A high-pressure roller mill intelligent operation control semi-physical experiment method

Abstract

The application discloses a kind of high-pressure roller mill intelligent operation control semi-physical experiment methods, comprising the following steps: industrial computer A, work intelligence machine B, industrial computer C are connected with switch D data;Industrial computer A simulates high-pressure roller mill process flow in Metsim;Industrial computer A simulates high-pressure roller mill equipment roll gap, material valve displacement control in MATLAB;Work intelligence machine B designs work intelligence machine controller and realizes set value optimization;Industrial computer C carries out data monitoring.The application realizes the simulation of high-pressure roller mill running process, and controls high-pressure roller mill equipment roll gap, material valve displacement by designing work intelligence machine controller;And by embedding optimization decision algorithm, set value is optimized to improve production quality;While designing operation monitoring interface, monitoring running process data, realize running process visualization.Stable data communication channel is established between each module to ensure the stability of semi-physical simulation platform operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-pressure roller mill model simulation, and particularly relates to a high-pressure roller mill intelligent operation control semi-physical experiment method. BACKGROUND

[0002] The high-pressure roller mill operation process is often used as a pre-separation process in mineral processing to save costs and improve processing efficiency. The high-pressure roller mill device is the core equipment in the high-pressure roller mill operation process, and has been designed since the 1980s with the goal of high efficiency and low energy consumption. It is widely used in different fields such as metallurgy, chemical industry, and cement. The high-pressure roller mill operation process is still based on traditional control theory, and the parameter setting relies on human experience, which has the problems of large result error and unstable working condition. At the same time, the parameter setting is mainly aimed at high yield, but ignores the cost and environmental problems caused by high energy consumption.

[0003] However, the research and improvement of the high-pressure roller mill operation process need to be tested in the production field to improve the operation efficiency of the high-pressure roller mill device, reduce energy consumption, prolong the service life of the device, and ensure production safety. Some problems may be encountered in the actual testing process, such as whether the method can adapt to the complex and variable field environment, whether it has high stability and reliability, etc. Therefore, in order to solve these problems, how to conveniently and effectively verify the effectiveness of the control and optimization decisions of the researchers, reduce the risk and experimental cost, and establish a semi-physical simulation system around the core device high-pressure roller mill to provide a platform for further research. SUMMARY

[0004] In order to simulate the operation process of the high-pressure roller mill through the simulation platform, optimize the production process and process parameters, improve the production efficiency, reduce the production cost, and provide more economic benefits for enterprises, and at the same time provide an experimental platform for researchers to verify the feasibility and effect of new equipment or new technology, reduce the research and development risk, and save the research and development cost, the present application provides a high-pressure roller mill intelligent operation control semi-physical experiment method.

[0005] In order to achieve the above technical purposes, the present application adopts the following technical solutions:

[0006] A high-pressure roller mill intelligent operation control semi-physical experiment method, comprising the following steps:

[0007] S1: The industrial computer A, the industrial computer B, and the industrial computer C are all in data connection with the switch D;

[0008] S2: The industrial computer A simulates the high-pressure roller mill process flow in Metsim;

[0009] S3: Industrial computer A simulates high-pressure roller mill device roll gap, material valve displacement control in MATLAB;

[0010] S4: Designing industrial computer controller in industrial computer B and realizing set value optimization;

[0011] S5: Industrial computer C carries out data monitoring.

[0012] Compared with the prior art, the beneficial effects of the present application are:

[0013] The present application realizes the simulation of the high-pressure roller mill operation process, and controls the high-pressure roller mill device roll gap and material valve displacement through the design of the industrial computer controller; and optimizes the set value through the embedded optimization decision algorithm to improve the production quality; at the same time, the running monitoring interface is designed to monitor the running process data, and the running process visualization is realized. Stable data communication channels are established between the modules to ensure the stability of the semi-physical simulation platform operation. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0015] Figure 1 It is a high-pressure roller mill process flowchart simulated in Metsim.

[0016] Figure 2 It is a high-pressure roller mill device roll gap control chart simulated in MATLAB.

[0017] Figure 3 It is a high-pressure roller mill device material valve displacement control chart simulated in MATLAB.

