Roller press control system and method based on online simulation and deep learning

Abstract

The invention discloses a roller press control system and method based on online simulation and deep learning, and belongs to the technical field of roller press energy conservation. Intelligent control management is performed on a roller press system through combination of an online monitoring technology, a CAE dynamic simulation technology and a deep learning technology; the stable operation of the roller press is guaranteed, the production efficiency and the service life of the roller press are improved, the grinding energy consumption is reduced, and the management level is improved. In addition, the invention provides a set of complete roller press capacity and service life optimization management control system and method, which integrates the advantages of real-time and high efficiency of online monitoring, the advantage of microcosmic particle dynamics accurately captured by a CAE dynamic simulation technology, and the advantage of rapid response of a deep learning technology. And the actual complex and changeable working conditions of the roller press are quickly calculated, and instant response control is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of roller press energy saving, and specifically, relates to a roller press control system and method based on online simulation and deep learning. Background Art

[0002] Since the emergence of roller press technology in the 1980s, as an energy-saving and reliable crushing device, it has been increasingly used in the cement and mining industries. Compared with other crushing technologies, roller press technology has the advantages of high flexibility, good wear resistance, high output and low energy consumption. The principle of roller press crushing is that the loose cement particles move downward under the pressure of the upper particles, and are squeezed at the roller inlet to form a compact material layer. After the high pressure between the roller presses, the cement particles generate a large strong interaction force and are crushed or cracked for subsequent crushing.

[0003] However, in the actual production process, due to factors such as long-term operation of the equipment, complex and changeable working conditions, and improper operation and maintenance, the production capacity of the roller press is easily reduced and the service life is shortened, which brings great economic losses to the enterprise. At present, the management of the roller press mainly relies on regular shutdown and maintenance and manual experience judgment. This method is not only inefficient, but also difficult to accurately and timely discover potential problems of the equipment. Therefore, how to achieve the optimal management of the production capacity and life of the roller press and improve the operation efficiency and reliability of the equipment has become a technical problem to be solved in this field. In the prior art, various sensors are installed at key parts of the equipment to collect the operation status data of the equipment in real time, and big data machine learning analysis is performed to perform empirical analysis and intelligent control. Online monitoring technology has the advantages of strong real-time performance and large data volume, and can timely detect abnormal status and fault warning information of the equipment. However, purely relying on big data analysis cannot consider the essential dynamic mechanism of equipment operation, and it is difficult to fully reflect the operation status and performance of the equipment, resulting in very limited optimization and control effects. Numerical simulation technology can predict the service life and production capacity of the equipment, optimize the operation parameters, and improve the production efficiency and operation stability of the equipment by establishing a dynamic simulation model of the roller press and performing simulation analysis. Numerical simulation technology can provide accurate performance prediction and optimization solutions to make up for the shortcomings of online monitoring technology. However, numerical simulation has the problem of untimely calculation. Especially during the operation of the equipment, the calculation complexity of real-time simulation is high, and it is difficult to quickly provide optimization suggestions and decision support. It is currently mainly used in the scientific research and development stage or the problem diagnosis stage, and it is impossible to achieve real-time calculation to guide production. In order to solve the above problems, the present invention provides the following technical solutions. Summary of the invention

[0004] The purpose of the present invention is to provide a roller press control system and method based on online simulation and deep learning, so as to solve the problem that the working condition adjustment of the roller press intelligent control system in the prior art ignores real-time changes, and the historical data can only reflect the past operating conditions, and it is difficult to adapt to the real-time changes of the current working conditions. Changing factors in the production process (such as raw material quality, environmental conditions, equipment status, etc.) may cause historical data to fail and production parameters cannot be adjusted in time; lack of predictive ability, historical data analysis cannot effectively predict future production trends and potential problems, and it is difficult to make forward-looking decisions and preventive maintenance; relying solely on sensor monitoring data is costly, and it is impossible to obtain particle dynamics data inside the roller press.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A roller press control system based on online simulation and deep learning, comprising a sensor module, a deep learning module, a numerical simulation module, a control module, an actuator and a human-machine interface;

