A prediction method and device for sludge thermal drying

Abstract

The present invention provides a method and device for predicting sludge thermal drying, comprising: obtaining thermal drying configuration data for sludge that needs to be thermally dried; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data; applying a sludge thermal drying model to set a sludge drying simulation environment; inputting the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the sludge thermal drying process based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, the sludge thermal drying prediction result including the predicted changes in moisture content, temperature, and flow field during the thermal drying process. The sludge thermal drying model is used to predict the sludge drying process, obtaining a sludge thermal drying prediction result including the changes in moisture content, temperature, and flow field during the sludge drying process, eliminating the need for physical experiments to collect sludge drying information and reducing the cost of acquiring sludge drying information.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a prediction method and device for sludge thermal drying. Background Art

[0002] Sludge, a byproduct of sewage treatment, is highly hazardous and bulky. Faced with increasing pressure on the environment and resources, there is an urgent need to improve sludge treatment capacity. Sludge drying, a crucial step in sludge treatment and resource utilization, removes excess water from sludge, significantly reducing its volume and, consequently, its storage and transportation costs. More importantly, dehydrated sludge is safe and stable, facilitating subsequent resource utilization. Therefore, the development of efficient sludge drying technology is crucial for sustainable development.

[0003] At present, in the process of sludge drying, in order to achieve deep dehydration of the sludge, a heat source is usually introduced into the drying equipment to achieve deep dehydration. Information such as the water evaporation rate of the sewage drying process and the moisture change of the sludge are helpful for the design and optimization of the drying equipment. At present, information on the sludge drying process is usually collected through physical experiments, which requires extremely high costs to collect sludge drying information using physical experiments. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a prediction scheme for sludge thermal drying, which can be used to predict the sludge thermal drying process, thereby predicting relevant information of the sludge thermal drying process without investing high costs to collect information on the sludge drying process.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A method for predicting sludge thermal drying, comprising:

[0007] Acquiring thermal drying configuration data of sludge that needs to be thermally dried; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data;

[0008] Use the pre-built sludge thermal drying model to set up the sludge drying simulation environment;

[0009] The thermal drying configuration data is input into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result. The sludge thermal drying prediction result includes the prediction of the moisture content change, temperature change and flow field change of the sludge during the thermal drying process.

[0010] The above method may optionally further include:

[0011] The sludge thermal drying prediction result and the sludge drying initial setting plan are analyzed to obtain the optimal sludge drying plan.

[0012] The above method optionally enables the sludge thermal drying model to predict the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data to obtain a sludge thermal drying prediction result, including:

[0013] determining a simulation duration based on the thermal drying attribute data in the thermal drying configuration data;

[0014] In the sludge drying simulation environment, the sludge thermal drying model processes the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation to simulate the numerical changes in the moisture content, temperature and flow field of the sludge during the thermal drying process within the simulation time to obtain the sludge thermal drying prediction results.

[0015] Optionally, the method described above includes applying a pre-built sludge thermal drying model to set up a sludge drying simulation environment, including:

[0016] Determining environmental parameters of the sludge thermal drying model;

[0017] Obtaining configuration data corresponding to the environmental parameters;

[0018] The configuration data is applied to construct a sludge drying simulation environment.

[0019] In the above method, optionally, when the sludge thermal drying model processes the thermal drying configuration data based on a preset airflow energy conservation equation and an evaporation rate equation, a preset evaporation latent heat correction coefficient is applied for correction.

[0020] A device for predicting sludge thermal drying, comprising:

[0021] An acquisition unit, configured to acquire thermal drying configuration data of sludge to be thermally dried; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data;

[0022] A setting unit, used for setting a sludge drying simulation environment by applying a pre-built sludge thermal drying model;

[0023] A prediction unit is used to input the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, wherein the sludge thermal drying prediction result includes the prediction of the moisture content change, temperature change and flow field change of the sludge during the thermal drying process.

[0024] The above device may optionally further include:

[0025] The optimization unit is used to analyze the sludge thermal drying prediction result and the sludge drying initial setting plan to obtain the optimal sludge drying plan.

[0026] In the above device, optionally, the prediction unit includes:

[0027] a first determining subunit, configured to determine a simulation duration based on the thermal drying attribute data in the thermal drying configuration data;

[0028] The simulation subunit is used to process the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation in the sludge drying simulation environment, so as to simulate the numerical changes in the moisture content, temperature and flow field of the sludge in the thermal drying process within the simulation time, and obtain the sludge thermal drying prediction result.

