Energy-saving control method and system for drying equipment

Abstract

The invention discloses an energy-saving control method and system for drying equipment, relates to the technical field of energy saving, and aims to remarkably improve the drying efficiency and reduce the energy consumption by accurately controlling heat flow transfer and optimizing the heating process. Comprising the steps of firstly obtaining material parameters and establishing a heat flow simulation model; the power of a heating source is controlled through a multi-layer heat transfer adjusting mechanism, and it is ensured that each layer of material reaches the preset heat transfer condition in the whole drying process; dynamically adjusting heating source power and airflow parameters according to material surface temperature, humidity and airflow data detected in real time; optimizing a heat transfer efficiency function by using a genetic algorithm to improve heat distribution efficiency; the heating source power and the airflow direction are adjusted by monitoring the material moisture change in real time. The system comprises a data acquisition module, a heat flow simulation module, a feedback control module, a genetic algorithm optimization module, a moisture monitoring module and an increment updating module, the energy efficiency of the drying equipment is improved, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy saving, and in particular to an energy saving control method and system for drying equipment. Background Art

[0002] The drying process plays an important role in many industrial productions, especially in the fields of food processing, pharmaceuticals, textiles, wood processing, etc. Drying equipment usually consumes a lot of energy to provide heating and airflow power, which makes energy conservation a key research direction. Traditional drying equipment mostly adopts fixed temperature and humidity control modes, lacking the ability to flexibly adjust to different material characteristics and different environmental conditions, resulting in energy waste and low efficiency during the drying process.

[0003] Most existing drying equipment uses constant power heating to achieve material heating and water evaporation through the coordination of air flow and heating elements. Although this method is simple, it does not take into account the diversity of materials and changes in environmental conditions, making it difficult to achieve the best energy efficiency ratio during the heating process. Due to differences in material type, particle size, humidity, and fluctuations in external climate conditions, drying equipment often cannot achieve precise temperature and humidity control, which in turn affects the drying effect and energy efficiency. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose an energy-saving control method and system for drying equipment.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: An energy-saving control method for drying equipment comprises the following steps: S1: Obtain the density, particle size, humidity and thickness parameters of the dried material, perform layered analysis on the material, and establish a heat flow simulation model based on three heat transfer modes: heat conduction, convection and radiation: S2: The heat flow simulation model controls the heating source through a multi-layer heat transfer regulation mechanism. The multi-layer heat transfer regulation controls the power of the heating source according to the material properties to ensure that each layer of material meets the heat transfer conditions for material drying during the entire drying process; S3: Real-time detection of the surface temperature of the material humidity and air flow speed Data, dynamically adjust the heating process; according to the particle size of the material and density Constructing a particle size density curve and deriving a heat transfer efficiency function from the particle size density curve in: is a constant coefficient used to adjust the overall proportion of the model; a, b, c are constants obtained through data fitting, describing the efficiency of energy conversion of materials with different particle sizes and densities during heat transfer; the feedback coefficient is optimized by genetic algorithm , to obtain the optimal heat transfer efficiency; the optimization process of the genetic algorithm selects the optimal feedback coefficient through repeated iterations, so that the heat transfer efficiency function The value of reaches the optimal value, and the optimized feedback coefficient Used to dynamically adjust the heating source power and airflow direction and strength To ensure the best distribution of heat inside the material; based on the difference between the surface temperature and the internal temperature of the material, a nonlinear feedback mechanism is established to adjust the heating intensity; when the surface temperature of the material When the surface temperature is too high, the heating power is reduced through feedback adjustment to avoid energy waste; on the contrary, when the surface temperature is too high, the heating power is reduced through feedback adjustment to avoid energy waste. When it is lower, increase the heating intensity to speed up the transfer of heat; S4: By collecting the moisture change data on the surface and inside of the material in real time, a moisture distribution model is established to describe the migration process of moisture inside the material. Combined with the heat flow simulation model, the evaporation rate of moisture is adjusted. On this basis, the power of the heating source and the direction of the airflow are dynamically adjusted to maximize the distribution of heat and airflow to support the efficiency of the moisture evaporation process. S5: Based on the real-time temperature of the material ,humidity , adjust the particle size density curve In order to obtain the best drying effect, the incremental update mechanism is used to fine-tune the particle size density curve according to the results of each data collection, and gradually adjust the heat transfer efficiency function to optimize the drying process.

