Intelligent production scheduling algorithm for cement grinding mill

Abstract

The invention relates to the technical field of control engineering, in particular to an intelligent production scheduling algorithm for a cement grinding mill. The method comprises the following steps: S1, setting target values of the weight of a constant-weight bin and the current of a mill elevator; s2, actual measurement values of the weight of the constant-weight bin and the current of the out-of-mill elevator are obtained in real time through a sensor and monitoring equipment; s3, calculating the deviation between the measured value and the target value through an absolute deviation algorithm, and formulating a feedback control strategy; the accurate deviation data provides powerful decision support for the management layer, and helps to formulate more effective production plans and strategies; s4, the APC optimization technology is used for achieving automatic switching and automatic control of all working conditions; and S5, continuously monitoring the measured values of the weight of the constant-weight bin and the current of the mill elevator, and regularly evaluating and feeding back the effect of the control strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of control engineering, specifically, a cement mill intelligent production scheduling algorithm. BACKGROUND

[0002] The cement mill intelligent production scheduling algorithm refers to a method of optimizing the management of the cement mill production process using modern information technology and control technology. This method realizes the intelligentization of the production process through real-time monitoring, data analysis and automatic control, improves production efficiency, reduces energy consumption, guarantees product quality and reduces human operation errors.

[0003] In the existing cement mill intelligent production scheduling algorithm, under different production conditions, complex working conditions are exhibited, and the existing algorithm may be difficult to handle all complex situations, especially in the case of system parameter mutation or abnormal situations. The nonlinearity and time-varying characteristics of the cement mill system may make it difficult for the model to accurately reflect the actual system behavior, thereby affecting the control performance. There is strong interaction between the various components of the cement mill system, and the system has a large delay, which makes it difficult for the control algorithm to quickly and accurately respond to changes, which may result in poor control effect. Therefore, a cement mill intelligent production scheduling algorithm is provided. SUMMARY

[0004] The purpose of the present application is to provide a cement mill intelligent production scheduling algorithm to solve the problem of complex working conditions exhibited under different production conditions in the existing cement mill intelligent production scheduling algorithm, and the existing algorithm may be difficult to handle all complex situations, especially in the case of system parameter mutation or abnormal situations. The nonlinearity and time-varying characteristics of the cement mill system may make it difficult for the model to accurately reflect the actual system behavior, thereby affecting the control performance. There is strong interaction between the various components of the cement mill system, and the system has a large delay, which makes it difficult for the control algorithm to quickly and accurately respond to changes, which may result in poor control effect.

[0005] To achieve the above-mentioned purpose, the present application provides a cement mill intelligent production scheduling algorithm, comprising the following steps:

[0006] S1, setting the target value of the constant weight bin weight and the mill discharge elevator current;

[0007] S2, acquiring the measured value of the constant weight bin weight and the mill discharge elevator current in real time through sensors and monitoring devices;

[0008] S3, calculating the deviation of the measured value and the target value by absolute deviation algorithm, and formulating feedback control strategy;

[0009] S4, using APC optimization technology for automatic switching automatic control in all working conditions;

[0010] S5, continuously monitor the measured values of the constant weight bin weight and the mill discharge elevator current, and periodically evaluate the effect of the feedback control strategy.

[0011] As a further improvement of the technical solution, in S1, the target value of the constant weight bin weight refers to the weight of the material that should be maintained in the constant weight bin, and the target value of the mill discharge elevator current refers to the current level that the mill discharge elevator should reach in normal operation.

[0012] As a further improvement of the technical solution, in S2, the measured values of the constant weight bin weight and the mill discharge elevator current are preprocessed, which specifically includes filtering, amplification and conversion.

[0013] Wherein, the constant weight bin weight is monitored by a weighing sensor, and the mill discharge elevator current is monitored by a current sensor.

