阅读说明：本技术 一种基于云端智能模型的球磨制粉系统有效控制方法 (Ball milling and pulverizing system effective control method based on cloud intelligent model ) 是由 刘利民 于 2020-10-19 设计创作，主要内容包括：本发明提出了一种基于云端智能模型的球磨制粉系统有效控制方法,该方法的实现基于现场控制器和包含产出转换算法的智能控制模型,所述智能控制模型接收现场控制器和产出转换算法反馈的信息,并给出控制调整方案和指令。本发明采用人工智能和模糊控制技术,通过云端的智能控制模型对现场控制器采集的数据进行分析判断,给出控制决策,指导现场控制器对球磨进行有效调节。该方法为非线性MIMO模型的分布控制法,所述模型为设置在云中心的具有自学习自校正功能的智能调节控制模型。本发明可以对球磨运行实现有效自动调节控制,从而在相同产出的条件下降低能耗,提高球磨制粉的生产效益,并且可以为相关物联网系统的实现提供支撑。(The invention provides an effective control method of a ball milling and pulverizing system based on a cloud intelligent model, which is realized based on a field controller and an intelligent control model comprising an output conversion algorithm, wherein the intelligent control model receives information fed back by the field controller and the output conversion algorithm and provides a control adjustment scheme and instructions. The system adopts artificial intelligence and fuzzy control technology, analyzes and judges data acquired by the field controller through the intelligent control model at the cloud end, gives a control decision, and guides the field controller to effectively adjust the ball mill. The method is a distributed control method of a nonlinear MIMO model, and the model is an intelligent regulation control model which is arranged in a cloud center and has a self-learning self-correcting function. The invention can realize effective automatic regulation and control on the operation of the ball mill, thereby reducing energy consumption under the condition of the same output, improving the production benefit of ball milling powder preparation, and providing support for realizing a related Internet of things system.)

1. A ball milling and pulverizing system effective control method based on a cloud intelligent model is characterized in that: the method is realized based on a field controller and an intelligent control model comprising an output conversion algorithm, the intelligent control model receives information fed back by the field controller and the output conversion algorithm and gives a control adjustment scheme and an instruction, the field controller is used for analyzing an output signal of the ball milling powder making system and adjusting the input quantity of the ball milling powder making system, and the ball milling powder making system obtains an output target of the system through a plurality of outputs through the output conversion algorithm: the powder output PP;

the regulating quantity output of the field controller is used as the input of the ball milling powder making system, and the state detection signal of the field controller is used as the output of the ball milling powder making system; the input vector of the ball milling powder making system comprises hot air HA, recirculation air RA and coal supply CF, the output vector of the ball milling powder making system comprises outlet temperature OT, inlet negative pressure SP, inlet and outlet pressure difference PD and mill load CL, and the ball milling load is measured and calculated through the mill load CL and the inlet and outlet pressure difference PD; the powder output PP of the ball milling powder making system is obtained by an output conversion algorithm through an outlet temperature OT, an inlet negative pressure SP, an inlet-outlet pressure difference PD and a mill load CL;

the intelligent control model comprises a weight model W { xi (t) } of a yield conversion algorithm, a system control regulation model R { Yj (t)) } and a feedback model F { Zk (t)) } and a self-learning self-correcting correction model C_l(t)；

The output conversion algorithm based on the weight model is as follows:

the output and input relations based on the regulation model are as follows:

Xk(t)＝R{Yij(t)}{Yj(t)}^T (2-1)

the input and output relation based on the feedback model is as follows:

Yk(t)＝F{Zij(t)}{Zj(t)}^T+B_k (3-1)

the model adjustment relationship based on the correction model is as follows:

W{Xi(t)}＝ΣC_w(t){Xi(t)}{Yj(t)} (4-1)

R{Yj(t)}＝ΣC_R(t){Yj(t)}{Xi(t)} (4-2)

F{Zk(t)}＝ΣC_F(t){Xk(t)}{Yj(t)} (4-3)

wherein, the powder output PP is related to the output of the ball milling powder making system, X is related to the output of the ball milling powder making system, Y is related to the input of the ball milling powder making system, W is related to the weight model, R is related to the adjusting model, F is related to the feedback model, C is related to the correcting model, B is related to the reference input, { … }^TIs a transposed matrix.

2. The effective control method of the ball milling and pulverizing system based on the cloud intelligent model as claimed in claim 1, characterized in that: all models included in the intelligent control model are human-like intelligent models based on fuzzy expression.

3. The effective control method of the ball milling and pulverizing system based on the cloud intelligent model as claimed in claim 1, characterized in that: the self-learning self-correcting model adopts a human-like algorithm or an artificial intelligence algorithm, analyzes the effectiveness of the adjusting model according to the action relation between the adjusting instruction and the adjusting effect, corrects the related adjusting algorithm and instruction, and enables the adjusting effect of the intelligent adjusting part to reach an expected state in continuous learning and correction.

4. The effective control method of the ball milling and pulverizing system based on the cloud intelligent model as claimed in claim 3, characterized in that: the self-learning self-correcting correction model has the following relation:

W{Xi(t)}＝ΣC_w(t){Xi(t)}{Yj(t)} (4-1)

wherein xi (t) is associated with OT (t), SP (t), PD (t), CL (t);

R{Yj(t)}＝ΣC_R(t){Yj(t)}{Xi(t)} (4-2)

wherein Yj (t) and HA_j(t)，RA_j(t)，CF_j(t) correlating;

F{Zk(t)}＝ΣC_F(t){Xk(t)}{Yj(t)} (4-3)

wherein, Zk (t) and OT_k(t)，SP_k(t)，PD_k(t)，CL_k(t) correlating;

c in the formulae 4-1, 4-2, 4-3_w(t)、C_R(t)、C_FAnd (t) correction algorithms of the correction models are based on the human-like intelligent reasoning relation of the fuzzy description.