[0029] In the above device, optionally, the setting unit includes:

[0030] A second determining subunit is used to determine the environmental parameters of the sludge thermal drying model;

[0031] An acquisition subunit, configured to acquire configuration data corresponding to the environmental parameters;

[0032] The construction subunit is used to apply the configuration data to construct a sludge drying simulation environment.

[0033] In the above-mentioned device, optionally, when the simulation subunit executes the sludge thermal drying model to process the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation, a preset evaporation latent heat correction coefficient is applied for correction.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] The present invention provides a method and device for predicting sludge thermal drying, which comprises: obtaining thermal drying configuration data of sludge to be thermally dried; the thermal drying configuration data comprises sludge attribute data and thermal drying attribute data; applying a pre-built sludge thermal drying model to set a sludge drying simulation environment; inputting the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, wherein the sludge thermal drying prediction result comprises content of predicting changes in moisture content, temperature and flow field of the sludge during the thermal drying process. By using the sludge thermal drying model to predict the sludge drying process, the sludge thermal drying prediction results can be obtained, including the changes in moisture content, temperature and flow field in the thermal drying process of the sludge drying process. Analysis of the sludge thermal drying prediction results can optimize the sludge drying equipment and related processes of sludge drying. There is no need to use physical experiments to prevent the collection of sludge drying information, thereby reducing the cost of obtaining sludge drying information. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0037] Figure 1 A flow chart of a misdetection method for sludge thermal drying provided by an embodiment of the present invention;

[0038] Figure 2 A flow chart of a method for setting up a sludge drying simulation environment using a pre-built sludge thermal drying model provided in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of a thermal drying process of material particles provided in an embodiment of the present invention;

[0040] Figure 4 An exemplary diagram of a drying chamber provided in an embodiment of the present invention;

[0041] Figure 5 A qualitative display diagram of the changes in flow field temperature and sludge moisture content in the drying chamber provided by the present invention;

[0042] Figure 6 A schematic diagram of the quantitative statistical results of sludge moisture changes provided by the present invention;

[0043] Figure 7 A schematic structural diagram of a device for predicting sludge thermal drying provided by an embodiment of the present invention;

[0044] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0046] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0047] As can be seen from the background technology, sludge drying currently usually adopts the method of introducing a heat source to achieve deep dehydration, and belt sludge drying is widely used as an effective means of thermal drying of sludge. The sludge moves with the conveyor belt in the dryer and is in direct contact with the hot air flow. The moisture in the sludge is taken away during the convection between the gas and the sludge particles. This working process and equipment structure are not complicated, but there are still problems such as large equipment footprint and high energy consumption. Therefore, a large number of experimental explorations are needed to gain an in-depth understanding of the flow field and the spatiotemporal variation characteristics of the sludge in the belt dryer, as well as the influence of the equipment structure and operating process on the flow field and sludge properties. However, it is difficult to obtain real-time information on the sludge and flow field in the belt dryer using physical experiments, and the cost and labor cost of optimizing and iterating sludge experiments for dryer products are high.

[0048] In order to solve the above problems, the present invention provides a prediction scheme for sludge thermal drying. The application of this scheme can predict the changes in moisture content and flow field development during the sludge thermal drying process. The predicted information can be used for sludge drying mechanism research and for the optimal design of sludge drying equipment. This scheme does not require high-cost physical experimental collection to obtain relevant information on sludge drying, thereby reducing the cost of obtaining relevant information on sludge drying.

[0049] The present invention can be used in a wide variety of general-purpose or specialized computing environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multi-processor devices, and distributed computing environments including any of the above. The present invention can be applied to a processor in a computer or a server.

[0050] Reference Figure 1 , which is a flow chart of a misdetection method for sludge thermal drying provided by an embodiment of the present invention, and is specifically described as follows.

[0051] S101. Acquire thermal drying configuration data of sludge that needs to be thermally dried; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data.

[0052] The user inputs the thermal drying configuration data of the sludge to be dried, and the thermal drying configuration data includes sludge attribute data and thermal drying attribute data; further, the sludge attribute data includes but is not limited to sludge diameter, sludge length, initial sludge moisture content, total sludge mass and mud pile thickness; the thermal drying attribute data includes but is not limited to simulation time and the gas velocity, temperature and humidity of the drying gas, wherein the simulation time is the total time of simulating the sludge thermal drying process, which can be understood as the drying time.