[0018] Figure 4 It is a control flowchart of the industrial computer controller.

[0019] Figure 5 It is a set value optimization flowchart. DETAILED DESCRIPTION

[0020] The specific embodiments of the present application will be further described below in combination with the drawings.

[0021] As shown in the drawings, a high-pressure roller mill intelligent operation control semi-physical experiment method comprises the following steps: Figures 1-5

[0022] S1: Industrial computer A, industrial computer B and industrial computer C are all connected with switch D. ​

[0023] S2: The industrial computer A simulates the high-pressure roller grinding process flow in Metsim;

[0024] Step S2 specifically includes:

[0025] S21: Select the feeding belt, high-pressure roller grinding equipment, transition belt, vibrating screen, transport belt, and return belt in Metsim, and configure the parameters of each device respectively;

[0026] S22: The discharge port of the feeding belt is connected to the inlet of the high-pressure roller grinding equipment, the discharge port of the high-pressure roller grinding equipment is connected to the feeding position of the transition belt, the discharge position of the transition belt is connected to the inlet of the vibrating screen, the discharge position of the qualified outlet of the vibrating screen is connected to the feeding position of the transport belt, the discharge position of the unqualified outlet of the vibrating screen is connected to the inlet of the return belt, and the discharge position of the return belt is connected to the feeding position of the feeding belt, and the return belt enters the feeding belt at the same time as the new material, forming a crushing loop;

[0027] S23: Write the roll gap and throughput of the high-pressure roller grinding equipment from Matlab, and transmit the unqualified particle mass on the return belt, the qualified particle mass on the transport belt, and the single-ton energy consumption of the high-pressure roller grinding equipment to the industrial computer C through the switch D for data monitoring;

[0028] S3: The industrial computer A simulates the displacement control of the roll gap and the material-saving valve of the high-pressure roller grinding equipment in MATLAB;

[0029] Step S3 specifically includes:

[0030] S31: Simulate the roll gap control of the high-pressure roller grinding equipment in MATLAB:

[0031] S311: Connect the roll gap control solenoid valve electric signal from the controller of the industrial computer B to the roll gap control solenoid valve S signal port;

[0032] S312: Connect the roll gap control valve hydraulic oil module to the roll gap control solenoid valve T interface and the roll gap control ideal hydraulic source T interface to provide hydraulic oil; connect the roll gap control ideal hydraulic source P interface to the roll gap control solenoid valve P interface to provide pressure;

[0033] S313: Connect the A and B interfaces of the roll gap control solenoid valve to the A and B interfaces of the roll gap control hydraulic actuator respectively, connect the C port of the roll gap control hydraulic actuator to the ground, and connect the R interface to the dynamic roller mass block, and connect the dynamic roller mass block to the roll gap control displacement sensor;

[0034] S32: Simulate the displacement control of the material-saving valve in MATLAB:

[0035] S321: The electric signal of the movable roller side material limiting valve solenoid valve from the intelligent machine B controller is connected to the S signal port of the movable roller side material limiting valve solenoid valve; the hydraulic oil module of the movable roller side material limiting valve is connected to the T interface of the movable roller side material limiting valve solenoid valve and the T interface of the ideal hydraulic source of the movable roller side material limiting valve to provide hydraulic oil; the P interface of the ideal hydraulic source of the movable roller side material limiting valve is connected to the P interface of the movable roller side material limiting valve solenoid valve to provide pressure; the A and B interfaces of the movable roller side material limiting valve solenoid valve are respectively connected to the A and B interfaces of the hydraulic actuator of the movable roller side material limiting valve, the C port of the hydraulic actuator of the movable roller side material limiting valve is grounded, and the R interface is connected to the mass block of the movable roller side material limiting valve, and the mass block of the movable roller side material limiting valve is connected to the displacement sensor of the movable roller side material limiting valve;