[0006] Preferably, the heat transfer efficiency function The optimal solution process includes: Formula: in, is the optimal solution optimized by genetic algorithm, is the heat transfer efficiency function expressed in time The efficiency of heat transfer.

[0007] Preferably, the incremental update mechanism in S5 includes: ,in: Incremental update of the particle size density curve; The adjustment coefficient for incremental update controls the update step size; and They are the sensitivity of the particle size density curve to the material surface temperature and humidity; and are the changes in temperature and humidity respectively.

[0008] Preferably, the genetic algorithm optimization process in S3 collects the humidity change data of the material in real time and dynamically adjusts the heat transfer efficiency function parameters so that the heat transfer efficiency is always maintained at an optimal state to ensure that energy consumption is minimized.

[0009] Preferably, establishing a nonlinear feedback mechanism in S3 includes: in, To feedback the temperature difference, is the material surface temperature, is the internal temperature of the material, is the nonlinear feedback coefficient.

[0010] Preferably, the dynamic adjustment in S3 includes: adjusting the power of the local heating source according to the materials of different particle sizes to ensure heating uniformity; for materials with larger particle sizes, adjusting the airflow angle and intensity to ensure that the hot airflow can effectively penetrate the material layer to avoid excessive evaporation of surface moisture.

[0011] Preferably, in said S4, the moisture distribution model is established by collecting the moisture change data on the surface and inside of the material in real time, which specifically includes: real-time data collection: obtaining the material surface temperature in real time through the sensor , internal temperature and moisture content Indicates the surface moisture content of the material. Indicates the moisture content inside the material; establishes a moisture distribution model: according to the material parameters, combined with the heat flow simulation model, establishes a moisture distribution model for the material; the formula is: in, For any position and time The moisture content at represents the divergence, is the water migration coefficient, is the water evaporation source term.

[0012] Preferably, the moisture distribution model further comprises: after each moisture data collection, calculating the evaporation rate of the material surface and inside according to the particle size, humidity and temperature of the material; adjusting the evaporation rate by the formula: in, is the evaporation coefficient, is the ambient temperature, As a function of particle size and humidity, the effect of particle size on surface water evaporation is considered.

[0013] Preferably, the moisture distribution model further includes: temperature and humidity gradient adjustment: during the drying process, real-time monitoring of different levels of the material The temperature gradient between and humidity gradient ,in, Indicates the surface layer of the material. Indicates the bottom layer of the material; the heating intensity and airflow direction of different layers are adjusted by the following formula: in, is the heating power for each level, and To adjust the coefficient, adjust the heat transfer between different layers.

[0014] An energy-saving control system for drying equipment, the system comprising: Data acquisition module, used to obtain drying material parameters in real time and transmit the data to the central processing unit; The heat flow simulation module establishes a heat flow simulation model according to the heat conduction, convection and radiation heat transfer modes of the material, and controls the power of the heating source based on the model to ensure that the material reaches the predetermined heat transfer conditions during the drying process; The feedback control module dynamically adjusts the power of the heating source and the direction of the airflow according to the data output by the heat flow simulation module, generates a particle size density curve according to the particle size and density of the material, and derives the heat transfer efficiency function; A genetic algorithm optimization module optimizes the feedback coefficient of the heat transfer efficiency function through a genetic algorithm, selects the optimal feedback coefficient and applies it to the regulation of the heating source power and airflow parameters; The moisture monitoring module collects the moisture content data on the surface and inside of the material in real time, and establishes a moisture distribution model in combination with the heat flow simulation model to describe the process of moisture migration from the inside of the material to the surface; The incremental update module fine-tunes the particle size density curve according to the temperature and humidity data after each data collection, and adjusts the heat transfer efficiency function to optimize the drying process.

