Method for controlling the weight of the steady flow bin of roller press in cement production

Abstract

The present invention discloses a method for controlling the weight of a steady flow bin of a roller press in cement production, which is used for controlling the weight of the steady flow bin when materials enter the steady flow bin through an elevator during a roller press combined grinding process, and comprises the following steps: step 1, obtaining real-time data of the weight of the steady flow bin, and establishing a state space model of the weight of the steady flow bin; step 2, predicting the future output curve of the weight of the steady flow bin according to the state space model of the weight of the steady flow bin obtained in step 1, and comparing it with the expected set value of the weight of the steady flow bin to obtain the optimized value of the total feed amount of the mill; step 3, adjusting and controlling the total feed amount of the mill based on the optimized value of the total feed amount of the mill obtained in step 2, thereby controlling the actual weight of the steady flow bin. The present invention can realize the precise control of the weight of the steady flow bin during the production process.

Description

Technical Field

[0001] The invention relates to the field of cement production control methods, in particular to a method for controlling the bin weight of a roller press steady flow bin in cement production. Background Art

[0002] Most of the raw materials (clinker, slag, gypsum) used in cement production need to be ground into powder. During production, each raw material enters the steady flow bin through a metering belt scale, a belt conveyor and an elevator, and is squeezed by a roller press before being transported to subsequent production equipment through an elevator out of the roller press. Among them, the set flow rate of each metering belt scale (clinker, slag, gypsum) = the total feed amount of the mill * the set ratio of each raw material.

[0003] The steady flow bin is a device for holding the material to be pre-grinded. Its main function is to stabilize the flow. Once the steady flow bin is underweight or empty, the pressure of the material bed of the roller press will fluctuate greatly, resulting in poor extrusion effect. At the same time, the fine powder returned by the pulverizer will cause the roller press to vibrate and a large amount of dust will be raised in the feed pipe. Therefore, in actual production, the weight of the steady flow bin must be kept within a certain range so that the material grinding equipment (such as the roller press) can operate efficiently, thereby keeping the combined grinding system stable. Therefore, in the combined grinding process, the control of the steady flow bin directly affects the grinding process and even the finished material of the entire system as well as the energy consumption of the entire process.

[0004] At present, the control of steady flow bin weight still mainly relies on the operator's production experience to manually control the total feed amount of the mill. This can neither fully understand the operating status of the system as a whole, nor facilitate timely and automatic adjustment of operating parameters, which seriously affects the final product quality and production efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a method for controlling the weight of a steady flow bin in cement production, so as to solve the problem that the weight of a steady flow bin used in cement production in the prior art is difficult to be accurately controlled.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] The method for controlling the weight of a steady flow bin in cement production is used for controlling the weight of a steady flow bin when materials enter the steady flow bin through an elevator during a roller press combined grinding process, and comprises the following steps:

[0008] Step 1: Obtain the real-time data of the steady flow warehouse weight, and establish the steady flow warehouse weight state space model using the subspace identification method based on the acquired real-time data, as shown in formulas (1) and (2):

[0009] x(k+1)=Ax(k)+Bu(k) (1),

[0010] TWLC (k) = Cx(k) (2),

[0011] In formulas (1) and (2), x(k) is the state of the steady-flow bin-bin-reinforcement state space model; T WLC (k) is the output of the steady flow bin weight state space model, i.e., steady flow bin weight; u WLC (k) is the input quantity of the steady-flow bin weight state space model, i.e., the total feed amount of the mill; A, B, and C are matrices identified by the subspace identification method, k represents time, k=1,...,N;

[0012] Step 2: According to the steady flow bin weight state space model obtained in step 1, predict the future output curve of the steady flow bin weight, and compare it with the expected steady flow bin weight setting value to obtain the steady flow bin weight deviation, so as to obtain the optimal value u of the total feed amount of the mill. WLCpre (k);

[0013] Step 3: Optimized value u of total mill feed rate obtained in step 2 WLCpre (k) is used to adjust the total feed rate of the control mill, thereby controlling the actual weight of the steady flow bin.