[0036] S322: The electric signal of the fixed roller side material limiting valve solenoid valve from the intelligent machine B controller is connected to the S signal port of the fixed roller side material limiting valve solenoid valve; the hydraulic oil module of the fixed roller side material limiting valve is connected to the T interface of the fixed roller side material limiting valve solenoid valve and the T interface of the ideal hydraulic source of the fixed roller side material limiting valve to provide hydraulic oil; the P interface of the ideal hydraulic source of the fixed roller side material limiting valve is connected to the P interface of the fixed roller side material limiting valve solenoid valve to provide pressure; the A and B interfaces of the fixed roller side material limiting valve solenoid valve are respectively connected to the A and B interfaces of the hydraulic actuator of the fixed roller side material limiting valve, the C port of the hydraulic actuator of the fixed roller side material limiting valve is grounded, and the R interface is connected to the mass block of the fixed roller side material limiting valve, and the mass block of the fixed roller side material limiting valve is connected to the displacement sensor of the fixed roller side material limiting valve;

[0037] S323: The displacement of the movable roller side material limiting valve and the displacement of the fixed roller side material limiting valve are fitted by a fitting function to obtain the value of the throughput of the high-pressure roller mill equipment;

[0038] Step S323 specifically includes:

[0039] S3231: Since the material limiting valve device is a symmetrical structure, the moving distances of the movable roller side material limiting valve and the fixed roller side material limiting valve are equal, so:

[0040]

[0041] wherein, is the displacement of the material limiting valve, is the displacement of the movable roller side material limiting valve, is the displacement of the fixed roller side material limiting valve;

[0042] S3232: A fitting function is used to fit a linear regression model of the displacement of the material limiting valve and the throughput of the high-pressure roller mill equipment, and the formula is

[0043]

[0044] ;

[0045] S3233: The throughput of the high-pressure roller mill equipment is transmitted to Metsim;

[0046] S4: design the industrial computer controller in the industrial computer B and realize the set value optimization;

[0047] Step S4 specifically includes:

[0048] S41: the industrial computer B reads the actual value of the roll gap measured by the displacement sensor in the roll gap control in Matlab in the industrial computer A through the switch D , the actual value of the displacement of the movable roll side material valve measured by the displacement sensor of the movable roll side material valve , the actual value of the displacement of the fixed roll side material valve measured by the displacement sensor of the fixed roll side material valve ;

[0049] S42: respectively judge whether the difference between the optimized roll gap set value , the optimized movable roll side material valve displacement set value , and the optimized fixed roll side material valve displacement set value is within the threshold range, if it is within the threshold range, it is ended; if it is not within the threshold range, step S43 is executed;

[0050] The optimization process of the roll gap set value, the movable roll side material valve displacement set value and the fixed roll side material valve displacement set value in step S42 is as follows:

[0051] S421: select the first target of single ton energy consumption high pressure roller operation process, the motion power consumption of high pressure roller mill equipment movable roll and fixed roll rotation is as follows:

[0052]

[0053] Among them, is the angular velocity of the roll, is the torque, is the linear velocity, is the length of the roll, is the tangential force received by the roll;

[0054] The tangential force received by the roll is as follows:

[0055]

[0056] Among them, is the roll diameter, is the material pressure on the roll, is the compression zone start angle, is the release start angle, is the friction coefficient, which is 0.16;

[0057] The general formula of the material pressure on the roll is as follows:

[0058]

[0059]

[0060] in, For the parameters that need to be fitted, For the compaction of the extruded material, This refers to the throughput of the high-pressure roller equipment. For roll gap;

[0061] The relationship between total power consumption and motion power consumption is as follows:

[0062]

[0063] in, This refers to the voltage of the roller motor. This refers to the roller motor current. The power factor is 0.8. This is a constant coefficient with a value of 1.15;

[0064] Therefore, the energy consumption per ton is defined as:

[0065]

[0066] Energy consumption per ton reflects the energy consumed in producing one ton of product and is a key indicator for measuring energy efficiency in the production of high-pressure rollers.

