A high-efficiency and low-energy consumption roasting pretreatment equipment for waste oil refining hydrogenation catalyst

Abstract

The invention discloses a high-efficiency and low-energy consumption roasting pretreatment method and equipment for waste oil refining hydrogenation catalysts, relates to the technical field of roasting pretreatment, and mainly solves the problems of low efficiency, high energy consumption and high carbon emission in the roasting process. The steps include: removing large particle impurities and dust in the catalyst, as well as oil stains, carbon deposits and pollutants on the surface through a screening module, dissolving sodium carbonate in water and impregnating the catalyst inside, roasting the mixture under high temperature environment, performing roasting post-treatment on the roasted mixture through a roasting post-treatment module, and further purifying metal salts in the solution through a purification module.

Description

Technical Field

[0001] The invention relates to the technical field of roasting pretreatment, and more specifically to a high-efficiency and low-energy consumption roasting pretreatment equipment for waste oil refining hydrogenation catalyst. Background Art

[0002] The recovery and treatment of waste oil refining hydrogenation catalysts is specifically a roasting pretreatment. After mixing with sodium carbonate, it is roasted at a high temperature. The roasting temperature is about 700 degrees, so that the rare metals W and Mo in the catalyst are converted into soluble salts and enter the wet purification. In the process, the C and S components contained in the original catalyst are used to release heat, which can reduce or eliminate the use of fuels such as coal. With the enhancement of environmental awareness and the increasing scarcity of resources, the efficient and low-energy roasting pretreatment method of waste oil refining hydrogenation catalysts will be more widely used. The efficient and low-energy roasting pretreatment method of waste oil refining hydrogenation catalysts is mainly used in refineries and petrochemical enterprises. In these places, waste catalysts are usually used for hydrogenation reactions to remove impurities such as sulfur and nitrogen in crude oil and improve the quality of crude oil.

[0003] The conventional high-efficiency and low-energy consumption calcination pretreatment method for waste oil refining hydrogenation catalysts still requires a large amount of heat energy during the calcination process, which reduces the efficiency of the calcination process; the conventional high-efficiency and low-energy consumption calcination pretreatment method for waste oil refining hydrogenation catalysts requires crushing during pretreatment during the calcination process, which will lead to the risk of spontaneous combustion and high energy consumption and high carbon emissions.

[0004] Therefore, the present invention discloses a high-efficiency and low-energy consumption calcination pretreatment equipment for waste oil refining hydrogenation catalyst, which has a simple, convenient and efficient preparation process and has high commercialization and industrial production potential. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned technologies, the present invention discloses a high-efficiency and low-energy consumption roasting pretreatment method and equipment for waste oil refining hydrogenation catalysts. The precise control of temperature is achieved through a heat transfer algorithm, thereby improving the efficiency of the roasting process. The spontaneous combustion and heat release of sulfur and carbon in the catalyst are used for blast roasting, thereby solving the problems of spontaneous combustion risk, high energy consumption and high carbon emissions.

[0006] The present invention adopts the following technical solutions:

[0007] A method and equipment for high-efficiency and low-energy consumption roasting pretreatment of waste oil refining hydrogenation catalyst, comprising the following steps:

[0008] Step 1: Screening and cleaning the catalyst: Using the screening module, remove large particles of impurities and dust in the catalyst, as well as oil stains, carbon deposits and pollutants on the surface;

[0009] Step 2, mixing the screened catalyst with sodium carbonate: dissolving the sodium carbonate in water and impregnating the sodium carbonate into the catalyst in step 1;

[0010] Step 3, roasting the mixture in a high temperature environment: roasting the mixture in a high temperature environment through a roasting module; the roasting module includes a temperature control unit, a roasting unit, an auxiliary unit and an exhaust unit, the temperature control unit roasts in a high temperature environment of 700°, and realizes precise control of temperature through a heat transfer algorithm to improve energy utilization efficiency; the roasting unit performs blast roasting through the spontaneous combustion and heat release of sulfur and carbon in the catalyst; the auxiliary unit blows the bottom blast device through the inside of the roasting furnace to evenly disperse the air from the bottom and blow it into the combustion material to ensure sufficient combustion; the exhaust unit collects and releases the combustion exhaust gas through the negative pressure on the top of the roasting furnace through a vertical roasting method, the output end of the temperature control unit is connected to the input end of the roasting unit, the output end of the roasting unit is connected to the input end of the auxiliary unit, and the output end of the auxiliary unit is connected to the input end of the exhaust unit;

[0011] Step 4, performing post-calcination treatment on the calcined catalyst: performing post-calcination treatment on the calcined mixture through a post-calcination treatment module;

[0012] Step 5, purifying the metal salts in the solution: further purifying the metal salts in the solution through a purification module.

[0013] As a further description of the above technical solution, the screening module includes a screening unit, a washing unit and a filtering unit. The screening unit uses a vibrating screen device to intercept materials of different particle sizes when they pass through the screen to achieve size classification of the materials; the washing unit flushes the materials through a spray tower to remove surface oil, carbon deposits and pollutants; the filtering unit uses a filter membrane to remove residual tiny particles, the output end of the screening unit is connected to the input end of the washing unit, and the output end of the washing unit is connected to the input end of the filtering unit.

[0014] As a further description of the above technical solution, the mixing module includes a metering unit, a mixing unit, a control unit and a water metering unit. The metering unit prepares the catalyst and the auxiliary material in a ratio of 1:0.2 to 1; the mixing unit uses sodium carbonate as an auxiliary material to mix with the catalyst, and the mixed sodium carbonate is further impregnated into the interior of the catalyst by dissolving it in water to achieve sufficient contact; the control unit adjusts the feed amount, mixing time and stirring speed of the raw materials through a programmable logic controller PLC, the output end of the metering unit is connected to the input end of the mixing unit, and the output end of the mixing unit is connected to the input end of the control unit; the water metering unit accurately measures and controls the water flow and water pressure.