[0053] The user can input the thermal drying configuration data through the interactive interface, that is, the thermal drying configuration data can be input according to actual needs. It is extremely simple to control and adjust the parameters of the sludge thermal drying process using the solution provided by the present invention. By controlling the changes in the parameters, relevant information of the sludge thermal drying process under different parameters can be obtained. Relevant information of the sludge thermal drying process under different parameters can be quickly obtained, which is convenient for subsequent analysis and research.

[0054] S102. Use the pre-built sludge thermal drying model to set up a sludge drying simulation environment.

[0055] The sludge thermal drying model of the present invention may be a CFD-DEM evaporation model, which may be used to simulate the sludge thermal drying process. Simulation is to predict the sludge thermal drying process, thereby obtaining a sludge thermal drying prediction result.

[0056] Before using the sludge thermal drying model for simulation, it is necessary to apply the sludge thermal drying model to set up the sludge thermal drying simulation environment.

[0057] Reference Figure 2 , which is a flow chart of a method for setting a sludge drying simulation environment using a pre-built sludge thermal drying model provided by an embodiment of the present invention, and is specifically described as follows:

[0058] S201. Determine environmental parameters of a sludge thermal drying model.

[0059] S202: Acquire configuration data corresponding to the environmental parameters.

[0060] S203. Apply the configuration data to build a sludge drying simulation environment.

[0061] It should be noted that the environmental parameters include but are not limited to the drawing of the geometric model grid of the sludge thermal drying model, the relevant parameters of the continuous phase CFD and the discrete phase DEM. Refer to Table 1, which is an example table of environmental parameters provided in an embodiment of the present invention.

[0062]

[0063]

[0064] Table 1

[0065] Furthermore, Table 1 also illustrates configuration data of environmental parameters. The configuration data can be set according to actual needs, and the user can input the configuration data through the interactive interface.

[0066] After the configuration data is input, the configuration data is applied to construct a sludge drying simulation environment, which is used for subsequent simulation of the sludge drying process.

[0067] S103. Inputting the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the sludge thermal drying process based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result. The sludge thermal drying prediction result includes predicted changes in moisture content, temperature, and flow field of the sludge during the thermal drying process. Preferably, the flow field changes include changes in velocity, temperature, and humidity.

[0068] It should be noted that the sludge thermal drying model predicts the thermal drying process of sludge based on the sludge drying simulation environment and thermal drying configuration data, and the process of obtaining the sludge thermal drying prediction result is as follows: determining the simulation time based on the thermal drying property data; in the sludge drying simulation environment, the sludge thermal drying model processes the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation to simulate the numerical changes in the moisture content, temperature and flow field of the sludge in the thermal drying process within the simulation time, and obtain the sludge thermal drying prediction result.

[0069] Furthermore, the simulation duration includes multiple unit times, the duration of each unit time is the same, and the specific duration of the unit time can be set according to actual conditions.

[0070] During the sludge drying simulation process, the changes in sludge moisture content and temperature affect the changes in the flow field. Changes in sludge moisture content and temperature will cause changes in the environment (flow field), and environmental changes will cause changes in sludge moisture content and temperature. For example, the simulation process is as follows:

[0071] Determine each unit time of the simulation duration, and sort each unit time in chronological order;

[0072] The first unit time is taken as the target unit time;

[0073] In the sludge drying simulation environment, the airflow energy conservation equation and the evaporation rate equation are used to process the sludge property data to obtain the sludge change information and environmental change information under the target unit time. The sludge change information includes the change information of the sludge moisture content and temperature; the environmental change information includes the change information of the flow field;

[0074] updating the sludge drying simulation environment based on the environmental change information, and updating the sludge attribute data based on the sludge change information;

[0075] Taking the next unit time as the new target unit time, and based on the updated sludge drying simulation environment and the updated sludge property data, return to the step of processing the sludge property data using the airflow energy conservation equation and the evaporation rate equation under the sludge drying simulation environment to obtain the sludge change information and environmental change information under the target unit time. After obtaining the sludge change information and environmental change information of all unit times, the sludge thermal drying prediction result is obtained based on the sludge change information and environmental change information of all unit times.