[0015] The present invention has the following beneficial effects: 1. The present invention achieves significant energy-saving effects by precisely controlling the heat transfer process of the drying equipment. First, the heat flow simulation model combines multiple parameters such as material density, particle size, humidity and thickness to accurately predict the efficiency of heat transfer, and adjust the power of the heating source based on this. Through the multi-layer heat transfer adjustment mechanism, the heating power is adjusted in real time according to the thermal conductivity characteristics of different material layers, thereby avoiding energy waste. The genetic algorithm optimization module continuously optimizes the heat transfer efficiency function to ensure optimal heat distribution in different drying stages, further improving the thermal energy utilization rate of the system. In addition, the moisture monitoring module monitors the changes in moisture inside the material in real time, and dynamically adjusts the moisture evaporation rate in combination with the heat flow simulation model, further ensuring the high efficiency and energy saving of the drying process. Through the collaborative work of the feedback mechanism and the incremental update module, the entire system can be intelligently adjusted according to real-time data, thereby improving the adaptability and stability of the system.

[0016] 2. The independent functions and synergy of each module of the present invention ensure the rational distribution of heat and airflow during the drying process and avoid excessive energy consumption. Through feedback control and dynamic adjustment mechanism, not only the drying efficiency is improved, but also the uniformity and quality of material drying are ensured. In addition, the adaptive ability and optimization mechanism of the system enable the equipment to operate efficiently under different working conditions and achieve energy-saving goals. This solution greatly reduces the energy consumption of drying equipment through intelligent control, while improving production efficiency, providing an efficient and environmentally friendly solution for the field of industrial drying. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A method flow chart of an energy-saving control method for drying equipment proposed by the present invention; Figure 2 This is a system architecture diagram of an energy-saving control system for drying equipment in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, the present invention proposes an energy-saving control method for drying equipment.

[0020] In one embodiment, step S1: various parameters of the material are collected in real time through field equipment. The density sensor is installed on the material feed port or conveyor belt, and the density sensor uses laser or electromagnetic wave technology to measure the material. The particle size data is obtained through online image recognition technology. A high-resolution camera or laser scanner is installed on the material flow path to shoot or scan the material in real time. After the device captures the image information of the material, the size of the material particles is identified through the image processing algorithm, and the particle size distribution is calculated. In addition, if there are large particles in the material, the screening equipment can also be used to verify the particle distribution, separate materials of different particle sizes through the sieve, and obtain the proportion of each particle size segment. The humidity sensor is installed on the surface of the material or under the conveyor belt to monitor the humidity changes of the material in real time. The thickness of the material is obtained by a laser rangefinder or a contact sensor. After the above data is obtained in real time by the sensor module, it is input into the data acquisition module, and the data acquisition module transmits these data to the processing unit in the system in real time for further analysis and processing.

[0021] After obtaining the density, particle size, humidity and thickness data of the above materials, the materials are analyzed in layers. According to the particle size and density of the materials, the materials are divided into different layers. Each layer of the materials is modeled separately according to its heat transfer characteristics (heat conduction, convection and radiation characteristics). Through the obtained density, particle size, humidity, thickness and other data, the heat transfer model of each layer of materials is generated using the heat flow simulation algorithm. Through these heat transfer models, the heat transfer process inside each layer of materials can be accurately described.

[0022] In one embodiment, step S2: based on the material parameters obtained in step S1 and the constructed heat flow simulation model, the model is continuously run during the heating process. The heat flow simulation model calculates the transfer of heat in different layers of materials in real time, taking into account the different heat transfer characteristics of each layer of materials. Based on the simulation data, the heat transfer efficiency of different regions and different time points is predicted.

[0023] Based on the output of the heat flow simulation model, the heat demand of different layers of the material is identified, and the heating source is controlled using a multi-layer heat transfer regulation mechanism. Specifically, the power of the heating source is adjusted according to the actual conditions of each layer of material (such as temperature, humidity, particle size, etc.). For example, for the surface material, due to the rapid evaporation of water, the system may increase the power of the heating source in this layer; while for the lower layer of the material, the heat transfer is slow, and the system may reduce the power of the heating source to avoid energy waste.