[0014] Furthermore, in step 1, the acquired real-time data is filtered and normalized in turn, and then used to establish a steady-flow bin-bin-heavy state space model.

[0015] Furthermore, a moving average filter is used to filter the acquired real-time data.

[0016] Furthermore, the min-max algorithm is used to normalize the filtered real-time data.

[0017] Furthermore, in step 1, the average correlation variance method is used to determine the accuracy of the established steady-flow bin-bin-heavy state space model.

[0018] Furthermore, when controlling the actual weight of the steady flow bin in step 3, the actual weight of the steady flow bin at this time is obtained. When the actual weight of the steady flow bin is greater than the upper limit of the weight or less than the lower limit of the weight, step 3 is stopped.

[0019] Furthermore, in step 3, when controlling the actual weight of the steady flow bin, the increase or decrease of the actual weight of the steady flow bin and the increase or decrease of the working current of the roller press elevator are obtained;

[0020] When the actual weight reduction of the steady flow bin reaches the corresponding set threshold, and the working current increment of the roller press elevator reaches the corresponding set threshold, it is judged that an abnormality has occurred. At this time, the predicted optimization value u at the previous moment will be used. WLCpre(k-1), so the total feed rate of the mill is no longer adjusted to avoid the elevator being crushed and causing shutdown; if the actual bin weight of the subsequent steady flow bin begins to increase, the actual predicted optimization value u WLCpre (k) Continue to adjust and control the total feed rate of the mill.

[0021] Compared with the prior art, the advantages of the present invention are:

[0022] The present invention predicts the weight of the steady flow bin by establishing a steady flow bin weight state space model, obtains the optimization value for controlling the material grinding equipment based on the comparison between the prediction result and the set value, and realizes the control of the material grinding equipment based on the optimization value, thereby realizing the control of the weight of the steady flow bin, thereby realizing the precise control of the weight of the steady flow bin in the production process. In addition, when controlling the material grinding equipment, various abnormal situations are also considered, and the control process can be adjusted according to the abnormalities that occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of a control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0025] like Figure 1 As shown, the method for controlling the weight of a steady flow bin in cement production in this embodiment is used in a combined grinding system for cement, and the combined grinding system includes a steady flow bin and a material grinding device. The material to be ground is placed in the steady flow bin, and the material grinding device grinds the material in the steady flow bin. The ground material is transported out of the steady flow bin through an elevator. This embodiment includes the following steps:

[0026] Step 1: First, obtain the real-time data of the steady flow warehouse weight {T WLC (k),u WLC (k)}, where T WLC (k) is the weight of the steady flow bin, u WLC (k) is the total feed amount of the mill, k is time, k=1,...,N.

[0027] Then, a moving average filter is used to filter the real-time data of the steady flow bin {T WLC (k),u WLC (k)} is filtered to obtain the filtered data in is the weight of the steady flow bin after filtering, is the total feed volume of the mill after filtering.

[0028] Next, the min-max normalization algorithm is used to normalize the filtered data. Perform normalization to obtain normalized data in is the normalized steady flow bin weight, is the normalized total mill feed amount.

[0029] Finally, based on the normalized data The subspace identification method is used to identify the matrices A, B, and C, and the steady-state flow bin-bin weight state space model is established as shown in formulas (1) and (2):

[0030] x(k+1)=Ax(k)+Bu(k) (1),

[0031] T WLC (k) = Cx(k) (2),

[0032] In formulas (1) and (2), x(k) is the state of the steady-flow bin-bin-reinforcement state space model; T WLC (k) is the output of the steady flow bin weight state space model, i.e., steady flow bin weight; u WLC (k) is the input quantity of the steady-flow bin weight state space model, that is, the total feed amount of the mill; A, B, C are matrices identified by the subspace identification method, k represents time, k=1,...,N.

[0033] In step 1 of this embodiment, the process of identifying the matrix using the subspace identification method is as follows:

[0034]

[0035] In formula (3), x m (k) represents the state of the steady flow warehouse weight state space model; u(k) represents the input of the steady flow warehouse weight state space model, y(k) represents the output of the steady-state bin-bin-heavy state space model, A m , B m , C m is the matrix before identification.