[0015] As a further description of the above technical solution, the implementation method of the heat transfer algorithm is:

[0016] (1) Model establishment: During the roasting process, a mathematical model is established for the roasting furnace and the internal heat transfer process according to the heat transfer mode, which includes conduction, convection and radiation;

[0017] (2) Parameter definition: Determine the key parameters in the model, including the thermal conductivity, specific heat capacity, density, surface area, heat exchange coefficient, flow characteristics and temperature distribution of the gas in the furnace;

[0018] (3) Boundary condition setting: Set the boundary conditions of the model according to the actual situation, including fixed wall temperature, heat flux density, convection heat transfer coefficient and radiation exchange;

[0019] (4) Equation solving: Solve the model heat transfer equation according to the heat transfer function, and perform iterative calculation until convergence to a stable solution. The formula expression of the heat transfer function is:

[0020]

[0021] In formula (1), R represents the heat transfer function, i represents the order in which the function solves the data, n represents the end value of the function solution order, and α i Indicates the current data value being solved, δ indicates the coefficient of the function equation being solved, and ε indicates the function convergence factor. represents the function that solves the model heat transfer equation, Indicates iterative calculation of function calculation;

[0022] (5) Result verification and adjustment: The model is verified according to the analysis verification function. When there is a large deviation between the model prediction result and the actual observation value, the model parameters need to be adjusted and the model structure needs to be improved. The formula expression of the analysis verification function is:

[0023]

[0024] In formula (2), I represents the analysis verification function, κ represents the function data verification coefficient, β i represents the function value to be verified, i represents the function data verification order, λ represents the function prediction factor, Indicates the validation formula for the model. Represents the function's prediction of the model data;

[0025] (6) Control strategy implementation: The temperature distribution predicted by the model is used as the input of the temperature control unit, combined with the actual temperature feedback signal, and the output of the heating system is adjusted in real time through PID control and fuzzy control strategies;

[0026] (7) Real-time monitoring and optimization: The temperature parameters are continuously monitored during the roasting process, and the control strategy is adjusted in real time according to the actual situation. At the same time, the collected data can be used for subsequent process optimization.

[0027] As a further description of the above technical solution, the post-calcination treatment module includes a leaching unit, a dissolution unit and a separation unit. The leaching unit leaches the valuable metal components in the calcined catalyst through water; the dissolution unit dissolves the soluble metal salts by adjusting the pH value; the separation unit separates the insoluble residue through a filter press, the output end of the leaching unit is connected to the input end of the dissolution unit, and the output end of the dissolution unit is connected to the input end of the separation unit.

[0028] As a further description of the above technical solution, the purification module includes a washing unit, a metal recovery unit and a purification unit. The washing unit cleans the solid matter by using an aqueous solvent; the metal recovery unit separates the target ions from the impurity ions by ion exchange adsorption; the purification unit obtains a pure enriched solution of molybdenum after analysis, and then precipitates the molybdenum into molybdic acid by adding acid.

[0029] As a further description of the above technical solution, the performance detection module includes a thermal stability detection unit, an activity detection unit and a data analysis unit. The thermal stability detection unit evaluates the stability of the material under calcination temperature conditions through differential scanning calorimetry DSC; the data analysis unit analyzes and evaluates the data through MATLAB data analysis software, the output end of the thermal stability detection unit is connected to the input end of the activity detection unit, and the output end of the activity detection unit is connected to the input end of the data analysis unit.

[0030] As a further description of the above technical solution, the equipment includes a feed roller and a roasting furnace, the top of the feed roller is fixedly connected to a first stepper motor, the output shaft of the first stepper motor is fixedly connected to a stirring shaft, the outer surface of the stirring shaft is fixedly connected to a stirring rod, the number of the stirring rods is two groups, and they are symmetrically distributed, the bottom of the feed roller is fixedly connected to a roller bottom plate, the top of the feed roller is provided with a feed port, the top of the feed roller is fixedly connected to an L-shaped bracket through a bearing, the speed of the feed roller is controlled by a neural network control algorithm, the bottom of the L-shaped bracket is fixedly connected to a sealing roller, the inside of the roasting furnace is fixedly connected to a support ring, the support ring and the roller bottom plate are fixedly connected, the outer surface of the roasting furnace is fixedly connected to a first fixed block, one side of the first fixed block is fixedly connected to a second stepper motor, the output shaft of the second stepper motor is fixedly connected to a first rotating shaft, one end of the first rotating shaft is fixedly connected to a first bevel gear, and the first bevel gear The outer surface is meshedly connected with a second bevel gear, the outer surface of the second bevel gear is meshedly connected with a bevel gear ring, the bevel gear ring is rotatably sleeved on the outer side of the drum bottom plate, a second fixed block is fixedly connected above the outer surface of the sealing drum, the bottom of the second fixed block is fixedly connected with an electric push rod, the bottom of the electric push rod is fixedly connected with a third fixed block, the third fixed block and the sealing ring are fixedly connected, the outer surface of the drum bottom plate is fixedly connected with a third stepper motor, the output shaft of the third stepper motor is fixedly connected with a second rotating shaft, the outer surface of the second rotating shaft is rotatably connected with a flip bottom plate, the bottom of the flip bottom plate is fixedly connected with a mobile power supply, the bottom of the roasting furnace is fixedly connected with a fourth stepper motor, the output shaft of the fourth stepper motor is fixedly connected with the third rotating shaft, one end of the third rotating shaft is fixedly connected with a rotating tray, the outer surface of the roasting furnace is fixedly connected with a ventilation pipe, the other side of the outer surface of the roasting furnace is fixedly connected with an exhaust pipe, the bottom of the roasting furnace is fixedly connected with a support leg, and the number of the support legs is four.

[0031] As a further description of the above technical solution, the neural network control algorithm process is as follows:

[0032] (1) Data collection: Collect a set of training data sets, including input variables and corresponding output variables. There are m training samples, each of which contains n input variables and one output variable.

[0033] (2) Network structure design: Design the structure of the neural network, including the input layer, hidden layer and output layer. Suppose the input layer has p neurons, the hidden layer has q neurons, and the output layer has one neuron;

[0034] (3) Network parameter initialization: Initialize the weights and biases of the neural network. Here, a random initialization method is used.