[0007] The sensor module collects the operating status data of the equipment in real time and transmits the data to the control module, deep learning module and numerical simulation module;

[0008] The deep learning module processes and analyzes real-time data and numerical simulation results, identifies operating modes and failure modes, and provides optimization suggestions and early warning information to the control module;

[0009] The numerical simulation module performs simulation analysis based on real-time data, captures the microscopic particle dynamics data inside the roller press, predicts the service life and production capacity of the equipment, and passes the simulation results to the deep learning module and the control module;

[0010] The control module makes decisions and optimizes control based on the deep learning results and simulation results, and generates control instructions to send to the actuators;

[0011] The actuator adjusts the operating parameters of the roller press according to the control instructions;

[0012] The human-machine interface provides an operation interface.

[0013] A roller press control method based on online simulation and deep learning comprises the following steps:

[0014] S1. Establishing numerical simulation model

[0015] Based on the particle dynamics simulation software, DEM is used to model and simulate the particle dynamics behavior and particle-geometry interaction behavior, and the boundary conditions are set according to the actual working parameters of the roller press, and the calculation is submitted;

[0016] The results of the DEM particle dynamics simulation of the roller press were analyzed to explore the influence of the roller press on the change of the particle size of the particles entering and exiting the roller, the power of the roller press, and the wear amount;

[0017] S2. Verification and adjustment of simulation results

[0018] Adjust the simulation model by comparing the simulation results with the actual production data;

[0019] S3. Output simulation data

[0020] After the simulation model is verified, the simulation results are output to the control module and the deep learning module;

[0021] S4. Establish a deep learning analysis and prediction model

[0022] The deep learning module analyzes the plant production data and simulation results, and establishes a deep learning analysis and prediction model to predict the key performance indicators of the roller press;

[0023] S5. Verification and adjustment of deep learning analysis and prediction models

[0024] Based on the actual production data and simulation results of the plant, the deep analysis and prediction model was verified, and the model parameters were adjusted using the Bayesian optimization method;

[0025] S6. Output analysis data

[0026] After the deep learning analysis and prediction model is verified, the analysis results are output to the control module, which uses this data to provide optimization decision support.

[0027] Beneficial effects of the present invention:

[0028] 1. The present invention combines online monitoring technology, CAE dynamic simulation technology and deep learning technology to perform intelligent control and management of the roller press system, thereby ensuring the stable operation of the roller press, improving the production efficiency and service life of the roller press, reducing grinding energy consumption and improving management level.

[0029] 2. The present invention provides a complete set of roller press capacity and life optimization management control system and method, which integrates the advantages of real-time and efficient online monitoring, the advantages of CAE dynamic simulation technology in accurately capturing microscopic particle dynamics, and the advantages of deep learning technology in rapid response, and can quickly calculate and instantly respond to the actual complex and changeable roller press working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] Figure 1 It is a schematic diagram of the framework structure of a roller press control system based on online simulation and deep learning;

[0032] Figure 2 It is a flow chart of a roller press control method based on online simulation and deep learning;

[0033] Figure 3 It is a schematic diagram of the process of optimizing the roller press system. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] A roller press control system based on online simulation and deep learning, comprising a sensor module, a deep learning module, a numerical simulation module, a control module, an actuator and a human-machine interface;

[0036] like Figure 1 As shown, when working, the sensor module collects the operating status data of the equipment in real time and transmits the data to the control module, the deep learning module and the numerical simulation module;

[0037] The deep learning module processes and analyzes real-time data and numerical simulation results, identifies operating modes and fault modes, and provides optimization suggestions and early warning information to the control module.

[0038] The numerical simulation module performs simulation analysis based on real-time data, captures the microscopic particle dynamics data inside the roller press, predicts the service life and production capacity of the equipment, and passes the simulation results to the deep learning module and the control module.

