distributed on its side; the air suction ports (5) are arranged opposite to the air outlets (4).

3. The low-temperature dehumidification and drying system based on Internet of Things collection and dynamic control according to claim 2 is characterized in that: A heat pump condenser (6) and a heater (7) are arranged in the processing chamber (3); the heat pump condenser (6) and the heater (7) are arranged opposite to each other, and a processing channel (8) is arranged between the two. One end of the processing channel (8) is connected to the air outlet duct (1) through a variable frequency blower (9), and the other end of the processing channel (8) is connected to the return air duct (2) through a variable frequency suction fan (10). The heat pump condenser (6), the heater (7), the variable frequency blower (9) and the variable frequency suction fan (10) all establish communication with the control platform.

4. The low-temperature dehumidification and drying system based on Internet of Things collection and dynamic control according to claim 2 is characterized in that: A pressure balancing plate (11) is provided in the air outlet duct (1).

5. The low-temperature dehumidification and drying system based on Internet of Things data collection and dynamic control according to claim 2 is characterized in that: A flexible bellows (12) is provided between the air suction port (5) and the air return duct (2).

6. The low-temperature dehumidification and drying system based on Internet of Things data collection and dynamic control according to claim 2 is characterized in that: The Internet of Things acquisition unit comprises a moisture meter, a wind speed sensor, a temperature sensor and a humidity sensor; the moisture meter is arranged on the surface of the production line of the product to be dried; the wind speed sensor, the temperature sensor and the humidity sensor are all arranged on the air intake port (5); the moisture meter, the wind speed sensor, the temperature sensor and the humidity sensor are all wirelessly connected to the control platform via a wireless communication module.

7. A low-temperature dehumidification and drying method based on Internet of Things data collection and dynamic control, characterized in that: The following steps are involved: S1. System initialization, input initial operating parameters through the control platform; S2. The control platform controls the modular circulation drying device to dry the product to be dried on the product production line according to the initial operating parameters; S3. The IoT collection unit collects the moisture content of the product to be dried, the wind speed, temperature and humidity at the air inlet (5) in real time, and uploads it to the control platform; S4. The control platform inputs the data collected by the Internet of Things acquisition unit into the drying prediction model. The drying prediction model calculates the optimal wind speed, optimal temperature and optimal humidity of the drying medium, and generates dynamic control instructions to control the operation of the modular circulation drying device.

8. The low-temperature dehumidification and drying method based on Internet of Things collection and dynamic control according to claim 7 is characterized in that: In step S2, the drying method is: S21. The variable frequency suction fan (10) is running to draw cold air into the processing chamber (3); S22. The heat pump condenser (6) heats the cold air, and the heater (7) heats and dehumidifies the cold air; S23. The variable frequency blower (9) is running to deliver the heated and dehumidified hot dry air to the air outlet duct (1), and the product to be dried is dried through the air outlet (4); S24. The dried wet and cold air is recovered and recycled by the air suction port (5).

9. The low-temperature dehumidification and drying method based on Internet of Things collection and dynamic control according to claim 7 is characterized in that: In step S4, the method for constructing the drying prediction model includes: S41. Data collection, collecting the initial moisture content, hot air temperature, hot air humidity, hot air speed, drying time data of the material in the historical drying process, and storing the collected data in the database; S42. Data processing: clean the collected data and check whether there are missing values ​​in the data. If the missing values ​​are few and have little impact on the overall data, directly delete the records containing missing values; if there are many missing values, use interpolation method to fill them; identify outliers in the data through anomaly detection algorithm, delete, replace or convert the outliers; detect and remove duplicate records in the data; convert the data into a distribution with a mean of 0 and a standard deviation of 1, the formula is: x'=(x-μ) / σ, where x is the original data, μ is the mean of the data, and σ is the standard deviation of the data; perform feature processing on the normalized data; S43. Using ModifiedPage model to build drying prediction model: MR=exp[-k·t n ]; Where MR is the moisture ratio; k is the drying constant, k=-0.00047H+0.00022T-0.00025; n is the drying index, n=-0.0011H 2 +0.0360H-0.00016T 2 +0.0133T+0.3774; H is the material thickness, T is the hot air temperature; t is the drying time; S44. Model verification and optimization: verify the prediction effect of the model through experimental data, adjust the model according to the verification results, and introduce machine learning algorithms to further improve the prediction accuracy.

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