[0076] It should be noted that during the simulation process, the simulation is performed in sequence in multiple unit times. After each unit time is simulated, the prediction information within the unit time will be output. The prediction information includes the prediction data of the sludge properties output by the sludge drying simulation within the unit time, and the prediction data of the simulation environment obtained based on the prediction data of the sludge properties. Furthermore, the prediction information of the sludge properties is equivalent to the sludge change information mentioned above, and the prediction data of the simulation environment is equivalent to the environment change information mentioned above. Based on the prediction data of the sludge properties in the prediction information of the current unit time, the sludge property data for the simulation of the next unit time is updated, and based on the prediction data of the simulation environment in the prediction information of the current unit time, the sludge drying simulation environment is updated so that the simulation of the next unit time is based on the updated sludge drying simulation environment and the updated sludge property data. This cycle is repeated until the prediction information for each unit time is obtained. Furthermore, the initial sludge drying simulation environment per unit time is the constructed sludge drying simulation environment, and the sludge property data is the data obtained from the thermal drying configuration data.

[0077] Furthermore, the prediction data of the simulation environment during the simulation process includes process change data during the simulation process, and the prediction data of the sludge properties includes change data of the sludge properties during the simulation process, such as moisture content, temperature, etc.

[0078] It should be noted that the sludge thermal drying model includes an evaporation model and a CFD-DEM model. The evaporation model is embedded in the CFD-DEM model from the perspective of energy conservation. The heat required for the evaporation of moisture in the material particles serves as a source term for energy conservation in the fluid-particle system. The evaporation model includes the evaporation rate equation, and the CFD-DEM model includes the airflow energy conservation equation.

[0079] It should be noted that the evaporation rate equation can also be called the evaporation rate equation of material particles, as follows:

[0080]

[0081] in:

[0082] m p is the mass of the material particles, the unit of measurement is kg;

[0083] Y H2O is the moisture content in the material particles (defined as the ratio of the mass of water in the material to the dry basis of the material), kg w / kg db ;

[0084] t is time, the unit of measurement is s;

[0085] is the evaporation rate, the unit of measurement is kg / s, preferably, the evaporation rate Calculated by formula (2);

[0086]

[0087] in:

[0088] h m is the surface average mass transfer coefficient, measured in m / s, calculated by formula (6);

[0089] A p is the surface area of ​​the particle, measured in m 2 ;

[0090] ρ v,s and ρ v,∞ are the water vapor mass density on the particle surface and in the air flow, respectively, and the unit of measurement is kg / m 3 , calculated by formulas (3) to (5);

[0091] f(Y H2O ) is a dimensionless function related to the critical moisture content and the equilibrium moisture content and is calculated by formula (8);

[0092]

[0093]

[0094]

[0095] in:

[0096] P v,sat (T) is the saturated vapor pressure at a given temperature, Pa;

[0097] T p is the temperature of the particle surface, K;

[0098] ρ f is the fluid density, kg / m 3 ;

[0099] T f is the local fluid temperature, K;

[0100] p is the fluid pressure, Pa;

[0101] is the relative humidity of the local airflow, dimensionless;

[0102]

[0103] in:

[0104] Sh is the Sherwood number, dimensionless, calculated by formula (7);

[0105] D is the mass diffusion coefficient, kg / m 3 ;

[0106] d p is the equivalent diameter of the particle, m;

[0107]

[0108] in;

[0109] Re p is the local Reynolds number, dimensionless;

[0110] Sc is the Schmidt number, dimensionless;

[0111] Different types of water in sludge have different removal rates, f(Y H2O ) is expressed as:

[0112] in;

[0113] B is the damping factor, dimensionless, ranging from 1 to 2; Y H2O,cr is the critical moisture content, kg w / kg db ; Y H2O,eq For equilibrium moisture content, kg w / kg db ;

[0114] Furthermore, the airflow energy conservation equation is:

[0115]

[0116] in:

[0117] γ is the porosity, dimensionless;

[0118] u is the gas velocity, m / s;

[0119] h f is the specific enthalpy of the gas flow, J / mol;

[0120] Τ f is the stress tensor of the airflow, dimensionless;

[0121] q f is the heat flux, kJ / s;

[0122] Q p→f is the heat exchange between the airflow and the particle phase, kJ / s;

[0123]

[0124] in:

[0125] is the heat transfer rate between the airflow and the particles, kJ / s; the change of particle temperature with time can be obtained from the differential equation:

[0126]

[0127] in:

[0128] c p is the specific heat of the granular material, J / (kg·K);

[0129] is the total heat transfer rate of the particles, kJ / s;

[0130]

[0131] in:

[0132] is the heat transfer that occurs when particles come into contact with other particles or the wall, kJ / s;

[0133] is the convective heat transfer between particles and airflow, kJ / s;

[0134] is the latent heat of evaporation required to reduce the moisture content in the particle phase, kJ / s, calculated by formula (13);

[0135]

[0136] in:

[0137] h fg is the latent heat of evaporation of free water, kJ / kg, calculated by formula (14);

[0138] ξ is a correction coefficient suitable for describing the change process of sludge moisture content (related to the drying conditions of the sludge), which can be understood as the latent heat coefficient of sludge evaporation, dimensionless, and calculated by formula (15);

[0139]

[0140] in:

[0141] B, C, and n are model constants and dimensionless;

[0142] T cr is the critical temperature, K;

[0143] R is the gas constant, J / (mol·K);

[0144] M is the molar mass of the gas, g / mol;

[0145]

[0146] in:

[0147] h pile is the thickness of the local mud pile, m;

[0148] u f is the incoming gas velocity.

[0149] Furthermore, in the process of processing the thermal drying attribute data based on the preset airflow energy conservation equation and the evaporation rate equation, the sludge thermal drying model applies the preset evaporation latent heat correction coefficient for correction. It should be noted that the evaporation latent heat correction coefficient is the correction coefficient ξ in the above formula (15). The preset evaporation latent heat correction coefficient here can be understood as the sludge evaporation latent heat coefficient. This correction coefficient can be obtained through a large number of experiments. The sludge moisture correction is used to correct the temperature inside the sludge particles during the sludge drying process, the uneven distribution of moisture, and the influence of geometric changes such as expansion, contraction, cracking and hardening on the drying process, thereby improving the accuracy of the prediction process.

[0150] Furthermore, after obtaining the sludge thermal drying prediction results, the sludge thermal drying prediction results and the sludge drying initial setting plan can be analyzed to obtain the optimal sludge drying plan, thereby optimizing the equipment parameters of the sludge thermal drying equipment; the sludge thermal drying prediction results have a directional guiding role in the sludge thermal drying process, and the equipment parameters of the sludge thermal drying equipment are optimized based on the sludge thermal drying prediction results so that the sludge can obtain better drying results in the actual drying process, such as selecting a suitable drying time to avoid the problem of sludge still being dried when the sludge drying degree has reached the ideal state, resulting in the need to spend more drying costs.

[0151] The sludge thermal drying prediction results include the predicted moisture content curve and evaporation rate curve of the sludge during the thermal drying process. Furthermore, the sludge thermal drying prediction results can also include the predicted flow field change information of the sludge during the thermal drying process. The thermal drying prediction results can be used for the research and analysis of the sludge drying process, and can also be used for the optimal design of sludge drying equipment.

[0152] In the method provided by an embodiment of the present invention, thermal drying configuration data for sludge that needs to be thermally dried is obtained; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data; a sludge drying simulation environment is set up using a pre-built sludge thermal drying model; the thermal drying configuration data is input into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, which includes the predicted content of the moisture content change, temperature change, and flow field change of the sludge during the thermal drying process. By using the sludge thermal drying model to predict the sludge drying process, a sludge thermal drying prediction result including the content of the moisture content change, temperature change, and flow field change during the sludge drying process can be obtained. Analysis of the sludge thermal drying prediction result can optimize the sludge drying equipment and related processes of the sludge drying, without the need to use physical experiments to prevent the collection of sludge drying information, thereby reducing the cost of obtaining sludge drying information.

[0153] In order to specifically illustrate the feasibility of the prediction scheme for sludge thermal drying provided by the present invention and the accuracy of the obtained sludge thermal drying prediction results, the present invention provides a specific experimental content for illustration, which is described in detail as follows.

[0154] like Figure 3 The figure shows a schematic diagram of the thermal drying process of material particles according to an embodiment of the present invention. The CFD-DEM evaporation model for predicting the sludge drying process according to the present invention is suitable for describing the process of convection between airflow of a certain velocity, temperature, pressure, and humidity and material particles, while simultaneously conducting heat transfer (conduction) and mass transfer (evaporation). The particles as a whole have a certain temperature and moisture content.