[0067] S422: Selecting the qualified particle mass fraction is the second objective in the high-pressure roller operation process. The pressure roller of the high-pressure roller mill is divided into three parts: the two sides are the edge areas, and the middle is the center area. The formula for the qualified particle distribution of each part is as follows:

[0068] Full roll qualified particle distribution:

[0069]

[0070] in, The percentage of qualified particles for the entire roller. The number of sections for the roller. For the first roller One partition, For the first roller Mass fraction of qualified particles within each partition;

[0071] Edge-qualified particle distribution:

[0072] in, The mass fraction of qualified particles at the edge. , the distance of the two side edges of the roller, the largest integer less than or equal to , the largest integer greater than or equal to ;

[0073] Central qualified particle distribution:

[0074]

[0075] The calculation formula is as follows:

[0076]

[0077] wherein, is the velocity component in the direction of , is the vertical distance from the entrance of the compression zone to the release zone, is the breakage rate, The calculation formula is as follows:

[0078]

[0079]

[0080]

[0081] wherein, is the power consumption of each block, is the retention rate of each block in the particle bed compression zone, is the breakage rate, ;

[0082] is the cumulative percentage of mineral particles, the value of which is calculated for different types of particles, and the calculation formula is as follows:

[0083]

[0084] wherein, is the particle of type, is the particle of type, is the mass fraction of the compression zone type particle, is the breakage rate of the particle of type in the sub-zone, is the breakage function, and the calculation formula is as follows:

[0085] ​

[0086] wherein, Dp is the particle size, is a constant;

[0087] Since the field data can only derive the mass fraction of qualified particle size, therefore, The type particle only considers one type of particle, i.e. the mass fraction of qualified particle size; therefore The value of is rewritten as:

[0088]

[0089] Therefore, the central qualified particle distribution formula is rewritten as shown below:

[0090]

[0091] wherein, The tangential force is related to the throughput The tangential force is related to the throughput is related to the roll gap is a nonlinear function;

[0092] Therefore, according to the rewritten central qualified particle distribution formula, the mass fraction of qualified particles of the high-pressure roller equipment is related to the throughput and the roll gap of the high-pressure roller equipment;

[0093] S423: The general formula of the material-to-roller pressure and the rewritten central qualified particle distribution formula involve parameters that need to be identified. According to the field data collected by the high-pressure roller mill equipment, the lsqnonlin solver is used to solve them, and the following results are obtained: = 0.000722, = 0.382, = 2402, = 3045, and it is determined that = 3, i.e. the high-pressure roller is divided into three parts;

[0094] S424: Determine the objective function, the first optimization objective, minimize the energy consumption per ton, denoted as:

[0095]

[0096] The second optimization objective, maximize the mass fraction of qualified particles, is denoted as:

[0097]

[0098] S425: Select the design variables for optimization as follows:

[0099]

[0100] The constraint conditions are as follows:

[0101] The ratio of the roll gap to the roll diameter of the high-pressure roll is referred to as the relative gap, and the ratio ranges from 0.01 to 0.02, that is:

[0102]

[0103] The throughput of the high-pressure roll device should be not less than 2700 t / h according to the requirements of the enterprise, that is:

[0104]

[0105] S426: Select the optimal setting value of the high-pressure roll operation process;

[0106] The step S426 is specifically implemented as follows:

[0107] S4261: Initialize the population: randomly generate a certain number of individuals to form an initial population, and initialize the algorithm parameters: the maximum number of iterations N = 300, the population size Num = 30, the crossover ratio P_Cro = 0.7, the mutation ratio P_Mutation = 0.4, and the mutation probability m = 0.02;

[0108] S4262: Evaluate the fitness: for each individual, calculate its value on each objective function;

[0109] S4263: Fast non-dominated sorting: determine the dominance relationship between individuals according to the fitness function value of the individual, and when there is no individual that is superior to a certain individual, the individual is a non-dominated individual; traverse all individuals in the population to obtain different levels of non-dominated frontiers;

[0110] S4264: Calculate the crowding distance: for each non-dominated level, calculate the crowding distance between individuals, that is the crowding distance between the individual and adjacent and The significance of this step is that the crowding distance can maintain the diversity of the population;

[0111] S4265: Select the new population: according to the non-dominated sorting and the crowding distance, select a certain number of individuals as the parent population;

[0112] S4266: Generate the offspring population: generate a certain number of offspring individuals through crossover and mutation operations;

[0113] S4267: Update the population: combine the parent population and the offspring population to form a new population;

[0114] S4268: generating the optimal solution: performing fast non-dominated sorting and crowdedness calculation, selecting N individuals as the parent population, and recording the parent population as the optimal solution set when the termination condition is met, and if the termination condition is not met, jumping to step S4262;