[0039] The control module makes decisions and optimizes control based on the deep learning results and simulation results, and generates control instructions to send to the actuator.

[0040] The actuator adjusts the operating parameters of the roller press according to the control instructions to ensure that the equipment operates under optimal conditions.

[0041] The human-machine interface provides an interactive interface between the operator and the control module, displays real-time data, analysis results and simulation results, and receives operation instructions.

[0042] Through the coordinated work of these modules, the system can realize intelligent control and management of the roller press, ensure the stable operation of the roller press, and improve production efficiency and the service life of the equipment.

[0043] The present invention provides a roller press control method based on online simulation and deep learning, such as Figure 2 and Figure 3 As shown, the following steps are included:

[0044] S1. Establish a numerical simulation model. Based on particle dynamics simulation software such as Rocky and EDEM, the discrete element method (DEM) is used to model and simulate particle dynamics behavior and particle-geometry interaction behavior. According to the actual working parameters of the roller press, boundary conditions, material parameters, particle parameters, particle-particle and particle-geometry interaction parameters, etc. are set and submitted for calculation. The model can simultaneously achieve: (1) particle crushing simulation; (2) calculation simulation of equipment operating power; (3) simulation and prediction of equipment wear; (4) simulation of particle-particle and particle-wall interaction.

[0045] The results of the DEM particle dynamics simulation of the roller press were analyzed, and the particle size distribution data of the particles entering and exiting the rollers and the operating power data of each equipment (moving roller, fixed roller and booster device) were extracted. The contact force cloud diagrams of particles-particles and particles-walls in the feeding tube, the streamline diagram of particle flow, and the volume loss and stress cloud diagrams of equipment wall wear were drawn.

[0046] In the technology of particle dynamics simulation, by jointly adjusting the feed position, shape, material distribution and the coordination of the roller press hydraulic system, the influence of the roller press on the change of the particle size of the particles entering and exiting the roller, the power and wear of the roller press are explored, and the roller press system is optimized.

[0047] S2. Verify and adjust the simulation results. Verify the simulation results based on the actual production data of the plant. By comparing the simulation results with the actual production data, adjust the simulation model to improve the accuracy and reliability of the model.

[0048] During the model validation process, the simulation model parameters were tuned using Bayesian optimization methods:

[0049]

[0050] Where θ is the model parameter; D is the observed data; p(θ|D) is the posterior probability distribution of the parameter, calculated by Bayes’ theorem:

[0051]

[0052] S3. Output simulation data. After the simulation model is verified, the simulation results are output to the control module and the deep learning module. The control module uses the simulation data to provide decision support, and the deep learning module uses the simulation data for further analysis.

[0053] S4. Establish a deep learning analysis and prediction model. The deep learning module analyzes the factory production data and simulation results to establish a deep learning analysis and prediction model. This model is used to predict key performance indicators such as the operating status, production capacity and life of the roller press.

[0054] S5. Verification and adjustment of deep learning analysis and prediction model. Verify the analysis and prediction model based on the actual production data and simulation results of the plant. By comparing the prediction results of the analysis and prediction model with the actual data, use the Bayesian optimization method to adjust the model parameters to improve the prediction accuracy of the model.

[0055] S6. Output analysis data. After the deep learning analysis prediction model is verified, the analysis results are output to the control module. The control module uses this data to provide optimization decision support, including operating condition adjustment and whether to perform equipment maintenance to ensure that the roller press operates under optimal conditions and improve production efficiency and equipment life.

[0056] The control module is the core part of the whole system, which is responsible for receiving the data of the sensor module, the simulation results of the numerical simulation module and the instructions of the deep learning module, and making decisions according to the preset control strategy. The control module interacts with the operator through the human-machine interface, receives the operation instructions and displays the system operation status. The actuator actually adjusts the operating parameters of the roller press, such as the roller gap distance, the given pressure and the feed amount, according to the instructions of the control module, to ensure that the roller press operates under the optimal working conditions. The actuator includes components such as hydraulic system, motor and transmission device, which can respond to control instructions quickly to ensure the efficient operation of the system.