[0014] As a further improvement of the technical solution, in S3, the specific steps of formulating the feedback control strategy are:

[0015] S2.1, calculate the deviation between the measured value and the target value of the constant weight bin weight and the mill discharge elevator current, respectively;

[0016] S2.2, set the control logic according to the size and direction of the deviation;

[0017] S2.3, execute the control action according to the set control logic;

[0018] Wherein, the corresponding control action includes adjusting the valve opening, changing the motor speed, adjusting the feeding rate.

[0019] As a further improvement of the technical solution, in S2.1, the absolute deviation algorithm is specifically:

[0020] Constant weight bin weight deviation:

[0021] ΔW = W 实测 - W 目标 ;

[0022] Wherein, ΔW represents the constant weight bin weight deviation; W 实测 represents the measured bin weight of the constant weight bin; W 目标 represents the preset target bin weight.

[0023] Mill discharge elevator current deviation:

[0024] ΔI = I 实测 - I 目标 ;

[0025] Wherein, ΔI represents the mill discharge elevator current deviation; I d represents the measured current of the mill discharge elevator; I tThe target current preset for the mill elevator is indicated.

[0026] As a further improvement of the technical solution, in S2.2, the control logic is specifically: if the bin weight deviation is positive, that is, the measured bin weight is higher than the target bin weight, it may be necessary to reduce the raw material supply; if the current deviation is positive, that is, the measured current is higher than the target current, it may be necessary to adjust the running speed or load of the elevator.

[0027] As a further improvement of the technical solution, in S4, the specific steps involved in the APC optimization technique are:

[0028] S3.1, a dynamic prediction model reflecting the production process is established;

[0029] S3.2, by estimating the parameters of the dynamic prediction model, it is used to ensure that the dynamic prediction model can accurately reflect the dynamic characteristics of the actual production process;

[0030] Among them, the dynamic prediction model parameters include temperature, pressure;

[0031] S3.3, the dynamic prediction model is used to predict the control sequence, and an optimal control sequence is calculated;

[0032] S3.4, the controller adjusts various operating variables in the production process according to the control sequence obtained by optimization to execute the feedback control strategy;

[0033] S3.5, during the control execution process, the controller continuously receives new measurement data, and adjusts the feedback control strategy according to the real-time data and the update of the dynamic prediction model, to cope with changes and disturbances in the production process.

[0034] As a further improvement of the technical solution, in S3.1, the dynamic prediction model is specifically:

[0035] T(t+1)=a1T(t)+b1u1(t)+c1w1(t);

[0036] P(t+1)=a2P(t)+b2u2(t)+C2w2(t);

[0037] Where T(t) represents the temperature at time t; P(t) represents the pressure at time t; u1(t) represents the operating variable for controlling temperature; u2(t) represents the operating variable for controlling pressure; w1(t) represents the external disturbance environmental temperature fluctuation; w2(t) represents the external disturbance raw material composition change; a1 represents the autoregressive coefficient of temperature; b1 represents the influence coefficient of control variable u1 on temperature; c1 represents the influence coefficient of external disturbance w1 on temperature; a2 represents the autoregressive coefficient of pressure; b2 represents the influence coefficient of control variable u2 on pressure; c2 represents the influence coefficient of external disturbance W2 on pressure.

[0038] As a further improvement of the technical solution, the S3.2, the autoregressive integrated moving average model is specifically:

[0039] Delta d X t =c+phi1Delta d-1 X t-1 +...+phi p Delta d-p X t-p +theta1Epsilon t-1 +...+theta q Epsilon t-q +Epsilon t ;

[0040] Where, Delta d represents the difference operator; d represents the difference order; X t represents the value of time series at time t; c represents the constant term; phi i represents the autoregressive term coefficient; theta j is the moving average term coefficient; epsilon t represents the error term.

[0041] As a further improvement of the technical solution, in the S5, the system running state is monitored continuously, which is used to ensure that each index is within the target range; the effect of feedback control strategy is evaluated regularly, and adjustment is made according to the actual situation, which is used to maintain the efficient and stable operation of the system.