[0024] By real-time monitoring of the surface temperature and humidity changes of the material, the power of the heating source is dynamically adjusted. Through the heat flow simulation model, the heat transfer state of each layer of material is judged in real time, and the heating source is adjusted according to the heat distribution scheme provided by the model. For example, when the temperature of a layer of material is close to the set value, the system will automatically reduce the power of the heating source of that layer; conversely, if the temperature of a layer of material is too low, the system will increase the power of the heating source to accelerate heat transfer.

[0025] According to the results of the heat flow simulation model, the position and direction of the heating source are adjusted to optimize the heat distribution. By adjusting the distance between the heating source and the material layer and the angle of the heating source, it is ensured that the hot air flow can evenly penetrate each layer of the material to avoid the surface layer moisture evaporating too quickly, resulting in the inner layer material not being effectively dried. For example, for a material layer with a larger particle size, the system will adjust the airflow direction and angle to ensure that the airflow can effectively penetrate the material layer so that the inner layer material can also be fully heated.

[0026] The heat flow simulation model is not only used for the initial adjustment of the heating source, but also plays a role in the entire heating process. Whenever the temperature or humidity of the material changes, the system will readjust the heating source through the feedback mechanism. For example, when the surface temperature of the material is detected to be higher than the set value, the power of the heating source will be immediately reduced, and a new heating plan will be calculated through the heat flow simulation model. On the contrary, if the surface temperature of the material is low, the power of the heating source will be increased to ensure effective heat transfer.

[0027] In one embodiment, step S3: dynamically adjust the heating process by real-time detection of the material surface temperature, humidity and airflow speed data. This step constructs a particle size density curve based on the particle size and density of the material, and derives the heat transfer efficiency function through the curve, and optimizes the feedback coefficient using a genetic algorithm to ensure the optimal heat transfer efficiency during the heating process. The operation process is as follows: Based on the material parameters collected in real time, a particle size density curve is constructed to describe the behavior of materials of different particle sizes and densities during heat transfer. For example, larger particle sizes may lead to slower heat conduction, so in the heat flow simulation model, changes in particle size and density need to affect the calculation of the heat transfer efficiency function.

[0028] Based on the particle size density curve, the heat transfer efficiency function is derived, which describes the efficiency of energy conversion during heat transfer of materials with different particle sizes and densities. Yes, this formula is the heat conduction efficiency function you mentioned. It calculates the heat conduction efficiency based on the particle size, density and surface humidity of the material. Specifically:

[0029] in: is a constant coefficient that adjusts the overall scale of the model.

[0030] a, b, and c are constants obtained through data fitting, indicating the influence of particle size, density, and humidity on heat conduction efficiency.

[0031] a>0: The larger the particle size, the lower the heat conduction efficiency.

[0032] b>0: The greater the density, the higher the heat conduction efficiency.

[0033] c<0: The higher the humidity, the lower the heat conduction efficiency.

[0034] It is used to describe the efficiency of the heat conduction process, and then help adjust the heating source power, airflow direction, etc. during the drying process to achieve the best energy efficiency ratio. Through this function, the heat distribution and transfer efficiency of each layer of the material can be calculated, and the heating process can be optimized.

[0035] Genetic algorithm is used to optimize the feedback coefficient. Through continuous iteration and selection, the system can find the optimal feedback coefficient and make the heat transfer efficiency function reach the optimal state. The optimization process of genetic algorithm is as follows: Initial population generation: Based on the initial estimates of the particle size density curve and the heat transfer efficiency function, an initial population of multiple feedback coefficients is generated.

[0036] Fitness calculation: For each generation of the population, calculate its fitness, that is, the heat transfer efficiency under the feedback coefficient.

[0037] Selection operation: Select the appropriate feedback coefficient according to the fitness and eliminate individuals with poor performance.

[0038] Crossover and mutation: Through crossover and mutation operations, a new population is generated and optimized.

[0039] Iterative process: Through multiple generations of iterations, the feedback coefficient is continuously optimized until the heat transfer efficiency is optimal.

[0040] The optimization process of the genetic algorithm is iteratively calculated using the following formula: in, is the optimal solution optimized by genetic algorithm, is the heat transfer efficiency function expressed in time The efficiency of heat transfer.