[0057] In order to achieve accurate control and optimization of the roller press, the model predictive control strategy (MPC) is used to establish the discrete time state space model of the roller press system:

[0058] x k+1 =Ax k +Bu k +w k

[0059] y k =Cx k +v k

[0060] Among them, x k ∈R n : system state vector, including the output, roll gap, and temperature of the roller press; u k ∈R m : Control input vector, including roller press feed rate and given pressure; y k ∈R p: Output measurement vector, including sensor measurement data; A, B, C: System matrix, describing the system dynamics characteristics; W k 、v k : Process noise and measurement noise, assumed to be zero-mean Gaussian noise;

[0061] In the prediction domain N p By optimizing the control input sequence U(k)={u(k),u(k+1),...,u(k+N p -1)} minimize the following objective function:

[0062]

[0063] Among them, N p : Prediction time domain length, y ref,k+i : Reference output at the k+ith moment; Δu k+i-1 =u k+i-1 -u k+i-2 : Control the increment of input; Weighted quadratic norm; Q, R: positive definite weighted matrices, weighing the cost of tracking error and control increment respectively; then a quadratic programming (QP) problem is formed based on the objective function:

[0064]

[0065] stEΔU≤F

[0066] in, E, F: matrices and vectors constructed by system constraints. Use optimization algorithms such as interior point method or active set method to solve the QP problem and obtain the optimal control sequence. In practical applications, only the first control input is executed, followed by rolling optimization. Since the model parameters of the roller press may change over time, an adaptive control strategy is used to improve the robustness of the system. Use recursive least squares (RLS) to estimate the system model parameters online:

[0067] θ k =θ k+1 +L k (y k -φ T k θ k-1 )

[0068]

[0069] P k =P k-1 -L k φT k P k-1

[0070] Among them, θ k : estimated model parameter vector; φ k : regression vector, consisting of input and output data; P k : Covariance matrix. By updating the model parameters online, the controller can adapt to the dynamic changes of the system and maintain good control performance.

[0071] The sensor module is responsible for real-time monitoring of the operating status and environmental parameters of the roller press. Sensors include temperature sensors, pressure sensors, vibration sensors, movable roller displacement sensors, speed sensors, weighing sensors, etc., which are installed in key parts of the roller press, such as the roller shaft, hydraulic cylinder and transmission device, and weighing bin. The sensor module transmits the collected real-time data to the control module, numerical simulation module, and deep learning module through the data bus. The real-time monitoring function of the sensor module ensures that the system can detect abnormal status and fault warning information of the equipment in a timely manner, thereby improving the reliability and safety of the equipment. In order to improve the accuracy and robustness of sensor data, a fusion Kalman filter is used to fuse multi-source data. For data from multiple sensors:

[0072] z k =Hx k +v k

[0073] Among them, Z k : fused measurement data; H: observation matrix; measurement noise; v k : Measurement noise, assumed to be Gaussian white noise.

[0074] The human-machine interface provides a friendly and intuitive operation interface. Through the human-machine interface, operators can view real-time monitoring data, system operation status, fault alarm information, numerical simulation results, numerical simulation predictions, deep learning analysis results, etc., and input parameter settings and control instructions. The human-machine interface also has a data visualization function, which displays complex data results in the form of charts, graphs, etc., which is convenient for operators to understand and make decisions.

[0075] The deep learning module uses the CNN-LSTM neural network to process and analyze the real-time data and numerical simulation results collected by the sensor module. Through data mining and machine learning algorithms, the deep learning module can analyze historical data, discover the laws and trends of equipment operation, identify the operating mode and fault characteristics of the roller press, and provide optimization suggestions; at the same time, it can intelligently analyze the evolution of working conditions based on long-term data, process new data in real time, predict changes in working conditions, capture possible fault conditions, and issue early warnings to assist on-site personnel in making decisions and optimizing control.