[0035] (4) Forward propagation calculation: For each training sample, the input variable is input into the neural network, and the output value is calculated through forward propagation. Assume that the input of the i-th training sample is x i =[x i1 ,x i2 ,...,x in ], the corresponding output is y i , then the function from the input layer to the hidden layer in the forward propagation calculation process is:

[0036]

[0037] a hj =σ(z hj ) (4)

[0038] In formulas (3)-(4), Z hj represents the weighted input of the jth neuron in the hidden layer, The connection weight between the kth neuron in the input layer and the jth neuron in the hidden layer is represents the bias of the jth neuron in the hidden layer, represents the bias term of the jth neuron in the hidden layer, a hj represents the output of the jth neuron in the hidden layer, and n represents the input vector x i The dimension of x i Represents the input vector of the i-th training sample, containing n input variables, x ik represents the value of the kth input variable of the i-th training sample;

[0039] (5) Calculate the loss function: Use the predicted output and the actual output y i The difference between is used to calculate the loss function, which is as follows:

[0040]

[0041] In formula (5), represents the prediction error of the i-th sample, represents the regularization term, which is used to regularize the weights, λ is the regularization parameter, m represents the number of training samples, represents the predicted value of the i-th sample, y i represents the predicted value of the i-th sample, λ represents the regularization parameter, which is used to control the weight of the regularization term, n represents the number of neurons in the input layer, and h represents the number of neurons in the hidden layer;

[0042] (6) Model validation: Use a portion of the data set that was not involved in the training to input into the trained neural network, observe the difference between the output results and the expected results, and evaluate the performance and accuracy of the model.

[0043] The beneficial technical effects of the present invention compared with the prior art are as follows: the present invention discloses a method and equipment for pretreatment of waste oil refining hydrogenation catalysts with high efficiency and low energy consumption, which achieves precise control of temperature through a heat transfer algorithm and improves the activity performance of the catalyst; comprehensively detects and analyzes the catalyst performance through a performance detection module to improve the catalyst performance; and controls the feed drum through a neural network control algorithm to improve the efficiency of catalyst drying. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to understand and grasp the technical solution more intuitively and clearly, when describing the embodiments of the present invention or the prior art, drawings are often used to supplement and illustrate. It should be noted that the drawings are only one way of expressing the embodiments of the present invention or the prior art. In fact, the technical solution can also have other implementation methods and changes, which all fall within the protection scope of the present invention. Therefore, technicians can design other drawings as needed to implement the technical solution of the present invention, among which,

[0045] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0046] Figure 2 This is a schematic diagram of the roasting module structure of the present invention;

[0047] Figure 3 It is a schematic diagram of the three-dimensional structure of the equipment of the present invention;

[0048] Figure 4 It is a right side cross-sectional structural schematic diagram of the present invention;

[0049] Figure 5 It is a front cross-sectional structural schematic diagram of the present invention;

[0050] Among them, 1. the first stepper motor; 2. the stirring shaft; 3. the stirring rod; 4. the feeding roller; 5. the roller bottom plate; 6. the sealing roller; 7. the L-shaped bracket; 8. the supporting ring; 9. the roasting furnace; 10. the second stepper motor; 11. the first fixed block; 12. the first rotating shaft; 13. the first bevel gear; 14. the second bevel gear; 15. the bevel gear ring; 16. the second fixed block; 17. the third fixed block; 18. the electric push rod; 19. the sealing ring; 20. the third stepper motor; 21. the second rotating shaft; 22. the flip bottom plate; 23. the mobile power supply; 24. the fourth stepper motor; 25. the third rotating shaft; 26. the rotating tray; 27. the vent pipe; 28. the exhaust pipe; 29. ​​the supporting leg; 30. the feeding port. DETAILED DESCRIPTION

[0051] The following will clearly and completely explain the technical solutions in the embodiments of this invention through the drawings in the embodiments of this invention, and the description of the known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention. It is obvious that the described embodiments are only part of the embodiments of this invention, not all of the embodiments. At the same time, the description of the known structures and technologies is omitted in the following description to avoid unnecessary confusion of the concept of the present invention.

[0052] like Figure 1-Figure 5 As shown, a method and equipment for high-efficiency and low-energy consumption roasting pretreatment of waste oil refining hydrogenation catalysts comprises the following steps:

[0053] Step 1: Screening and cleaning the catalyst: Using the screening module, remove large particles of impurities and dust in the catalyst, as well as oil stains, carbon deposits and pollutants on the surface;

[0054] Step 2, mixing the screened catalyst with sodium carbonate: dissolving the sodium carbonate in water and impregnating the sodium carbonate into the catalyst in step 1;

[0055] Step 3, roasting the mixture in a high temperature environment: roasting the mixture in a high temperature environment through a roasting module; the roasting module includes a temperature control unit, a roasting unit, an auxiliary unit and an exhaust unit, the temperature control unit roasts in a high temperature environment of 700°, and realizes precise control of temperature through a heat transfer algorithm to improve energy utilization efficiency; the roasting unit performs blast roasting through the spontaneous combustion and heat release of sulfur and carbon in the catalyst; the auxiliary unit blows the bottom blast device through the inside of the roasting furnace to evenly disperse the air from the bottom and blow it into the combustion material to ensure sufficient combustion; the exhaust unit collects and releases the combustion exhaust gas through the negative pressure on the top of the roasting furnace through a vertical roasting method, the output end of the temperature control unit is connected to the input end of the roasting unit, the output end of the roasting unit is connected to the input end of the auxiliary unit, and the output end of the auxiliary unit is connected to the input end of the exhaust unit;

[0056] Step 4, performing post-calcination treatment on the calcined catalyst: performing post-calcination treatment on the calcined mixture through a post-calcination treatment module;

[0057] Step 5, purifying the metal salts in the solution: further purifying the metal salts in the solution through a purification module.

[0058] In a further embodiment, the screening module includes a screening unit, a washing unit and a filtering unit. The screening unit uses a vibrating screen device to intercept materials of different particle sizes when they pass through the screen to achieve size classification of the materials; the washing unit flushes the materials through a spray tower to remove surface oil, carbon deposits and pollutants; the filtering unit uses a filter membrane to remove residual tiny particles, the output end of the screening unit is connected to the input end of the washing unit, and the output end of the washing unit is connected to the input end of the filtering unit.