[0041] After the feedback coefficient is optimized, it is applied to the dynamic adjustment of the power of the heating source and the direction of the airflow. For example, if the feedback coefficient indicates that the heat transfer efficiency of a layer of material is low, the heating power of this layer is increased; if the feedback coefficient indicates that the heating of a layer of material is excessive, the heating power of this layer is reduced.

[0042] The power of the heating source is further adjusted by monitoring the temperature difference between the surface and the inside of the material. The temperature difference reflects the distribution of heat inside the material, so the heating source is adjusted through a nonlinear feedback mechanism. Specifically, when the surface temperature of the material is too high, the feedback mechanism will reduce the power of the heating source to avoid excessive heat accumulation on the surface of the material; when the surface temperature of the material is low, the system will increase the power of the heating source to accelerate the transfer of heat. The feedback mechanism formula for temperature difference is: in, To feedback the temperature difference, is the material surface temperature, is the internal temperature of the material, is the nonlinear feedback coefficient.

[0043] Through step S3, dynamic adjustment based on real-time monitoring data of materials can be achieved, the heat transfer efficiency function is derived using the particle size density curve, and the feedback coefficient is optimized through a genetic algorithm, ultimately ensuring that the heat transfer efficiency in the drying process is optimal, greatly improving energy utilization efficiency and reducing energy waste.

[0044] In one embodiment, step S4: the moisture data collected in real time is input into the moisture distribution model to simulate and adjust the moisture migration and evaporation process inside the material. The specific implementation process is as follows: Through temperature and humidity sensors, infrared imaging sensors and other equipment, the temperature, humidity and moisture content data on the surface and inside of the material are obtained respectively.

[0045] Using the input data, a moisture distribution model is established based on the physical properties and thermal conductivity of the material. This model can describe the migration process of moisture from the surface to the inside of the material, as well as the evaporation rate of moisture at different locations. The basic formula of the moisture distribution model is as follows: in, For any position and time The moisture content at represents the divergence, is the water migration coefficient, It is the source item of water evaporation. Through this model, the system can predict the distribution of water in different areas and simulate the rate of water evaporation.

[0046] Based on the moisture distribution model, the power of the heating source is adjusted in real time according to the moisture content at different locations on the surface and inside of the material. For areas with more moisture, the system increases the heating power and airflow intensity to accelerate the evaporation of moisture; for areas with less moisture, the system reduces the heating power and airflow intensity to prevent overheating.

[0047] The airflow distribution is dynamically adjusted according to the moisture distribution. For areas with more moisture, the airflow speed is increased to help the surface moisture evaporate; while for areas with less moisture, the airflow speed is reduced to avoid over-drying.

[0048] The water evaporation rate is dynamically adjusted according to the material's particle size, humidity, temperature and other parameters to ensure optimal heat and water distribution during the drying process. Based on the prediction results of the water distribution model, the system optimizes the water evaporation rate by adjusting the heating power, air flow speed and other operating parameters, so that the entire drying process achieves the highest efficiency and quality standards. The evaporation rate adjustment formula is as follows: Evaporation rate in, is the evaporation coefficient, is the ambient temperature, It is a function of particle size and humidity, taking into account the effect of particle size on surface water evaporation.

[0049] After each adjustment of the heating source power, air flow speed and evaporation rate, adjustments are made based on real-time monitoring data feedback to ensure the stability and efficiency of the drying process. If the moisture content on the surface or inside the material reaches the target value, the heating is stopped or the air flow is reduced to prevent over-drying. Through the feedback loop, various operations can be optimized in real time to ensure that the material reaches the desired dry state.

[0050] Through step S4, the system can accurately adjust the heating source power, airflow distribution and evaporation rate according to the moisture distribution model to ensure the optimization of moisture distribution, heat distribution and energy efficiency during the drying process. Based on real-time monitoring and feedback, the system ensures the best drying effect of the material while avoiding overheating or energy waste.