[0155] like Figure 4 The figure shows a schematic diagram of a drying chamber provided by an embodiment of the present invention. Specifically, the present invention is implemented in a drying chamber with a diameter of 200 mm and a height of 800 mm. A porous tray is located 350 mm above the drying chamber to hold sludge particles. In the figure, 1 is the drying chamber airflow outlet, 2 is the porous tray within the drying chamber, 3 is the drying chamber airflow inlet, and 4 is the drying chamber cavity. Airflow is pumped into the drying chamber from the bottom, passes through the porous tray and the sludge layer, and then flows out from the top of the drying chamber. Airflow properties: The air velocity entering the drying chamber from the bottom is 0.67 m / s, the temperature is 80°C, the humidity is 0.05048, and the drying time is 120 minutes. Sludge properties: The sludge diameter is 8 mm, the sludge length is 40 mm, the initial sludge moisture content is 5.729 kgw / kgdb, the total sludge mass is 0.86 kg, and the mud pile thickness is 50 mm.

[0156] Before applying the CFD-DEM evaporation model described in the present invention, the basic settings for CFD-DEM simulation must be completed, including the drawing of the geometric model grid, continuous phase CFD and discrete phase DEM. The relevant parameter settings refer to the contents of Table 1 above.

[0157] The relevant equations of the sludge thermal drying model used in the present invention for predicting the sludge thermal drying process can refer to the above-mentioned formulas (1)-(15). The values ​​of some parameters in the formulas are explained in detail here, for example, the initial moisture content of the sludge is 5.729 kgw / kgdb; the damping factor is 1.6; the critical moisture content is 5; and the equilibrium moisture content is 0.2.

[0158] like Figure 5The diagram provides a qualitative display of the changes in the flow field temperature and sludge moisture content in the drying chamber provided by the present invention. It can be seen that as the drying proceeds, the moisture content of the sludge particles gradually decreases, and the sludge particles close to the side walls are dried earlier. This is because the air flow temperature at the bottom of the particles is higher, and channels for rapid air flow are easily formed between the sludge particles and the side walls, which are conducive to improving the drying efficiency. The temperature changes of the flow field in the drying chamber can also be seen. In the initial stage of the drying process, the air flow temperature above the sludge is significantly lower than the temperature below, because the wet and cold sludge takes away the heat of the air flow. As the drying process proceeds, the air flow temperature above the sludge is equivalent to the temperature below, which also shows a decrease in the sludge moisture content and an increase in its own temperature.

[0159] like Figure 6 The present invention provides a schematic diagram of the quantitative statistical results of sludge moisture changes. The left figure shows the relationship between sludge moisture content and time. It can be seen that the moisture content of the sludge gradually decreases as the drying progresses. Compared with the experimental results, it is found that the two sets of data are in good agreement. The right figure shows the relationship between the evaporation rate and the sludge moisture content. It can be seen that the curve shows a typical staged removal characteristic and is in good agreement with the experimental data. First, as the temperature of the sludge particles increases, the maximum evaporation rate is quickly reached, and then the water is removed at a constant rate. This stage means the evaporation of free water on the surface of the sludge particles. Then the evaporation rate gradually decreases, which belongs to the deceleration stage. This stage means the evaporation of capillary water and interstitial water between the particles. Finally, the evaporation rate drops to zero, which means that there is only unremovable bound water in the sludge particles. In summary, the CFD-DEM evaporation model for predicting the sludge thermal drying process proposed in the present invention has good predictive ability.