[0059] The working principle of the screening module is as follows: the screening unit uses a vibrating screen device as a core component. By accurately controlling the vibration frequency and amplitude, the vibrating screen can effectively intercept materials of different particle sizes on the screen. Larger particles cannot pass through the screen holes due to gravity, and are retained in the upper layer, realizing the preliminary size classification of the material; the washing unit uses a spray tower to deeply clean the material. The spray tower is equipped with multiple layers of nozzles, which can spray high-pressure water flow to wash the material in all directions without dead ends, which can not only effectively remove oil, carbon deposits and other pollutants on the surface of the material, but also ensure that the material itself will not be damaged during the cleaning process. At the same time, the spray tower is also equipped with temperature and pressure sensors to monitor the parameters of the cleaning process in real time to ensure the stability and consistency of the cleaning effect; the filtration unit uses a high-precision filter membrane to remove the residual tiny particles. The filter membrane has extremely high porosity and interception capacity, and can effectively intercept tiny particles with a diameter smaller than the set value. When the liquid or gas containing tiny particles passes through the filter membrane, the particles are retained on the membrane surface to form a filter cake, and the clear liquid or gas is discharged through the membrane pores.

[0060] In a further embodiment, the mixing module includes a metering unit, a mixing unit, a control unit and a water metering unit, wherein the metering unit prepares the catalyst and the auxiliary material in a ratio of 1:0.2 to 1; the mixing unit uses sodium carbonate as an auxiliary material to mix with the catalyst, and the mixed sodium carbonate is further impregnated into the interior of the catalyst by dissolving it in water to achieve sufficient contact; the control unit adjusts the feed amount, mixing time and stirring speed of the raw materials through a programmable logic controller PLC, the output end of the metering unit is connected to the input end of the mixing unit, and the output end of the mixing unit is connected to the input end of the control unit; the water metering unit accurately measures and controls the water flow and water pressure.

[0061] In a further embodiment, the heat transfer algorithm is implemented by:

[0062] (1) Model establishment: During the roasting process, a mathematical model is established for the roasting furnace and the internal heat transfer process according to the heat transfer mode, which includes conduction, convection and radiation;

[0063] (2) Parameter definition: Determine the key parameters in the model, including the thermal conductivity, specific heat capacity, density, surface area, heat exchange coefficient, flow characteristics and temperature distribution of the gas in the furnace;

[0064] (3) Boundary condition setting: Set the boundary conditions of the model according to the actual situation, including fixed wall temperature, heat flux density, convection heat transfer coefficient and radiation exchange;

[0065] (4) Equation solving: Solve the model heat transfer equation according to the heat transfer function, and perform iterative calculation until convergence to a stable solution. The formula expression of the heat transfer function is:

[0066]

[0067] In formula (1), R represents the heat transfer function, i represents the order in which the function solves the data, n represents the end value of the function solution order, and α i Indicates the current data value being solved, δ indicates the coefficient of the function equation being solved, and ε indicates the function convergence factor. represents the function that solves the model heat transfer equation, Indicates iterative calculation of function calculation;

[0068] (5) Result verification and adjustment: The model is verified according to the analysis verification function. When there is a large deviation between the model prediction result and the actual observation value, the model parameters need to be adjusted and the model structure needs to be improved. The formula expression of the analysis verification function is:

[0069]

[0070] In formula (2), I represents the analysis verification function, κ represents the function data verification coefficient, β i represents the function value to be verified, i represents the function data verification order, λ represents the function prediction factor, Indicates the validation formula for the model. Represents the function's prediction of the model data;

[0071] (6) Control strategy implementation: The temperature distribution predicted by the model is used as the input of the temperature control unit, combined with the actual temperature feedback signal, and the output of the heating system is adjusted in real time through PID control and fuzzy control strategies;

[0072] (7) Real-time monitoring and optimization: The temperature parameters are continuously monitored during the roasting process, and the control strategy is adjusted in real time according to the actual situation. At the same time, the collected data can be used for subsequent process optimization.

[0073] The working principle of the heat transfer algorithm is as follows: the role of the heat transfer algorithm is to achieve precise control of the temperature in the roasting furnace, improve the efficiency of energy utilization, and ensure that the catalyst achieves the required physical and chemical properties during the roasting process. Through mathematical modeling, parameter definition, boundary condition setting, equation solving, result verification and adjustment, control strategy implementation, real-time monitoring and optimization, etc., effective management and control of the complex heat transfer process in the roasting furnace can be achieved; the heat transfer algorithm solves the problems of inaccurate temperature control, low energy utilization and difficult to ensure roasting effect during the roasting of waste oil refining hydrogenation catalysts. Through precise temperature control, the activity, selectivity and stability of the catalyst can be improved, thereby improving the efficiency and product quality of the catalytic cracking process; through the currently solved data value α i , the function equation solution coefficient δ and the function convergence factor ε generation formula and The sum is used to solve the model heat transfer equation and iterate to find a stable solution; the coefficient κ and the function value β to be verified are verified through function data i And the function prediction factor λ to get the formula and The sum is used to verify and predict the model; at the same time, the differences between the heat transfer algorithm and the particle swarm optimization algorithm are shown in Table 1:

[0074] Table 1 Differences between heat transfer algorithm and particle swarm optimization algorithm

[0075]

[0076] In summary, the heat transfer algorithm is superior to the particle swarm optimization algorithm in terms of data verification efficiency and strategy implementation rate. It can be seen that the heat transfer algorithm is the best choice for the present invention.

[0077] In a further embodiment, the post-calcination treatment module includes a leaching unit, a dissolution unit and a separation unit. The leaching unit leaches the valuable metal components in the calcined catalyst through water and acid solution; the dissolution unit dissolves the soluble metal salts by adjusting the pH value; the separation unit separates the insoluble residue through membrane separation, the output end of the leaching unit is connected to the input end of the dissolution unit, and the output end of the dissolution unit is connected to the input end of the separation unit.