[0051] In one embodiment, step S5: based on the real-time collected temperature and humidity data, the incremental update mechanism is used to continuously optimize the particle size density curve of the material, and the heat transfer efficiency function is adjusted through the optimization to ensure the high efficiency and energy saving of the drying process. The specific implementation process is as follows: Based on the real-time collected temperature and humidity data, the particle size density curve of the material is corrected through the incremental update mechanism. This allows the curve to more accurately reflect the physical properties and moisture distribution of the current material. The incremental update process can be described by the following formula: ,in: Incremental update of the particle size density curve; The adjustment coefficient for incremental update controls the update step size; and They are the sensitivity of the particle size density curve to the material surface temperature and humidity; and are the changes in temperature and humidity respectively.

[0052] After the particle size density curve is updated, the corrected curve is used to adjust the heat transfer efficiency function to optimize the heat distribution and transfer efficiency. The heat transfer efficiency function determines the distribution of heating source power and airflow intensity. The power of the heating source and the airflow distribution are dynamically adjusted according to the function to achieve the optimal heat transfer and water evaporation process.

[0053] According to the changes in temperature and humidity and the updated heat transfer efficiency function, the heating source power and airflow intensity are gradually adjusted to optimize the water evaporation rate and avoid local overheating or overhumidification.

[0054] The heating source power and airflow intensity after each adjustment will be fed back to the system in real time. The system will continue to monitor the temperature and humidity changes of the material and timely correct the particle size density curve and heat transfer efficiency function. Through the continuous incremental update mechanism and dynamic adjustment process, the system can optimize the drying effect in real time, ensure that the moisture of the material evaporates evenly throughout the drying process, avoid over-drying or over-wetting, and ultimately achieve efficient and energy-saving drying effects.

[0055] Through step S5, the system uses the incremental update mechanism to continuously optimize the particle size density curve and heat transfer efficiency function, accurately adjust the heating source power and airflow intensity, and achieve efficient, energy-saving, and uniform drying effects. This process not only improves drying efficiency, but also effectively saves energy and ensures the best quality of materials during the drying process.

[0056] Based on the same inventive concept as the energy-saving control method for drying equipment in the aforementioned embodiment, Figure 2 As shown, this application provides an energy-saving control system for drying equipment. Through the collaborative work of multiple modules, intelligent control of the drying process is achieved, heat distribution is optimized, drying efficiency is improved, and energy consumption is effectively reduced. The specific implementation process is as follows: The data acquisition module is responsible for obtaining key parameters such as density, particle size, humidity and thickness of the drying material in real time. This module collects data through different types of sensors, such as: Density sensor: installed on the material inlet or conveyor belt, using laser or electromagnetic wave technology to measure the density of the material.

[0057] Particle size sensor: Use a high-resolution camera or laser scanner to capture or scan materials in real time, obtain the size of its particles, and calculate the particle size distribution through image processing algorithms.

[0058] Humidity sensor: installed on the material surface or under the conveyor belt to monitor the humidity changes of the material in real time.

[0059] Thickness sensor: Use laser rangefinder or contact sensor to obtain material thickness information.

[0060] These data are transmitted to the central processing unit through the data acquisition module for further analysis and processing.

[0061] The heat flow simulation module establishes a heat flow simulation model based on the three heat transfer modes of heat conduction, convection and radiation of the material, and calculates the heat transfer between the layers of the material in real time. Through this model, the system can predict the heat demand of different areas of the material and control the power regulation of the heating source. Specifically including: Hierarchical modeling: Materials are divided into different layers based on characteristics such as particle size, density and thickness, and each layer is modeled separately according to its heat transfer characteristics.

[0062] Dynamically adjust the heating source power: Based on the output of the heat flow simulation model, the system adjusts the heating source power of each layer of material in real time. For example, the system may increase the heating power of the surface material because the water evaporates faster; while for the lower layer of the material, the heat transfer is slower, so the system will reduce the heating source power to avoid energy waste.

[0063] The feedback control module dynamically adjusts the heating source power and airflow direction according to the data output by the heat flow simulation module to ensure that the heat is reasonably distributed inside and on the surface of the material. Specifically, it includes: Generation of particle size density curve: According to the particle size and density of the material, a particle size density curve is constructed to derive the heat transfer efficiency function.

[0064] Adjustment of heat transfer efficiency function: According to the changes of factors such as particle size, density and humidity, the power distribution of the heating source is adjusted in real time to optimize the heat distribution.