[0160] Computational fluid dynamics-discrete element method (CFD-DEM), a mature process engineering simulation method, addresses the motion of solid particles within the Eulerian-Lagrangian framework. This allows for direct and detailed consideration of numerous particle-scale factors, such as particle shape, porosity, and fluid-solid interactions. Grain drying, as one of the earliest and most widely used research areas in the field of drying, has developed evaporation models to describe the drying processes of various grains, such as wheat, corn, and soybeans, and can be combined with CFD-DEM models. These materials undergo minimal deformation during drying. However, sludge drying involves not only the removal of surface and internal moisture but also significant geometric changes such as expansion, contraction, cracking, and hardening. Although sludge drying, like grain drying, follows the typical staged drying process, existing CFD-DEM evaporation models are not suitable for describing this process. Furthermore, directly predicting all characteristics of sludge particle drying using CFD-DEM methods is difficult and computationally expensive. Therefore, this solution uses the lumped parameter method, treating the sludge particles as a whole, ignoring the uneven distribution of temperature and moisture inside the particles, and without deformation. The unreasonable impact that these assumptions may have on the prediction will be dealt with by the correction coefficients in the evaporation model obtained by experimental measurement. In order to solve the above-mentioned problems in the prior art, this application proposes a CFD-DEM evaporation model for predicting the sludge thermal drying process, which can achieve high-precision prediction of the sludge moisture content. The solution provided by the present invention includes a CFD-DEM evaporation model for predicting the sludge thermal drying process, which includes the evaporation rate of the material particles calculated according to the speed, temperature, pressure, humidity of the air flow and the temperature and moisture content of the material particles; wherein the removal rate of different types of water in the sludge is corrected according to the different stages of the moisture content of the material particles; wherein the evaporation model is embedded in the CFD-DEM model from the level of energy conservation, and the heat required for the evaporation of the moisture of the material particles is used as a source term of energy conservation in the fluid-particle system.

[0161] In the solution provided by the present invention, the CFD-DEM evaporation model for predicting the sludge thermal drying process can achieve high-precision prediction of the sludge moisture content and provide flow field information inside the dryer. By treating the temperature and moisture content of the sludge particles as scalar quantities on the particle scale and assuming that the sludge particles do not undergo complex deformation processes, the computational cost is greatly reduced; and the CFD-DEM evaporation model for predicting the sludge thermal drying process can simulate the sludge drying process in a belt sludge dryer (as well as a dryer that uses a heat source in direct contact with wet materials), and can deeply participate in the design and optimization process of such dryers; in addition to the above advantages, the CFD-DEM evaporation model for predicting the sludge thermal drying process expands the application field of the CFD-DEM evaporation model by proposing a correction coefficient suitable for describing the change process of the sludge moisture content (related to the drying conditions of the sludge). This method can be widely used in the research of drying materials in the fields of medicine, energy, chemical industry and environment.

[0162] and Figure 1 Correspondingly, the present invention also provides a prediction device for sludge thermal drying, which is used to support Figure 1 To implement the method shown, the device is set on a computer processor or a server.

[0163] Reference Figure 7 , which is a structural schematic diagram of a sludge thermal drying prediction device provided in an embodiment of the present invention, and the specific description is as follows.

[0164] An acquisition unit 301 is configured to acquire thermal drying configuration data of sludge that needs to be thermally dried; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data;

[0165] A setting unit 302 is used to set a sludge drying simulation environment using a pre-built sludge thermal drying model;

[0166] The prediction unit 303 is used to input the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result. The sludge thermal drying prediction result includes the prediction of the moisture content change, temperature change and flow field change of the sludge during the thermal drying process.

[0167] In the device provided by an embodiment of the present invention, thermal drying configuration data for sludge that needs to be thermally dried is obtained; the thermal drying configuration data includes sludge attribute data and thermal drying attribute data; a pre-built sludge thermal drying model is applied to set a sludge drying simulation environment; the thermal drying configuration data is input into the sludge thermal drying model, so that the sludge thermal drying model predicts the sludge thermal drying process based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, which includes the predicted content of the moisture content change, temperature change, and flow field change of the sludge during the thermal drying process. By using the sludge thermal drying model to predict the sludge drying process, a sludge thermal drying prediction result including the content of the moisture content change, temperature change, and flow field change during the sludge drying process can be obtained. Analysis of the sludge thermal drying prediction result can optimize the sludge drying equipment and related processes of the sludge drying, eliminating the need to use physical experiments to prevent the collection of sludge drying information, thereby reducing the cost of obtaining sludge drying information.

[0168] In another embodiment, the device may also be configured as:

[0169] The optimization unit is used to analyze the sludge thermal drying prediction result and the sludge drying initial setting plan to obtain the optimal sludge drying plan.

[0170] In another embodiment, the prediction unit 303 of the apparatus may also be configured as follows:

[0171] a first determining subunit, configured to determine a simulation duration based on the thermal drying attribute data in the thermal drying configuration data;

[0172] The simulation subunit is used to process the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation in the sludge drying simulation environment, so as to simulate the numerical changes in the moisture content, temperature and flow field of the sludge in the thermal drying process within the simulation time, and obtain the sludge thermal drying prediction result.