[0042] Compared with the prior art, the beneficial effects of the present application are:

[0043] 1. In the cement mill intelligent production scheduling algorithm, the absolute deviation algorithm is used to calculate the deviation between the measured value and the target value, and a feedback control strategy is formulated. The accurate deviation data provides strong decision support for the management layer, helping to develop more effective production plans and strategies.

[0044] 2. In the cement mill intelligent production scheduling algorithm, the APC optimization technology can cover all working conditions of the production process, whether it is normal production or abnormal working condition, and can maintain efficient and stable control. Through optimization of the control strategy, the efficiency of the production process can be improved, the yield can be increased, and the product quality can be improved. By accurately controlling each variable in the production process, unnecessary energy consumption can be reduced, and production costs can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The overall method flowchart of the present application. DETAILED DESCRIPTION

[0046] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0047] Embodiments

[0048] Please refer to Figure 1 The present embodiment provides a cement mill intelligent production scheduling algorithm, which comprises the following steps:

[0049] S1, set the target value of the constant weight bin bin weight and the mill discharge elevator current;

[0050] In this example, the target value of the constant weight bin weight refers to the weight of the material that should be maintained in the constant weight bin, and the target value of the mill discharge elevator current refers to the current level that the mill discharge elevator should reach in normal operation. S2, real-time acquisition of the measured value of the constant weight bin weight and the mill discharge elevator current through sensors and monitoring devices;

[0051] In this example, the measured value of the constant weight bin weight and the mill discharge elevator current is preprocessed, which specifically includes filtering, amplification and conversion.

[0052] Among them, the constant weight bin weight is monitored by a weighing sensor, and the mill discharge elevator is monitored by a current sensor.

[0053] S3, calculate the deviation of the measured value and the target value by an absolute deviation algorithm, and develop a feedback control strategy;

[0054] In this example, the specific steps of developing a feedback control strategy are as follows:

[0055] S2.1, calculate the deviation between the measured value and the target value of the constant weight bin weight and the mill discharge elevator current respectively;

[0056] In this example, the absolute deviation algorithm is as follows:

[0057] Constant weight bin weight deviation:

[0058] ΔW = W 实测 -W 目标 ;

[0059] Among them, ΔW represents the constant weight bin weight deviation; W 实测 represents the measured bin weight of the constant weight bin; W 目标 represents the preset target bin weight;

[0060] Mill discharge elevator current deviation:

[0061] ΔI = I 实测 -I 目标 ;

[0062] wherein, ΔI represents the current deviation of the mill elevator; I 实测 represents the measured current of the mill elevator; I 目标 represents the preset target current of the mill elevator.

[0063] S2.2, according to the size and direction of the deviation, set the control logic;

[0064] In this example, the control logic is specifically: if the bin weight deviation is positive, that is, the measured bin weight is higher than the target bin weight, it may be necessary to reduce the raw material supply; if the current deviation is positive, that is, the measured current is higher than the target current, it may be necessary to adjust the running speed or load of the elevator.

[0065] S2.3, according to the set control logic, execute the corresponding control action;

[0066] wherein, the corresponding control action includes adjusting the valve opening, changing the motor speed, adjusting the feeding rate.

[0067] S4, using APC optimization technology for automatic switching automatic control in all working conditions;

[0068] In this example, in the S5, the specific steps of the APC optimization technology based on the predictive model control are:

[0069] S3.1, establish a dynamic prediction model reflecting the production process;

[0070] In this example, the dynamic prediction model is specifically:

[0071] T(t+1) = a1T(t) + b1u1(t) + c1w1(t);

[0072] P(t+1) = a2P(t) + b2u2(t) + c2w2(t);

[0073] wherein, T(t) represents the temperature at time t; P(t) represents the pressure at time t; u1(t) represents the operation variable for controlling the temperature; u2(t) represents the operation variable for controlling the pressure; w1(t) represents the external disturbance environmental temperature fluctuation; w2(t) represents the external disturbance raw material composition change; a1 represents the autoregressive coefficient of the temperature; b1 represents the influence coefficient of the control variable u1 on the temperature; c1 represents the influence coefficient of the external disturbance w1 on the temperature; a2 represents the autoregressive coefficient of the pressure; b2 represents the influence coefficient of the control variable u2 on the pressure; c2 represents the influence coefficient of the external disturbance w2 on the pressure.