[0065] The genetic algorithm optimization module optimizes the feedback coefficient of the heat transfer efficiency function through genetic algorithms to ensure that the heat transfer efficiency during the heating process is optimal. The optimization process includes: Initial population generation: Based on the particle size density curve and the preliminary estimate of the heat transfer efficiency function, the initial population of multiple feedback coefficients is generated.

[0066] Fitness calculation and selection operation: Select the optimal feedback coefficient through multiple generations of iterations.

[0067] Crossover and mutation operations: Through crossover and mutation operations, new feedback coefficients are generated to further optimize the heat transfer efficiency.

[0068] The moisture monitoring module uses temperature and humidity sensors, infrared imaging sensors and other equipment to obtain real-time moisture content data on the surface and inside of the material. The system combines the heat flow simulation model to establish a moisture distribution model and adjust the heating source power, airflow direction and humidity in real time to optimize the moisture evaporation process. The specific steps include: Establishment of moisture distribution model: Through parameters such as moisture migration coefficient and evaporation source term, a model is established to describe the process of moisture migration from the inside of the material to the surface.

[0069] Dynamically adjust the power of the heating source: According to the moisture distribution, the system adjusts the heating power so that areas with high moisture content receive more heating, promoting moisture evaporation.

[0070] After each data collection, the incremental update module fine-tunes the particle size density curve based on the temperature and humidity data and adjusts the heat transfer efficiency function to optimize the drying process. The incremental update process includes: Incremental update of particle size density curve: According to the real-time temperature and humidity changes, the particle size density curve is adjusted through the incremental update mechanism to ensure that it more accurately reflects the physical properties of the current material.

[0071] Adjustment of heat transfer efficiency function: Adjust the heat transfer efficiency function through the corrected particle size density curve to optimize heat distribution and ensure reasonable adjustment of heating source power and airflow intensity.

[0072] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy-saving control method for drying equipment, characterized in that: The following steps are involved: S1: Obtain the density, particle size, humidity and thickness parameters of the dried material, perform layered analysis on the material, and establish a heat flow simulation model based on three heat transfer modes: heat conduction, convection and radiation: S2: The heat flow simulation model controls the heating source through a multi-layer heat transfer regulation mechanism. The multi-layer heat transfer regulation controls the power of the heating source according to the material properties to ensure that each layer of material meets the heat transfer conditions for material drying during the entire drying process; S3: Real-time detection of the surface temperature of the material humidity and air flow speed Data, dynamically adjust the heating process; according to the particle size of the material and density Constructing a particle size density curve and deriving a heat transfer efficiency function from the particle size density curve in: is a constant coefficient used to adjust the overall proportion of the model; a, b, c are constants obtained through data fitting, describing the efficiency of energy conversion of materials with different particle sizes and densities during heat transfer; the feedback coefficient is optimized by genetic algorithm , to obtain the optimal heat transfer efficiency; the optimization process of the genetic algorithm selects the optimal feedback coefficient through repeated iterations, so that the heat transfer efficiency function The value of reaches the optimal value, and the optimized feedback coefficient Used to dynamically adjust the heating source power and airflow direction and strength To ensure the best distribution of heat inside the material; based on the difference between the surface temperature and the internal temperature of the material, a nonlinear feedback mechanism is established to adjust the heating intensity; when the surface temperature of the material When it is too high, the heating power is reduced through feedback adjustment to avoid energy waste; On the contrary, when the surface temperature When it is lower, increase the heating intensity to speed up the transfer of heat; S4: By collecting the moisture change data on the surface and inside of the material in real time, a moisture distribution model is established to describe the migration process of moisture inside the material. Combined with the heat flow simulation model, the evaporation rate of moisture is adjusted. On this basis, the power of the heating source and the direction of the airflow are dynamically adjusted to maximize the distribution of heat and airflow to support the efficiency of the moisture evaporation process. S5: Based on the real-time temperature of the material ,humidity , adjust the particle size density curve In order to obtain the best drying effect, the incremental update mechanism is used to fine-tune the particle size density curve according to the results of each data collection, and gradually adjust the heat transfer efficiency function to optimize the drying process.