[0115] S427: after step S426, 30 schemes are obtained, selecting the qualified particle mass fraction above 75%, the single ton energy consumption range in , and retaining the schemes meeting the conditions;

[0116] S428: performing weighted processing on the target according to the following formula, the weight , and selecting the scheme with the highest score as the optimal scheme:

[0117]

[0118] S429: after the optimal scheme is confirmed and applied, the optimal set value is transmitted to the industrial computer B, and the control variable is gradually reached to the set value through the industrial computer B;

[0119] S43: starting the industrial computer controller to perform PID control, calculating the output roll gap control solenoid valve electric signal, the dynamic roller side material cutting valve solenoid valve electric signal, and the fixed roller side material cutting valve solenoid valve electric signal, and inputting them into the Matlab simulation of step S3;

[0120] S44: returning to step S41 until the difference in step S42 is within the threshold range;

[0121] S5: the industrial computer C performs data monitoring.

[0122] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A semi-physical experimental method for intelligent operation control of a high-pressure roller mill, characterized in that, Includes the following steps: S1: Industrial computer A, intelligent machine B, and industrial computer C are all connected to switch D via data connection. S2: Industrial computer A simulates the high-pressure roller mill process in Metsim; S3: Industrial computer A simulates the displacement control of the roller gap and material-saving valve in a high-pressure roller mill in MATLAB; S4: Design the AI ​​controller in AI Machine B and implement setpoint optimization; Specifically, it includes: S41: The AI ​​machine B reads the actual roll gap value measured by the roll gap control displacement sensor in the Matlab of the industrial computer A through the switch D. The actual displacement value of the material-saving valve on the moving roller side, measured by the displacement sensor of the material-saving valve on the moving roller side. The actual value of the displacement of the fixed roll side material-saving valve measured by the displacement sensor. ; S42: Determine each one separately With the optimized roll gap setting , With the optimized displacement setting value of the moving roller side material saving valve , With the optimized displacement setting value of the fixed roller side material-saving valve If the difference is within the threshold range, the process ends; otherwise, step S43 is executed. The optimization process for the roll gap setting, the moving roll side material-saving valve displacement setting, and the stationary roll side material-saving valve displacement setting in step S42 is as follows: S421: Selecting the primary objective of the high-pressure roller operation process with a single ton of energy consumption, the motion power consumption of the rotating and stationary rollers of the high-pressure roller mill is as follows: ； in, The angular velocity of the roller, For torque, For linear velocity, The length of the roller, This represents the tangential force acting on the roller. The tangential force acting on the roller is shown below: ； in, The diameter of the roller is... For the pressure of the material against the rollers, The starting angle of the compression zone. To release the starting angle, The coefficient of friction; Material roller pressure The general formula is as follows: ， ， in, For the parameters that need to be fitted, For the compaction of the extruded material, This refers to the throughput of the high-pressure roller equipment. For roll gap; The relationship between total power consumption and motion power consumption is as follows: ； in, This refers to the voltage of the roller motor. This refers to the roller motor current. The power factor is 0.