[0074] S3.2, by estimating the parameters of the dynamic prediction model, to ensure that the dynamic prediction model can accurately reflect the dynamic characteristics of the actual production process;

[0075] In this example, the autoregressive integrated moving average model is specifically:

[0076] Δ d X t = c + φ1Δ d-1 X t-1 +... + φ p Δ d-p X t-p + θ1∈ t-1 +... + θ q ∈ t-q + ∈ t ;

[0077] Where Δ d represents the difference operator; d represents the difference order; X t represents the value of the time series at time t; c represents the constant term; φ i represents the autoregressive term coefficient; θ j is the moving average term coefficient; ∈ t represents the error term.

[0078] Wherein the dynamic prediction model parameters include temperature, pressure;

[0079] S3.3, using the dynamic prediction model to predict the control sequence, and calculating an optimal control sequence;

[0080] S3.4, the controller adjusts various operating variables in the production process according to the control sequence obtained by optimization to execute the feedback control strategy;

[0081] Specifically, the feedback control strategy is a basic control method, which compares the difference between the actual output of the system and the expected target value (i.e. deviation), and then adjusts the control input according to the deviation to reduce or eliminate the deviation, so that the system output is as close as possible to the expected target. This strategy relies on real-time monitoring and immediate response to system status.

[0082] S3.5, during the control execution process, the controller continuously receives new measurement data, and adjusts the feedback control strategy according to the real-time data and the update of the dynamic prediction model, to cope with changes and disturbances in the production process.

[0083] Specifically, APC optimization technology, i.e. Advanced Process Control, is a technology that applies advanced mathematical models and control algorithms to optimize and adjust the production process in real time. It differs from traditional single-loop control and achieves the improvement of production efficiency, the reduction of energy consumption and the improvement of product quality through multivariable control, nonlinear control and model predictive control strategies.

[0084] S5, continuously monitor the measured values of the constant weight bin weight and the mill elevator current, and periodically evaluate the effect of the feedback control strategy.

[0085] In this example, the system operation state is continuously monitored to ensure that various indicators are within the target range, and the effect of the feedback control strategy is periodically evaluated and adjusted according to actual conditions to maintain efficient and stable operation of the system.

[0086] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A cement mill intelligent scheduling algorithm, characterized in that: The method comprises the following steps: S1, setting the target value of the constant weight bin weight and the discharge mill elevator current; S2, obtaining the measured value of the constant weight bin weight and the discharge mill elevator current in real time through sensors and monitoring devices; S3, calculating the deviation between the measured value and the target value through an absolute deviation algorithm, and formulating a feedback control strategy; S4, using APC optimization technology to realize automatic switching and automatic control in all working conditions; S5, continuously monitoring the measured value of the constant weight bin weight and the discharge mill elevator current, and regularly evaluating the effect of the feedback control strategy.

2. The intelligent scheduling algorithm for a cement mill according to claim 1, characterized in that: In S1, the target value of the constant weight bin weight refers to the weight of the material that should be maintained in the constant weight bin, and the target value of the discharge mill elevator current refers to the current level that the discharge mill elevator should reach in normal operation.

3. The intelligent scheduling algorithm for a cement mill according to claim 1, characterized in that: In S2, the measured value of the constant weight bin weight and the discharge mill elevator current is preprocessed, which specifically includes filtering, amplification and conversion.