2. The energy-saving control method for drying equipment according to claim 1, characterized in that: The heat transfer efficiency function The optimal solution process includes: Formula: in, is the optimal solution optimized by genetic algorithm, is the heat transfer efficiency function expressed in time The efficiency of heat transfer.

3. The energy-saving control method for drying equipment according to claim 1, characterized in that: The incremental update mechanism in S5 includes: ,in: Incremental update of the particle size density curve; The adjustment coefficient for incremental update controls the update step size; and They are the sensitivity of the particle size density curve to the material surface temperature and humidity; and are the changes in temperature and humidity respectively.

4. The energy-saving control method for drying equipment according to claim 1, characterized in that: The genetic algorithm optimization process in S3 collects the humidity change data of the material in real time and dynamically adjusts the heat transfer efficiency function parameters so that the heat transfer efficiency is always maintained at the optimal state to ensure that energy consumption is minimized.

5. The energy-saving control method for drying equipment according to claim 1, characterized in that: The nonlinear feedback mechanism established in S3 includes: in, To feedback the temperature difference, is the material surface temperature, is the internal temperature of the material, is the nonlinear feedback coefficient.

6. The energy-saving control method for drying equipment according to claim 1, characterized in that: The dynamic adjustment in S3 includes: adjusting the power of the local heating source according to the materials of different particle sizes to ensure heating uniformity; for materials with larger particle sizes, adjusting the airflow angle and intensity to ensure that the hot airflow can effectively penetrate the material layer to avoid excessive evaporation of surface moisture.

7. The energy-saving control method for drying equipment according to claim 1, characterized in that: In S4, the moisture distribution model is established by collecting the moisture change data on the surface and inside of the material in real time, which specifically includes: real-time data collection: obtaining the material surface temperature in real time through the sensor , internal temperature and moisture content Indicates the surface moisture content of the material. Indicates the moisture content inside the material; establishes a moisture distribution model: according to the material parameters, combined with the heat flow simulation model, establishes a moisture distribution model for the material; the formula is: in, For any position and time The moisture content at represents the divergence, is the moisture migration coefficient, is the water evaporation source term.

8. The energy-saving control method for drying equipment according to claim 7, characterized in that: The moisture distribution model further includes: after each moisture data collection, the evaporation rate of the material surface and inside is calculated according to the particle size, humidity and temperature of the material; the evaporation rate is adjusted by the formula: in, is the evaporation coefficient, is the ambient temperature, As a function of particle size and humidity, the effect of particle size on surface water evaporation is considered.

9. The energy-saving control method for drying equipment according to claim 7, characterized in that: The moisture distribution model further includes: temperature and humidity gradient adjustment: real-time monitoring of different levels of materials during the drying process The temperature gradient between and humidity gradient ,in, Indicates the surface layer of the material. Indicates the bottom layer of the material; the heating intensity and airflow direction of different layers are adjusted by the following formula: in, is the heating power for each level, and To adjust the coefficient, adjust the heat transfer between different layers.

10. An energy-saving control system for drying equipment, using the energy-saving control method for drying equipment according to any one of claims 1 to 9, characterized in that: The system comprises: Data acquisition module, used to obtain drying material parameters in real time and transmit the data to the central processing unit; The heat flow simulation module establishes a heat flow simulation model according to the heat conduction, convection and radiation heat transfer modes of the material, and controls the power of the heating source based on the model to ensure that the material reaches the predetermined heat transfer conditions during the drying process; The feedback control module dynamically adjusts the power of the heating source and the direction of the airflow according to the data output by the heat flow simulation module, generates a particle size density curve according to the particle size and density of the material, and derives the heat transfer efficiency function; A genetic algorithm optimization module optimizes the feedback coefficient of the heat transfer efficiency function through a genetic algorithm, selects the optimal feedback coefficient and applies it to the adjustment of the heating source power and airflow parameters; The moisture monitoring module collects the moisture content data on the surface and inside of the material in real time, and establishes a moisture distribution model in combination with the heat flow simulation model to describe the process of moisture migration from the inside of the material to the surface; The incremental update module fine-tunes the particle size density curve according to the temperature and humidity data after each data collection, and adjusts the heat transfer efficiency function to optimize the drying process.

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