[0036] From formula (3), we can get:

[0037] Δx m (k+1 ) =x m (k+1)-x m (k)

[0038] =A m (x m (k)-x m (k-1))+B m (u(k)-u(k)-1))

[0039] =A m Δx m (k)+B m Δu(k) (4),

[0040] y(k+1)-y(k)=C m (x m (k+1)-x m (k)) = C m Δx m (k+1)=C m A m Δx m (k)+C m B m Δu(k)(5),

[0041] According to (4) and (5), the state space equation is shown in formula (6):

[0042]

[0043] in:

[0044] C=[0 m 1],0 m =[0,…,0].

[0045] A m , B m , C m is the matrix before identification by the subspace identification method, and A, B, and C are the matrices identified by the subspace identification method.

[0046] In step 1 of this embodiment, after obtaining the steady-flow bin-bin-resource state space model, the average correlation variance method can be used to determine the accuracy of the steady-flow bin-bin-resource state space model. The process is as follows:

[0047] Step 1.1: Using the steady flow warehouse weight state space model and the steady flow warehouse weight real-time data {T WLC (k),u WLC (k)}, find the model prediction output T PreWLC (k);

[0048] Step 1.2: Find the model prediction output T PreWLC (k) and the actual output T WLC (k) The difference ΔT WLC (k);

[0049] Step 1.3, according to the formula:

[0050]

[0051] Find the average correlation variance. The smaller the value, the higher the accuracy.

[0052] Step 2: According to the steady flow bin weight state space model obtained in step 1, predict the future output curve of the steady flow bin weight, and compare it with the expected steady flow bin weight setting value to obtain the steady flow bin weight deviation, so as to obtain the optimal value u of the total feed amount of the mill. WLCpre (k).

[0053] The comparison process of step 2 of this embodiment is as follows:

[0054] Through the above comparison process, the weight deviation of the steady flow bin is obtained, and the predictive control optimization is performed based on the deviation to obtain the optimal value u of the total feed amount of the material grinding equipment WLCpre (k).

[0055] Step 3: Optimized value u of total mill feed rate obtained in step 2 WLCpre (k) is used to adjust and control the total feed amount of the grinding mill. Therefore, when the material grinding equipment is grinding the material in the steady flow bin, the actual bin weight of the steady flow bin can be dynamically adjusted according to the grinding workload of the material grinding equipment, thereby realizing precise control of the bin weight of the steady flow bin during the grinding process.

[0056] In step 3 of this embodiment, it is also considered that the current of the steady flow bin and the current of the roller press hoist may be abnormal, and the control process of step 3 is adjusted according to the abnormal situation. The specific description is as follows:

[0057] (1) Get the actual weight G1 of the current stable flow warehouse. When the actual weight G1 of the stable flow warehouse is greater than the upper limit of the weight G up Or less than the lower limit of warehouse weight G down When , it indicates that the weight of the steady flow bin has abnormal data. At this time, stop executing step 3 and use manual control to adjust the weight of the steady flow bin. up , G down All can be configured according to the actual needs of cement production, and G up >G down .

[0058] (2) Obtain the increase or decrease in the actual bin weight G1 of the steady flow bin within time T1 and the increase or decrease in the working current of the roller press elevator within time T2.

[0059] When the actual weight of the steady flow bin G1 decreases by G within time T1 delta Reach the corresponding set threshold, and the working current increment I of the roller press elevator delta When the corresponding set threshold is reached, it is judged that the current of the roller press hoist is abnormal. At this time, the predicted optimization value u at the previous moment will be used. WLCpre(k-1), so the total feed rate of the mill is no longer adjusted according to step 3. If after time T3, the actual bin weight G1 of the steady flow bin begins to increase, then the predicted optimization value u obtained in step 3 is used again WLCpre (k) Continue to adjust and control the total feed rate of the mill.

[0060] The embodiments described in the present invention are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made to the technical solutions of the present invention by engineers and technicians in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have all been recorded in the claims.