[0078] The working principle of the post-calcination treatment module is as follows: the leaching unit uses water and acid solution as leaching agents, and utilizes their chemical activity to effectively leach the valuable metal components in the calcined catalyst. In this process, the acid solution reacts with the metal oxide to generate soluble metal salts, thereby achieving the initial extraction of the metal; the dissolution unit further processes the soluble metal salts in the leachate. By precisely adjusting the pH value, the solubility of metal salts can be optimized, making them easier for subsequent separation and recovery. This step not only improves the metal extraction rate, but also facilitates the subsequent separation process; the separation unit uses membrane separation technology to separate insoluble residues. Membrane separation technology has the advantages of good selectivity, high efficiency, and low energy consumption. It can effectively separate metal ions from residues to obtain pure metal solutions. At the same time, this technology can also reduce the generation of waste liquid and reduce environmental pollution.

[0079] In a further embodiment, the purification module includes a washing unit, a metal recovery unit and a purification unit, wherein the washing unit washes solid matter by using an aqueous solvent; the metal recovery unit elutes metal ions from a medium by ion exchange; the purification unit heats the metal by vacuum distillation to purify the metal, the output end of the washing unit is connected to the input end of the metal recovery unit, and the output end of the metal recovery unit is connected to the input end of the purification unit.

[0080] The working principle of the purification module is as follows: the washing unit is responsible for pre-treating the solid material. It uses water solvent to deeply clean the solid material and effectively remove impurities and pollutants on the surface. This process not only helps to improve the efficiency of subsequent steps, but also ensures that the metal finally extracted has a higher purity; the metal recovery unit comes into play, which uses ion exchange technology to accurately elute metal ions from the medium. This process requires precise control of conditions to ensure that all valuable metal ions can be effectively recovered while avoiding secondary pollution to the environment; the purification unit is responsible for further improving the purity of the metal. It heats the metal through vacuum distillation, evaporates it and then condenses it, thereby removing impurities and impurities. This process needs to be carried out under strictly controlled temperature and pressure to ensure that the final metal has extremely high purity and performance stability.

[0081] In a further embodiment, the equipment includes a feed roller 4 and a roasting furnace 9, the top of the feed roller 4 is fixedly connected to a first stepper motor 1, the output shaft of the first stepper motor 1 is fixedly connected to a stirring shaft 2, the outer surface of the stirring shaft 2 is fixedly connected to stirring rods 3, the number of stirring rods is two groups, and the number of stirring rods is symmetrically distributed, the bottom of the feed roller 4 is fixedly connected to a roller bottom plate 5, the top of the feed roller 4 is provided with a feed port 30, the top of the feed roller 4 is fixedly connected to an L-shaped bracket 7 through a bearing, and the rotation speed of the feed roller 4 is determined by a neural network. The control algorithm is used for control, the bottom of the L-shaped bracket 7 is fixedly connected to a sealing roller 6, the interior of the roasting furnace 9 is fixedly connected to a support ring 8, the support ring 8 and the roller bottom plate 5 are fixedly connected, the outer surface of the roasting furnace 9 is fixedly connected to a first fixed block 11, one side of the first fixed block 11 is fixedly connected to a second stepping motor 10, the output shaft of the second stepping motor 10 is fixedly connected to a first rotating shaft 12, one end of the first rotating shaft 12 is fixedly connected to a first bevel gear 13, the outer surface of the first bevel gear 13 is meshedly connected to a second bevel gear 14, and the The outer surface of the second bevel gear 14 is meshedly connected with a bevel gear ring 15, and the bevel gear ring 15 is rotatably sleeved on the outer side of the drum bottom plate 5. A second fixed block 16 is fixedly connected above the outer surface of the sealing drum 6, and an electric push rod 18 is fixedly connected to the bottom of the second fixed block 16, and a third fixed block 17 is fixedly connected to the bottom of the electric push rod 18. The third fixed block 17 and the sealing ring 19 are fixedly connected. The outer surface of the drum bottom plate 5 is fixedly connected to a third stepping motor 20, and the output shaft of the third stepping motor 20 is fixedly connected to a second rotating shaft 21. The second rotating shaft The outer surface of 21 is rotatably connected to a flip base plate 22, the bottom of the flip base plate 22 is fixedly connected to a mobile power supply 23, the bottom of the roasting furnace 9 is fixedly connected to a fourth stepper motor 24, the output shaft of the fourth stepper motor 24 is fixedly connected to a third rotating shaft 25, one end of the third rotating shaft 25 is fixedly connected to a rotating tray 26, the outer surface of the roasting furnace 9 is fixedly connected to a ventilation pipe 27, the other side of the outer surface of the roasting furnace 9 is fixedly connected to an exhaust pipe 28, the bottom of the roasting furnace 9 is fixedly connected to support legs 29, and the number of the support legs 29 is four.