4. The intelligent scheduling algorithm for a cement mill according to claim 1, characterized in that: In S3, the specific steps of formulating the feedback control strategy are as follows: S2.1, calculating the deviation between the measured value and the target value of the constant weight bin weight and the discharge mill elevator current respectively; S2.2, setting the control logic according to the size of the deviation; S2.3, executing the control action according to the set control logic; Wherein, the control action includes adjusting the valve opening, changing the motor speed, adjusting the feeding rate.

5. The intelligent scheduling algorithm for a cement mill according to claim 4, characterized in that: In S2.1, the absolute deviation algorithm is as follows: Constant weight bin weight deviation: ΔW = W 实测 - W 目标 ; Wherein, AW represents the constant weight bin weight deviation; W 实测 represents the measured bin weight of the constant weight bin; W 目标 represents the preset target bin weight; Discharge mill elevator current deviation: ΔI = I 实测 - I 目标 ; where ΔI represents the deviation of the mill hoist current; I 实测 represents the measured current of the mill hoist; I 目标 represents the preset target current of the mill hoist.

6. The intelligent scheduling algorithm for a cement mill according to claim 4, characterized in that: In S2.2, the control logic is as follows: if the bin weight deviation is positive, that is, the measured bin weight is higher than the target bin weight, the raw material supply may need to be reduced; if the current deviation is positive, that is, the measured current is higher than the target current, the running speed or load of the elevator may need to be adjusted.

7. The intelligent scheduling algorithm for a cement mill according to claim 1, characterized in that: In S4, the specific steps involved in the APC optimization technology are as follows: S3.1, establishing a dynamic prediction model reflecting the production process; S3.2, estimating the dynamic prediction model parameters through the autoregressive integrated moving average model to ensure that the dynamic prediction model can accurately reflect the dynamic characteristics of the actual production process; Wherein, the dynamic prediction model parameters include temperature, pressure; S3.3, using the dynamic prediction model to predict the control sequence and calculating an optimal control sequence; S3.4, the controller adjusts various operating variables in the production process to execute the feedback control strategy according to the optimized control sequence; S3.5, during the control execution process, the controller continuously receives new measurement data and adjusts the feedback control strategy according to the real-time data and the update of the dynamic prediction model to cope with changes and disturbances in the production process.

8. The intelligent scheduling algorithm for a cement mill according to claim 7, characterized in that: In S3.1, the dynamic prediction model is as follows: T(t+1)=a1T(t)+b1u1(t)+c1w1(t); P(t+1)=a2P(t)+b2u2(t)+c2w2(t); Wherein, T(t) represents the temperature at time t; P(t) represents the pressure at time t; u1(t) represents the operation variable of controlling temperature; u2(t) represents the operation variable of controlling pressure; w1(t) represents the external disturbance environment temperature fluctuation; w2(t) represents the external disturbance raw material component change; a1 represents the autoregressive coefficient of temperature; b1 represents the influence coefficient of control variable u1 on temperature; c1 represents the influence coefficient of external disturbance w1 on temperature; a2 represents the autoregressive coefficient of pressure; b2 represents the influence coefficient of control variable u2 on pressure; c2 represents the influence coefficient of external disturbance w2 on pressure.

9. The intelligent scheduling algorithm for a cement mill according to claim 7, characterized in that: In the S3.2, the autoregressive integrated moving average model is specifically: Δ d X t = c + φ1Δ d-1 X t-1 +... + φ p Δ d-p X t-p + θ1∈ t-1 +... + θ q ∈ t-q + ∈ t ; where Δ d denotes the difference operator; d denotes the difference order; X t denotes the value of the time series at time t; c denotes the constant term; φ i denotes the autoregressive term coefficient; θ j is the moving average term coefficient; ∈ t denotes the error term.

10. The intelligent scheduling algorithm for a cement mill according to claim 1, characterized in that: In the S5, the system operation state is continuously monitored, so as to ensure that each index is within the target range; the effect of the feedback control strategy is regularly evaluated, and adjustment is made according to the actual situation, so as to maintain the efficient and stable operation of the system.

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