8. The coefficient is constant. Therefore, the energy consumption per ton is defined as: ； S422: Selecting the qualified particle mass fraction is the second objective in the high-pressure roller operation process. The pressure roller of the high-pressure roller mill is divided into three parts: the two sides are the edge areas, and the middle is the center area. The formula for the qualified particle distribution of each part is as follows: Full roll qualified particle distribution: ； in, The percentage of qualified particles for the entire roller. The number of sections for the roller. For the first roller One partition, For the first roller Mass fraction of qualified particles within each partition; Edge-qualified particle distribution: ; in, The mass fraction of qualified particles at the edge. , This is the distance between the two edges of the roller. Less than or equal to The largest integer, greater than or equal to The largest integer; Central qualified particle distribution: ； The calculation formula is as follows: ； in, for The velocity component in the direction, This is the vertical distance from the inlet of the compression zone to the release zone. For breakage rate, The calculation formula is as follows: ， ， ， in, For the first The power consumption of the partition. For the first The retention rate of the zone in the particle bed compression zone For fracture rate, ; This represents the cumulative percentage of mineral particles, and its value varies depending on the type of particle. The calculation formula is as follows: ； in, for Type of particles, for Type of particles, For compression area Mass fraction of type particles, for Type of particles in The fragmentation rate of the partition. The breaking function is calculated using the following formula: ； in, For particle size, It is a constant; Since the field data can only be used to deduce the mass fraction of qualified particle sizes, therefore, For this type of particle, only one type of particle is considered, namely the mass fraction of the qualified particle size; therefore The value is rewritten as: Therefore, the formula for the distribution of qualified particles at the center can be rewritten as follows: ； S423: The parameters that need to be identified in the general formula for the pressure of the material against the roller and the rewritten formula for the distribution of qualified central particles are solved using the lsqnonlin solver based on data collected on-site from the high-pressure roller mill equipment. , , , The value, and determine The value; S424: Determine the objective function. The first optimization objective is to minimize the energy consumption per ton, denoted as: ； The second optimization objective, maximizing the mass fraction of qualified particles, is denoted as: ； S425: The following design variables are selected for optimization: ； The constraints are as follows: The ratio of the roll gap to the diameter of the high-pressure roll is called the relative clearance, and its range is 0.01-0.02, that is: ； According to the company's requirements, the throughput of the high-pressure roller equipment should not be less than 2700 t / h, that is: ； S426: Select the optimal setting value for the high-pressure roller operation process; S427: After step S426, 30 schemes will be obtained. Select the scheme with a qualified particle mass fraction of over 75% and a single-ton energy consumption range of [missing information]. Select the eligible options; S428: The target is weighted according to the following formula, with the weights... The solution with the highest score is selected as the optimal solution. ； S429: After the optimal solution is confirmed and applied, the optimal set value is transmitted to the AI ​​B, and the AI ​​B controls the variables to gradually reach the set value. S43: Start the AI ​​controller to perform PID control, calculate and input the output of the roll gap control solenoid valve, the moving roll side material saving valve solenoid valve, and the fixed roll side material saving valve solenoid valve into the Matlab simulation in step S3; S44: Return the value from step S41 until the difference in step S42 is within the threshold range; S5: Industrial computer C performs data monitoring.

2. The semi-physical experimental method for intelligent operation control of high-pressure roller mill according to claim 1, characterized in that, Step S2 specifically includes: S21: In Metsim, select the feeding belt, high-pressure roller mill, transition belt, vibrating screen, conveyor belt, and return belt, and configure the parameters of each device respectively; S22: The discharge port of the feeding belt is connected to the inlet of the high-pressure roller mill, the discharge port of the high-pressure roller mill is connected to the feeding position of the transition belt, the discharge position of the transition belt is connected to the inlet of the vibrating screen, the qualified discharge position of the vibrating screen is connected to the inlet of the conveyor belt, the unqualified discharge position of the vibrating screen is connected to the inlet of the return belt, and the discharge position of the return belt is connected to the inlet of the feeding belt, which enters the feeding belt at the same time as the new material to form a crushing circuit; S23: Write the roll gap and throughput of the high-pressure roller mill from Matlab, and transmit the mass of unqualified particles on the return belt, the mass of qualified particles on the transport belt, and the energy consumption per ton of the high-pressure roller mill to the industrial computer C for data monitoring via switch D.