[0082] The working principle of the waste oil refining hydrogenation catalyst high-efficiency and low-energy consumption roasting pretreatment equipment is as follows: first, the feed port 30 is opened, and the catalyst medicine is added into the feed drum 4, at which time the sealing ring 19 is in a sealed state, and then the cleaning liquid is injected through the feed port, and then the first stepper motor 1 is started, and the first stepper motor 1 drives the stirring shaft 2 to rotate, and the stirring shaft 2 drives the stirring rod to rotate, so as to clean the catalyst medicine, and after the impurities in the catalyst are cleaned out, the electric push rod 18 is then started, and the electric push rod 18 drives the second fixed block 16 to move, and the second fixed block drives the sealing ring 19 to move downward, so that the sealing drum is sealed. An opening appears at the bottom of the drum 6 to release the sewage, and then the spin-drying operation is carried out. First, the second stepper motor 10 is started, and the second stepper motor drives the first rotating shaft 12 to rotate, the first rotating shaft 12 drives the first bevel gear 13 to rotate, the first bevel gear 13 drives the second bevel gear 14 to rotate, the second bevel gear 14 drives the bevel gear ring 15 to rotate, and the bevel gear ring 15 drives the drum bottom plate 5 to rotate, thereby driving the entire feed drum 4 to rotate, and the feed drum 4 rotates at a high speed to throw out the residual water. The rotation speed of the feed drum 4 in the spin-drying operation is controlled by a neural network control algorithm to make its rotation speed adjustable, which is more efficient. In order to adapt to the actual industrial process, the dried catalyst medicine is then released into the roasting furnace 9 for roasting operation. First, the third stepper motor 20 is started, and the third stepper motor 20 drives the second rotating shaft 21 to rotate. The second rotating shaft 21 drives the flip bottom plate 22 to rotate. The flip bottom plate 22 flips to pour the catalyst medicine into the roasting furnace 9. Then the roasting furnace 9 is heated, and an inert gas is introduced through the ventilation pipe 27 to keep the state of the catalyst stable. The inert gas is discharged from the roasting furnace 9 through the exhaust pipe 28. At the same time, the high-temperature roasted gasified material is also discharged from the outside of the roasting furnace 9, so as to achieve the purification of the catalyst. Operation, through cleaning, spin-drying and roasting operations, the cleaning operation can effectively remove dirt and impurities on the surface of the object, keep the object clean and hygienic, and ensure the accuracy and reliability of subsequent operations. The spin-drying operation can quickly remove excess moisture on the surface of the object, reduce drying time, and avoid moisture interference with subsequent operations. The roasting operation can remove volatile substances in the object and change the structure and properties of the object. It is very important for the material preparation and processing process. By selecting appropriate cleaning, spin-drying and roasting methods, the quality, performance and reliability of the catalyst are ensured to meet actual needs and operational requirements.

[0083] In a further embodiment, the neural network control algorithm process is as follows:

[0084] (1) Data collection: Collect a set of training data sets, including input variables and corresponding output variables. There are m training samples, each of which contains n input variables and one output variable.

[0085] (2) Network structure design: Design the structure of the neural network, including the input layer, hidden layer and output layer. Suppose the input layer has p neurons, the hidden layer has q neurons, and the output layer has one neuron;

[0086] (3) Network parameter initialization: Initialize the weights and biases of the neural network. Here, a random initialization method is used.

[0087] (4) Forward propagation calculation: For each training sample, the input variable is input into the neural network, and the output value is calculated through forward propagation. Assume that the input of the i-th training sample is x i =[x i1 ,x i2 ,...,x in ], the corresponding output is y i , then the function from the input layer to the hidden layer in the forward propagation calculation process is:

[0088]

[0089] a hj =σ(z hj ) (4)

[0090] In formulas (3)-(4), Z hj represents the weighted input of the jth neuron in the hidden layer, The connection weight between the kth neuron in the input layer and the jth neuron in the hidden layer is represents the bias of the jth neuron in the hidden layer, represents the bias term of the jth neuron in the hidden layer, a hj represents the output of the jth neuron in the hidden layer, and n represents the input vector x i The dimension of x i Represents the input vector of the i-th training sample, containing n input variables, xi k represents the value of the kth input variable of the i-th training sample;

[0091] (5) Calculate the loss function: Use the predicted output and the actual output y i The difference between is used to calculate the loss function, which is as follows:

[0092]

[0093] In formula (5), represents the prediction error of the i-th sample, represents the regularization term, which is used to regularize the weights, λ is the regularization parameter, m represents the number of training samples, represents the predicted value of the i-th sample, yi represents the predicted value of the i-th sample, λ represents the regularization parameter, which is used to control the weight of the regularization term, n represents the number of neurons in the input layer, and h represents the number of neurons in the hidden layer;

[0094] (6) Model validation: Use a portion of the data set that was not involved in the training to input into the trained neural network, observe the difference between the output results and the expected results, and evaluate the performance and accuracy of the model.

[0095] The working principle of the neural network control algorithm is as follows: first, data acquisition is performed, the data acquisition system is connected to the sensor to obtain the real-time measurement data of the sensor, and the angular velocity sensor is used on the roller feeder to monitor the rotation speed of the roller feeder in real time. Detailed data related to the rotation speed of the roller feeder is collected and recorded within a fixed period. After the sensor data is collected, preprocessing is first performed, including filtering, noise reduction and calibration operations to remove noise and outliers, and ensure the accuracy and stability of the data. Next, the analog signal is converted into digital form by analog-to-digital conversion. Then, data standardization is performed to make the data fall within a specific range for subsequent calculation and analysis. Data extraction is performed based on the actual rotation speed requirements of the feed roller required in the actual cleaning process to reduce the dimension of the data and retain important information. Finally, data storage and transmission are performed. The processed data is transmitted to the controller for further processing and analysis. The entire data processing process is to extract useful information, reduce noise and interference, and make the data more interpretable and usable. Then the network structure design is carried out. First, according to the actual needs and data characteristics, an appropriate neural network structure is designed. Here, the multi-layer perceptron MLP is selected, and then the number of nodes in the input layer and the number of nodes in the hidden layer are determined. Adjust according to the actual situation. In addition, the number of nodes in the output layer should match the dimension of the expected rotation speed of the feed roller; then the parameters are initialized, and the weights and biases of the neural network are initialized. Here, the random initialization method is used for initialization. The initialization of weights and biases is particularly important for the training process of the neural network. It directly affects the convergence speed and performance of the model. Then the forward propagation calculation is performed. Let the input of the i-th training sample be x i =[x i1 ,x i2 ,...,x in ], the corresponding output is y i , the function is and a hj =σ(z hj );where Z hj represents the weighted input of the jth neuron in the hidden layer, The connection weight between the kth neuron in the input layer and the jth neuron in the hidden layer is Next, the loss function is calculated. First, the predicted value of the neural network is compared with the actual output value, and the loss function is calculated. in Represents the square of the difference between the predicted value and the true value, representing the prediction error of the i-th sample, Represents the regularization term, which is used to regularize the weights. The loss function calculation is used to evaluate model performance, guide model optimization, support backpropagation algorithm, and perform model selection and comparison. It provides a measure of the difference between the model prediction results and the actual target, helps the optimization algorithm adjust the model parameters, and minimizes the prediction deviation, thereby improving the accuracy and generalization ability of the model. At the same time, the loss function can also compare the performance of different models, guide the application of domain knowledge, and provide guidance and constraints for model training and optimization. Then backpropagation is performed to adjust the parameters. The backpropagation algorithm is used to update the weights and biases of the neural network according to the gradient of the loss function. Through optimization algorithms such as gradient descent, the network parameters are gradually adjusted to gradually reduce the loss function and improve the accuracy of the model. Finally, the model is verified and tuned. The verification data set is used to evaluate the performance of the trained neural network model, and then the model is tuned according to the verification results. The network structure and regularization parameters are tuned to improve the generalization ability of the model.