[0076] The numerical simulation module is based on discrete element simulation technology to establish a dynamic simulation model of the roller press and perform simulation analysis. The simulation model takes into account factors such as the structural characteristics, material characteristics, and multi-body operating conditions of the roller press, and can accurately capture the dynamic behavior of microscopic particles. The numerical simulation module can predict the production capacity and use of the equipment, optimize operating parameters, and provide accurate performance predictions and optimization solutions. The simulation results are transmitted to the deep learning module and the control module in real time to provide decision support.

[0077] In discrete element simulation, the motion of each particle is described by Newton's equation of motion:

[0078]

[0079]

[0080] Where mi is the mass of particle i; is the translational velocity of the particle; and are the normal contact force and tangential contact force between particles i and j, respectively; is the force exerted by the gas on particle i; Ii is the moment of inertia of the particle; is the angular velocity of the particle; is the vector pointing from the center of the particle to the contact point; μr is the rolling friction coefficient affected by particle i. The interaction between particles is calculated using the nonlinear Hertz model:

[0081]

[0082]

[0083]

[0084]

[0085] Among them, E' is the equivalent Young's modulus, R' is the equivalent radius, δn is the normal overlap, m' is the equivalent mass, is the relative normal velocity, e is the coefficient of restitution, G′ is the equivalent shear modulus, δt is the tangential overlap, is the relative tangential velocity and μs is the sliding friction coefficient.

[0086] The wear volume is calculated using the Archard model:

[0087]

[0088] Where ΔV is the wear volume of the geometric body within a simulation time step; ΔW τ is the work done by the contact force of the particle on the surface of the geometric body in one time step; C is the wear empirical constant, the smaller the value, the more wear-resistant the geometric material; F τ is the tangential component of the contact force between the particle and the surface of the geometric body; Δs τ is the relative displacement of the particle on the tangential plane within one time step.

[0089] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A roller press control system based on online simulation and deep learning, characterized in that: It includes sensor module, deep learning module, numerical simulation module, control module, actuator and human-machine interface; The sensor module collects the operating status data of the equipment in real time and transmits the data to the control module, deep learning module and numerical simulation module; The deep learning module processes and analyzes real-time data and numerical simulation results, identifies operating modes and failure modes, and provides optimization suggestions and early warning information to the control module; The numerical simulation module performs simulation analysis based on real-time data, captures the microscopic particle dynamics data inside the roller press, predicts the service life and production capacity of the equipment, and passes the simulation results to the deep learning module and the control module; The control module makes decisions and optimizes control based on the deep learning results and simulation results, and generates control instructions to send to the actuators; The actuator adjusts the operating parameters of the roller press according to the control instructions; The human-machine interface provides an operation interface.

2. A roller press control method based on online simulation and deep learning, characterized in that: The steps include: S1. Establishing numerical simulation model Based on the particle dynamics simulation software, DEM is used to model and simulate the particle dynamics behavior and particle-geometry interaction behavior, and the boundary conditions are set according to the actual working parameters of the roller press, and the calculation is submitted; The results of the DEM particle dynamics simulation of the roller press were analyzed to explore the influence of the roller press on the change of the particle size of the particles entering and exiting the roller, and the power and wear of the roller press. S2. Verification and adjustment of simulation results Adjust the simulation model by comparing the simulation results with the actual production data; S3. Output simulation data After the simulation model is verified, the simulation results are output to the control module and the deep learning module; S4. Establish a deep learning analysis and prediction model The deep learning module analyzes the plant production data and simulation results, and establishes a deep learning analysis and prediction model to predict the key performance indicators of the roller press; S5. Verification and adjustment of deep learning analysis and prediction models Based on the actual production data and simulation results of the plant, the deep analysis and prediction model was verified, and the model parameters were adjusted using the Bayesian optimization method; S6. Output analysis data After the deep learning analysis and prediction model is verified, the analysis results are output to the control module, which uses this data to provide optimization decision support.