3. The semi-physical experimental method for intelligent operation control of high-pressure roller mill according to claim 1, characterized in that, Step S3 specifically includes: S31: Simulation of Roll Gap Control in High-Pressure Roller Mill in MATLAB: S311: Electrical signal connection from the B controller of the intelligent machine to the S signal port of the roller gap control solenoid valve; S312: The hydraulic oil module of the roll gap control valve is connected to the T interface of the roll gap control solenoid valve and the T interface of the roll gap control ideal hydraulic source to provide hydraulic oil; the P interface of the roll gap control ideal hydraulic source is connected to the P interface of the roll gap control solenoid valve to provide pressure; S313: The A and B ports of the roll gap control solenoid valve are connected to the A and B ports of the roll gap control hydraulic actuator, respectively. The C port of the roll gap control hydraulic actuator is grounded, and the R port is connected to the moving roll mass block. The moving roll mass block is connected to the roll gap control displacement sensor. S32: Simulation of displacement control of a material-saving valve in MATLAB: S321: The electrical signal of the solenoid valve of the moving roller side material-saving valve from the B controller of the intelligent machine is connected to the S signal port of the solenoid valve of the moving roller side material-saving valve; the hydraulic oil module of the moving roller side material-saving valve is connected to the T interface of the solenoid valve of the moving roller side material-saving valve and the T interface of the ideal hydraulic source of the moving roller side material-saving valve to provide hydraulic oil; the P interface of the ideal hydraulic source of the moving roller side material-saving valve is connected to the P interface of the solenoid valve of the moving roller side material-saving valve to provide pressure; the A and B interfaces of the solenoid valve of the moving roller side material-saving valve are respectively connected to the A and B interfaces of the hydraulic actuator of the moving roller side material-saving valve; the C port of the hydraulic actuator of the moving roller side material-saving valve is grounded, and the R interface is connected to the mass block of the moving roller side material-saving valve; the mass block of the moving roller side material-saving valve is connected to the displacement sensor of the moving roller side material-saving valve. S322: The electrical signal of the solenoid valve of the fixed roller side material-saving valve from the B controller of the intelligent machine is connected to the S signal port of the solenoid valve of the fixed roller side material-saving valve; the hydraulic oil module of the fixed roller side material-saving valve is connected to the T interface of the solenoid valve of the fixed roller side material-saving valve and the T interface of the ideal hydraulic source of the fixed roller side material-saving valve to provide hydraulic oil; the P interface of the ideal hydraulic source of the fixed roller side material-saving valve is connected to the P interface of the solenoid valve of the fixed roller side material-saving valve to provide pressure; the A and B interfaces of the solenoid valve of the fixed roller side material-saving valve are respectively connected to the A and B interfaces of the hydraulic actuator of the fixed roller side material-saving valve; the C port of the hydraulic actuator of the fixed roller side material-saving valve is grounded, and the R interface is connected to the mass block of the fixed roller side material-saving valve; the mass block of the fixed roller side material-saving valve is connected to the displacement sensor of the fixed roller side material-saving valve. S323: The displacement of the material-saving valve on the moving roller side and the displacement of the material-saving valve on the fixed roller side are obtained by fitting a function to obtain the throughput value of the high-pressure roller mill.

4. The semi-physical experimental method for intelligent operation control of high-pressure roller mill according to claim 1, characterized in that, The specific implementation steps of step S426 are as follows: S4261: Initialize the population: Randomly generate a certain number of individuals to form the initial population and initialize the algorithm parameters; S4262: Assess fitness: For each individual, calculate its value on each objective function; S4263: Fast Non-Dominated Sort: Determine the dominance relationship between individuals based on their fitness function values. An individual is considered non-dominated when no individual is superior to another individual. Traverse all individuals in the population to obtain non-dominated frontiers at different levels. S4264: Calculate crowding distance: For each non-dominated level, calculate the crowding distance between individuals, i.e. Individual and neighbor and The crowded distance between them; S4265: Selecting a new population: Select a certain number of individuals as the parent population based on non-dominant ranking and crowding distance; S4266: Generate offspring population: Generate a certain number of offspring individuals through crossover and mutation operations; S4267: Population Update: Combine the parent population and the offspring population to form a new population; S4268: Generate the optimal solution: Perform fast non-dominated sorting and crowding calculation, select N individuals as the parent population, and record the parent population as the optimal solution set when the termination condition is met; otherwise, jump to step S4262.

5. The semi-physical experimental method for intelligent operation control of high-pressure roller mill according to claim 3, characterized in that, Step S323 specifically includes: S3231: Because the material-saving valve device has a symmetrical structure, the moving roller-side material-saving valve and the stationary roller-side material-saving valve have equal moving distances, therefore: ； in, For the displacement of the material-saving valve, This refers to the displacement of the material-saving valve on the moving roller side. This refers to the displacement of the material-saving valve on the fixed roller side; S3232: Fitting function: Linear regression model of the material-saving valve displacement and the throughput of the high-pressure roller mill, formula is as follows. ； S3233: The high-pressure roller mill equipment transmits data to Metsim.

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