[0096] Although the present invention has been described by the above specific embodiments, it should be understood by the technician that these embodiments are provided only as examples and do not limit the scope and application of the present invention. Without departing from the principles and essence of the present invention, the technician can make various omissions, substitutions and changes to the present invention to achieve results with substantially similar functions, and these embodiments also belong to the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A method for pretreatment of waste oil refining hydrogenation catalyst by high efficiency and low energy consumption roasting, characterized in that: The steps include: Step 1: Screening and cleaning the catalyst: Using the screening module, remove large particles of impurities and dust in the catalyst, as well as oil stains, carbon deposits and pollutants on the surface; Step 2, mixing the screened catalyst with sodium carbonate: dissolving the sodium carbonate in water and impregnating the sodium carbonate into the catalyst in step 1; The catalyst performance is comprehensively tested and analyzed by a performance testing module to improve the catalyst performance; the performance testing module includes a thermal stability testing unit, an activity testing unit and a data analysis unit, the thermal stability testing unit evaluates the stability of the material under the calcination temperature condition by differential scanning calorimetry DSC; the data analysis unit analyzes and evaluates the data by MATLAB data analysis software, the output end of the thermal stability testing unit is connected to the input end of the activity testing unit, and the output end of the activity testing unit is connected to the input end of the data analysis unit; Step 3, roasting the mixture in a high temperature environment: roasting the mixture in a high temperature environment through a roasting module; the roasting module includes a temperature control unit, a roasting unit, an auxiliary unit and an exhaust unit, the temperature control unit roasts in a high temperature environment of 700°, and realizes precise control of temperature through a heat transfer algorithm to improve energy efficiency; the roasting unit roasts with air through the spontaneous combustion and heat release of sulfur and carbon in the catalyst; the auxiliary unit blows the bottom air blowing device inside the roasting furnace to evenly disperse the air from the bottom into the combustion material to ensure full combustion; The exhaust unit collects and releases the combustion exhaust gas through the negative pressure of the roasting furnace top in a vertical roasting manner, the output end of the temperature control unit is connected to the input end of the roasting unit, the output end of the roasting unit is connected to the input end of the auxiliary unit, and the output end of the auxiliary unit is connected to the input end of the exhaust unit; Step 4, performing post-calcination treatment on the calcined catalyst: performing post-calcination treatment on the calcined mixture through a post-calcination treatment module; Step 5, purifying the metal salts in the solution: further purifying the metal salts in the solution through a purification module; The heat transfer algorithm is implemented as follows: (1) Model establishment: During the roasting process, a mathematical model is established for the roasting furnace and the internal heat transfer process according to the heat transfer mode, which includes conduction, convection and radiation; (2) Parameter definition: Determine the key parameters in the model, including the thermal conductivity, specific heat capacity, density, surface area, heat exchange coefficient, flow characteristics and temperature distribution of the gas in the furnace; (3) Boundary condition setting: Set the boundary conditions of the model according to the actual situation, including fixed wall temperature, heat flux density, convection heat transfer coefficient and radiation exchange; (4) Equation solving: Solve the model heat transfer equation according to the heat transfer function, and perform iterative calculation until convergence to a stable solution. The formula expression of the heat transfer function is: （1）； In formula (1), represents the heat transfer function, Indicates the order in which the function solves the data. Indicates the end value of the function solution order, Indicates the data value currently being solved. represents the coefficients of the function equation, represents the function convergence factor, represents the function that solves the model heat transfer equation, Indicates iterative calculation of function calculation; (5) Result verification and adjustment: The model is verified according to the analysis verification function. When there is a large deviation between the model prediction result and the actual observation value, the model parameters need to be adjusted and the model structure needs to be improved. The formula expression of the analysis verification function is: （2）； In formula (2), Represents the analysis and verification function, represents the function data verification coefficient, represents the function value to be verified, Indicates the function data verification order, represents the function predictor, Indicates the validation formula for the model. Represents the function's prediction of the model data; (6) Control strategy implementation: The temperature distribution predicted by the model is used as the input of the temperature control unit, and combined with the actual temperature feedback signal, the output of the heating system is adjusted in real time through PID control and fuzzy control strategies; (7) Real-time monitoring and optimization: Continuously monitor temperature parameters during the roasting process, adjust the control strategy in real time according to actual conditions, and use the collected data for subsequent process optimization.

2. The high-efficiency and low-energy consumption calcination pretreatment method for waste oil refining hydrogenation catalyst according to claim 1 is characterized in that: The screening module includes a screening unit, a washing unit and a filtering unit. The screening unit uses a vibrating screen device to intercept materials of different particle sizes when they pass through the screen to achieve size classification of the materials; the washing unit flushes the materials through a spray tower to remove surface oil, carbon deposits and pollutants; the filtering unit uses a filter membrane to remove residual tiny particles, the output end of the screening unit is connected to the input end of the washing unit, and the output end of the washing unit is connected to the input end of the filtering unit.

3. The high-efficiency and low-energy consumption calcination pretreatment method for waste oil refining hydrogenation catalyst according to claim 1 is characterized in that: The mixing module includes a metering unit, a mixing unit, a control unit and a water metering unit. The metering unit prepares the catalyst and the auxiliary material in a ratio of 1:0.2-1; the mixing unit uses sodium carbonate as an auxiliary material to mix with the catalyst, and the mixed sodium carbonate is further impregnated into the interior of the catalyst by dissolving it in water to achieve the purpose of full contact; the control unit adjusts the feeding amount, mixing time and stirring speed of the raw materials through a programmable logic controller PLC, the output end of the metering unit is connected to the input end of the mixing unit, and the output end of the mixing unit is connected to the input end of the control unit; the water metering unit accurately measures and controls the water flow and water pressure.