Claims

1. A method for controlling the weight of a steady flow bin of a roller press in cement production, which is used to control the weight of a steady flow bin when materials enter the steady flow bin through an elevator during a roller press combined grinding process, and is characterized in that: The following steps are involved: Step 1: Obtain the real-time data of the steady flow warehouse weight, and establish the steady flow warehouse weight state space model using the subspace identification method based on the acquired real-time data, as shown in formulas (1) and (2): x(k+1)=Ax(k)+Bu WLC (k) (1), T WLC (k)=Cx(k) (2), In formulas (1) and (2), x(k) is the state of the steady-flow bin-bin-reinforcement state space model; T WLC (k) is the output of the steady flow bin weight state space model, i.e., steady flow bin weight; u WLC (k) is the input quantity of the steady-flow bin weight state space model, i.e., the total feed amount of the mill; A, B, and C are matrices identified by the subspace identification method, k represents time, k=1,...,N; Step 2: According to the steady flow bin weight state space model obtained in step 1, predict the future output curve of the steady flow bin weight, and compare it with the expected steady flow bin weight setting value to obtain the steady flow bin weight deviation, so as to obtain the optimal value u of the total feed amount of the mill. WLCpre (k); Step 3: Optimized value u of total mill feed rate obtained in step 2 WLCpre (k) to adjust the total feed rate of the control mill, thereby controlling the actual weight of the steady flow bin; The process of identifying the matrix by the subspace identification method is as follows: In formula (3), x m (k) represents the state of the steady flow warehouse weight state space model; u(k) represents the input of the steady flow warehouse weight state space model, y(k) represents the output of the steady-state bin-bin-heavy state space model, A m , B m , C m is the matrix before identification; From formula (3), we can get: Δx m (k+1)=x m (k+1)-x m (k) =A m (x m (k)-x m (k-1))+B m (u(k)-u(k)-1)) =A m Δx m (k)+B m Δu(k) (4), y(k+1)-y(k)=C m (x m (k+1)-x m (k))=C m Δx m (k+1) =C m A m Δx m (k)+C m B m Δu(k) (5), According to (4) and (5), the state space equation is shown in formula (6): in: C＝[0 m 1],0 m ＝[0,…,0]； A m , B m , C m is the matrix before subspace identification method, and A, B, and C are the matrices identified by the subspace identification method.

2. The method for controlling the weight of a steady flow bin in cement production according to claim 1, characterized in that: In step 1, the acquired real-time data is filtered and normalized in turn, and then used to establish a steady-flow bin-bin-heavy state space model.

3. The method for controlling the weight of a steady flow bin in cement production according to claim 2, characterized in that: The acquired real-time data is filtered using a moving average filter.

4. The method for controlling the weight of a steady flow bin in cement production according to claim 2, characterized in that: The min-max algorithm is used to normalize the filtered real-time data.

5. The method for controlling the weight of a steady flow bin in cement production according to claim 1, characterized in that: In step 1, the average correlation variance method is used to determine the accuracy of the established steady-flow bin-bin-recharge state space model.

6. The method for controlling the weight of a steady flow bin in cement production according to claim 1, characterized in that: When controlling the actual weight of the steady flow bin in step 3, obtain the actual weight of the steady flow bin at this time. When the actual weight of the steady flow bin is greater than the upper limit of the weight or less than the lower limit of the weight, stop executing step 3.

7. The method for controlling the weight of a steady flow bin in cement production according to claim 1, characterized in that: In step 3, when controlling the actual weight of the steady flow bin, the increase or decrease of the actual weight of the steady flow bin and the increase or decrease of the working current of the roller press elevator are obtained; when the decrease of the actual weight of the steady flow bin reaches the corresponding set threshold, and the increase of the working current of the roller press elevator reaches the corresponding set threshold, it is judged that an abnormality occurs, and the predicted optimization value u obtained at the previous moment is used at this time. WLCpre (k-1), and the total feed rate of the mill is no longer adjusted; if the actual weight of the subsequent steady flow bin begins to increase, the predicted optimization value u obtained in step 2 is WLCpre (k) Continue to adjust and control the total feed rate of the mill.

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