[0173] In another embodiment, the setting unit 302 of the device may also be configured to:

[0174] A second determining subunit is used to determine the environmental parameters of the sludge thermal drying model;

[0175] An acquisition subunit, configured to acquire configuration data corresponding to the environmental parameters;

[0176] The construction subunit is used to apply the configuration data to construct a sludge drying simulation environment.

[0177] In another embodiment, the simulation subunit of the device executes the sludge thermal drying model to process the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation, and applies the preset evaporation latent heat correction coefficient for correction.

[0178] An embodiment of the present invention further provides a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned prediction method for sludge thermal drying.

[0179] The embodiment of the present invention further provides an electronic device, the structural diagram of which is shown in FIG. Figure 8 As shown, it specifically includes a memory 401 and one or more instructions 402, wherein the one or more instructions 402 are stored in the memory 401 and are configured to be executed by one or more processors 403 to execute the one or more instructions 402 to perform the above-mentioned prediction method for sludge thermal drying.

[0180] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0181] The specific implementation processes and derivative methods of the above embodiments are all within the protection scope of the present invention.

[0182] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0183] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0184] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting sludge thermal drying, characterized in that: include: Obtain thermal drying configuration data for sludge that needs to be thermally dried; The thermal drying configuration data includes sludge property data and thermal drying property data; Use the pre-built sludge thermal drying model to set up the sludge drying simulation environment; Inputting the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, wherein the sludge thermal drying prediction result includes predictions of changes in moisture content, temperature, and flow field of the sludge during the thermal drying process; The sludge thermal drying model is used to predict the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data to obtain the sludge thermal drying prediction result, including: determining a simulation duration based on the thermal drying attribute data in the thermal drying configuration data; In the sludge drying simulation environment, the sludge thermal drying model processes the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation to simulate the numerical changes in the moisture content, temperature and flow field of the sludge during the thermal drying process within the simulation time to obtain the sludge thermal drying prediction results.

2. The method according to claim 1, characterized in that Also includes: The sludge thermal drying prediction result and the sludge drying initial setting plan are analyzed to obtain the optimal sludge drying plan.

3. The method according to claim 1, characterized in that The application of the pre-built sludge thermal drying model to set up the sludge drying simulation environment includes: Determining environmental parameters of the sludge thermal drying model; Obtaining configuration data corresponding to the environmental parameters; The configuration data is applied to construct a sludge drying simulation environment.

4. The method according to claim 1, wherein The sludge thermal drying model uses a preset evaporation latent heat correction coefficient for correction during the process of processing the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation.

5. A device for predicting sludge thermal drying, characterized in that: include: An acquisition unit, used for acquiring thermal drying configuration data of sludge that needs to be thermally dried; The thermal drying configuration data includes sludge property data and thermal drying property data; A setting unit, used for setting a sludge drying simulation environment by applying a pre-built sludge thermal drying model; a prediction unit, configured to input the thermal drying configuration data into the sludge thermal drying model, so that the sludge thermal drying model predicts the thermal drying process of the sludge based on the sludge drying simulation environment and the thermal drying configuration data, and obtains a sludge thermal drying prediction result, wherein the sludge thermal drying prediction result includes predictions of changes in moisture content, temperature, and flow field of the sludge during the thermal drying process; The prediction unit includes: a first determining subunit, configured to determine a simulation duration based on the thermal drying property data; The simulation subunit is used to process the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation in the sludge drying simulation environment, so as to simulate the numerical changes in the moisture content, temperature and flow field of the sludge in the thermal drying process within the simulation time, and obtain the sludge thermal drying prediction result.

6. The device according to claim 5, characterized in that Also includes: The optimization unit is used to analyze the sludge thermal drying prediction result and the sludge drying initial setting plan to obtain the optimal sludge drying plan.

7. The device according to claim 5, characterized in that The setting unit includes: A second determining subunit is used to determine the environmental parameters of the sludge thermal drying model; An acquisition subunit, configured to acquire configuration data corresponding to the environmental parameters; The construction subunit is used to apply the configuration data to construct a sludge drying simulation environment.

8. The device according to claim 5, characterized in that The simulation subunit executes the sludge thermal drying model to process the thermal drying configuration data based on the preset airflow energy conservation equation and evaporation rate equation, and applies the preset evaporation latent heat correction coefficient for correction.

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