4. The high-efficiency and low-energy consumption calcination pretreatment method for waste oil refining hydrogenation catalyst according to claim 1 is characterized in that: The post-calcination treatment module includes a leaching unit, a dissolving unit and a separation unit. The leaching unit leaches the valuable metal components in the calcined catalyst through water; the dissolving unit dissolves the soluble metal salts by adjusting the pH value; The separation unit separates the insoluble residue by means of a filter press, the output end of the leaching unit is connected to the input end of the dissolution unit, and the output end of the dissolution unit is connected to the input end of the separation unit.

5. The high-efficiency and low-energy consumption calcination pretreatment method for waste oil refining hydrogenation catalyst according to claim 1 is characterized in that: The purification module includes a washing unit, a metal recovery unit and a purification unit. The washing unit washes the solid matter with a water solvent; the metal recovery unit separates the target ions from the impurity ions by ion exchange adsorption; the purification unit obtains a pure enriched solution of molybdenum after analysis, and then precipitates the molybdenum into molybdic acid by adding acid.

6. A high-efficiency and low-energy consumption calcination pretreatment equipment for waste oil refining hydrogenation catalyst, characterized in that: A method for pretreatment of waste oil refining hydrogenation catalysts with high efficiency and low energy consumption by roasting is applied as described in any one of claims 1 to 5, wherein the equipment comprises a feed roller (4) and a roasting furnace (9), the top of the feed roller (4) is fixedly connected to a first stepper motor (1), the output shaft of the first stepper motor (1) is fixedly connected to a stirring shaft (2), the outer surface of the stirring shaft (2) is fixedly connected to stirring rods (3), the number of the stirring rods is two groups, and they are symmetrically distributed, the bottom of the feed roller (4) is fixedly connected to a roller bottom plate (5), the top of the feed roller (4) is provided with a feed port (30), the top of the feed roller (4) is fixedly connected to an L-shaped support via a bearing The L-shaped bracket (7) is provided with a sealing roller (6) fixedly connected to the bottom of the L-shaped bracket (7), a support ring (8) is fixedly connected to the inside of the roasting furnace (9), the support ring (8) is fixedly connected to the roller bottom plate (5), a first fixed block (11) is fixedly connected to the outer surface of the roasting furnace (9), a second stepping motor (10) is fixedly connected to one side of the first fixed block (11), an output shaft of the second stepping motor (10) is fixedly connected to a first rotating shaft (12), one end of the first rotating shaft (12) is fixedly connected to a first bevel gear (13), and an outer surface of the first bevel gear (13) is meshed with a first gear. The sealing roller (6) is connected to a second bevel gear (14), the outer surface of the second bevel gear (14) is meshingly connected to a bevel gear ring (15), the bevel gear ring (15) is rotatably sleeved on the outer side of the drum bottom plate (5), a second fixed block (16) is fixedly connected above the outer surface of the sealing roller (6), the bottom of the second fixed block (16) is fixedly connected to an electric push rod (18), the bottom of the electric push rod (18) is fixedly connected to a third fixed block (17), the third fixed block (17) and the sealing ring (19) are fixedly connected, the outer surface of the drum bottom plate (5) is fixedly connected to a third stepping motor (20), the output shaft of the third stepping motor (20) is fixedly connected to a second rotating shaft (21), The outer surface of the second rotating shaft (21) is rotatably connected to a flip bottom plate (22), the bottom of the flip bottom plate (22) is fixedly connected to a mobile power source (23), the bottom of the roasting furnace (9) is fixedly connected to a fourth stepper motor (24), the output shaft of the fourth stepper motor (24) is fixedly connected to a third rotating shaft (25), one end of the third rotating shaft (25) is fixedly connected to a rotating tray (26), the outer surface of the roasting furnace (9) is fixedly connected to a ventilation pipe (27), the other side of the outer surface of the roasting furnace (9) is fixedly connected to an exhaust pipe (28), and the bottom of the roasting furnace (9) is fixedly connected to support legs (29), and the number of the support legs (29) is four.

7. The high-efficiency and low-energy consumption calcination pretreatment equipment for waste oil refining hydrogenation catalyst according to claim 6 is characterized by: The neural network control algorithm process is as follows: (1) Data collection: Collect a set of training data sets, including input variables and corresponding output variables. Suppose there are m training samples, each sample contains n input variables and one output variable; (2) Network structure design: Design the structure of the neural network, including the input layer, hidden layer, and output layer. Suppose the input layer has p neurons, the hidden layer has q neurons, and the output layer has one neuron; (3) Network parameter initialization: Initialize the weights and biases of the neural network. Here, a random initialization method is used. (4) Forward propagation calculation: For each training sample, the input variable is input into the neural network, and the output value is calculated through forward propagation. Assume that the input of the i-th training sample is , the corresponding output is , then the function from the input layer to the hidden layer in the forward propagation calculation process is: （3）； （4）； In formulas (3)-(4), represents the weighted input of the jth neuron in the hidden layer, The connection weight between the kth neuron in the input layer and the jth neuron in the hidden layer is , represents the bias of the jth neuron in the hidden layer, represents the bias term of the jth neuron in the hidden layer, represents the output of the jth neuron in the hidden layer, and n represents the input vector The dimension of Represents the input vector of the i-th training sample, containing n input variables, represents the value of the kth input variable of the i-th training sample; (5) Calculate the loss function: use the predicted output and the actual output The difference between is used to calculate the loss function, which is as follows: （5）； In formula (5), represents the prediction error of the i-th sample, represents the regularization term, which is used to regularize the weights, λ is the regularization parameter, m represents the number of training samples, represents the predicted value of the i-th sample, represents the predicted value of the i-th sample, represents the regularization parameter, which is used to control the weight of the regularization term, n represents the number of neurons in the input layer, and h represents the number of neurons in the hidden layer; (6) Model validation: Use a portion of the data set that was not involved in the training to input into the trained neural network, observe the difference between the output results and the expected results, and evaluate the performance and accuracy of the model.

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