3. A roller press control method based on online simulation and deep learning according to claim 2, characterized in that: During the model validation process in step S2, the simulation model parameters are adjusted using the Bayesian optimization method: Where θ is the model parameter; D is the observed data; p(θ|D) is the posterior probability distribution of the parameter, calculated by Bayes’ theorem:

4. The roller press control method based on online simulation and deep learning according to claim 2 is characterized in that: The discrete time state space model of the roller press system established using MPC is: x k+1 =Ax k +Bu k +w k y k =Cx k +v k Among them, x k ∈R n : system state vector, including the output, roll gap, and temperature of the roller press; u k ∈R m : Control input vector, including roller press feed rate and given pressure; y k ∈R p : Output measurement vector, including sensor measurement data; A, B, C: System matrix, describing the system dynamics characteristics; w k 、v k : Process noise and measurement noise, assumed to be zero-mean Gaussian noise; In the prediction domain N p By optimizing the control input sequence U(k)={u(k),u(k+1),…,u(k+N p -1)} minimize the following objective function: Among them, N p : Prediction time domain length, y ref,k+i : Reference output at the k+ith moment; Δu k+i-1 =u k+i-1 -u k+i-2 : Control the input increment; Weighted quadratic norm; Q, R: positive definite weighted matrices, weighing the cost of tracking error and control increment respectively; then a quadratic programming problem is formed based on the objective function: stEΔU≤F in, E, F: matrices and vectors constructed by system constraints. The optimization algorithm is used to solve the quadratic programming problem and obtain the optimal control sequence.

5. The roller press control method based on online simulation and deep learning according to claim 4 is characterized in that: After obtaining the optimal control sequence, only the first control input is executed, followed by rolling optimization.

6. A roller press control method based on online simulation and deep learning according to claim 5, characterized in that: The model parameters are also updated online by: Estimate system model parameters online using recursive least squares: i k =θ k+1 +L k (y k -f T k i k-1 ) P k =P k-1 -L k φ T k P k-1 Among them, θ k : estimated model parameter vector; φ k : regression vector, consisting of input and output data; P k : covariance matrix.

7. The roller press control method based on online simulation and deep learning according to claim 5 is characterized in that: The sensor module collects the running status data of the device in real time and uses a fusion Kalman filter to fuse the data of multiple sensors: z k =Hx k +v k Among them, z k : fused measurement data; H: observation matrix; measurement noise; v k : Measurement noise, assumed to be Gaussian white noise.

8. The roller press control method based on online simulation and deep learning according to claim 7 is characterized in that: The numerical simulation module establishes a dynamic simulation model of the roller press and performs simulation analysis. In the discrete element simulation, the motion of each particle is described by Newton's equation of motion: Where mi is the mass of particle i; is the translational velocity of the particle; and are the normal contact force and tangential contact force between particles i and j, respectively; is the force exerted by the gas on particle i; Ii is the moment of inertia of the particle; is the angular velocity of the particle; is the vector pointing from the center of the particle to the contact point; μr is the rolling friction coefficient affected by particle i.

9. The roller press control method based on online simulation and deep learning according to claim 8, characterized in that: The interaction between particles is calculated using the nonlinear Hertz model: Among them, E' is the equivalent Young's modulus, R' is the equivalent radius, δn is the normal overlap, m' is the equivalent mass, is the relative normal velocity, e is the coefficient of restitution, G′ is the equivalent shear modulus, δt is the tangential overlap, is the relative tangential velocity and μs is the sliding friction coefficient.

10. A roller press control method based on online simulation and deep learning according to claim 9, characterized in that: The wear volume is calculated using the Archard model: Where ΔV is the wear volume of the geometric body within a simulation time step; ΔW τ is the work done by the contact force of the particle on the surface of the geometric body in one time step; C is the wear empirical constant, the smaller the value, the more wear-resistant the geometric material; F τ is the tangential component of the contact force between the particle and the surface of the geometric body; Δs τ is the relative displacement of the particle on the tangential plane within one time step.

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