5. The effective control method of the ball milling and pulverizing system based on the cloud intelligent model as claimed in claim 1, characterized in that: the ball milling powder making system is a three-input four-output MIMO system, and the powder output PP of the system is obtained through four outputs through an output conversion algorithm.

6. The effective control method of the ball milling and pulverizing system based on the cloud intelligent model as claimed in claim 5, characterized in that: the regulation of the three inputs is: the coal feeding quantity CF is used for controlling and adjusting the inlet and outlet pressure difference PD and the mill load CL, the hot air HA is used for controlling and adjusting the outlet temperature OT, and the recirculation air RA is used for controlling and adjusting the inlet negative pressure SP.

[ technical field ] A method for producing a semiconductor device

The invention relates to the technical field of automatic control of ball milling powder making systems, in particular to an effective control method of a ball milling powder making system based on a cloud intelligent model, and more particularly, the method is used for adjusting and controlling a field ball milling powder making system through an intelligent model arranged in a cloud center so as to achieve the purpose of effective control.

[ background of the invention ]

The ball milling and pulverizing system is the main pulverizing system of the domestic thermal power plant. The system is a nonlinear multivariable system with large hysteresis, large inertia and strong coupling, and is a flexible rib for automatic control of a thermal power plant for a long time, and the system is an almost unsolvable problem especially in the instrument control era.

In order to realize the automatic adjustment control of the ball milling powder preparation system, almost all control methods of the nonlinear coupling multivariable system, such as decoupling, inverse Neisseria array, self-optimization, prediction, fuzzy, neural network, expert system, hybrid control and the like, have been researched, and many advances are made. However, the characteristics of multiple disturbance, nonlinearity and multiple coupling variables of the ball milling powder system make the control depending on the mathematical model difficult to realize.

At present, an intelligent control ball milling system is adopted, and is a main way for realizing automatic regulation and control of the system. However, because the coal types used by the power plant are variable, the coal quality is different, the indexes of the raw coal such as granularity, moisture, temperature, grindability coefficient, volatile content and the like are frequently changed, and the steel ball is continuously abraded in the operation process, the steel ball coal mill has time-varying characteristics, namely, the system parameters change along with the time, so that the regulation efficiency of a common intelligent control system is reduced. Therefore, the system control is poor in universality and effectiveness, and is difficult to popularize.

[ summary of the invention ]

The invention aims to solve the problems in the prior art, provides an effective control method of a ball milling powder making system based on a cloud intelligent model, adopts artificial intelligence and fuzzy control technology, provides support for realizing automatic adjustment control of ball milling powder making equipment, and has the advantages of good universality, strong effectiveness and self-learning and self-correcting functions.

In order to achieve the purpose, the invention provides an effective control method of a ball milling and pulverizing system based on a cloud intelligent model, which is characterized by comprising the following steps: the method is realized based on a field controller and an intelligent control model comprising an output conversion algorithm, the intelligent control model receives information fed back by the field controller and the output conversion algorithm and gives a control adjustment scheme and an instruction, the field controller is mainly responsible for analyzing an output signal of the ball milling powder making system and adjusting the input quantity of the ball milling powder making system, and the ball milling powder making system obtains the output target of the system through a plurality of outputs through the output conversion algorithm: the powder output PP;

the regulating quantity output of the field controller is used as the input of the ball milling powder making system, and the state detection signal of the field controller is used as the output of the ball milling powder making system; the input vector (the adjustment output of a field controller) of the ball milling powder making system comprises hot air HA, recirculation air RA and coal supply quantity CF, the output vector (the state detection signal of the field controller) of the ball milling powder making system comprises outlet temperature OT, inlet negative pressure SP, inlet and outlet pressure difference PD and mill load CL, and the ball milling load is measured and calculated through the mill load CL and the inlet and outlet pressure difference PD; the powder output PP of the ball milling powder making system is obtained by an output conversion algorithm through an outlet temperature OT, an inlet negative pressure SP, an inlet-outlet pressure difference PD and a mill load CL;

the intelligent control model comprises a weight model W { xi (t) } of a yield conversion algorithm, a system control regulation model R { Yj (t)) } and a feedback model F { Zk (t)) } and a self-learning self-correcting correction model Cl (t);

the output conversion algorithm based on the weight model is as follows:

the output and input relations based on the regulation model are as follows:

Xk(t)＝R{Yij(t)}{Yj(t)}^T (2-1)

the input and output relation based on the feedback model is as follows:

Yk(t)＝F{Zij(t)}{Zj(t)}^T+B_k (3-1)

the model adjustment relationship based on the correction model is as follows:

W{Xi(t)}＝ΣC_w(t){Xi(t)}{Yj(t)} (4-1)

R{Yj(t)}＝ΣC_R(t){Yj(t)}{Xi(t)} (4-2)

F{Zk(t)}＝ΣC_F(t){Xk(t)}{Yj(t)} (4-3)

whereinThe output PP is related to the output of the ball milling system, X is related to the output of the ball milling system, Y is related to the input of the ball milling system, W is related to the weight model, R is related to the regulation model, F is related to the feedback model, C is related to the correction model, B is related to the reference input, { … }^TIs a transposed matrix.

Preferably, all models included in the intelligent control model are human-like (human-simulated) intelligent models (artificial intelligent models) based on fuzzy expressions, the fuzzy expressions are eleven-grade fuzzy descriptions, and each model adopts an artificial intelligent human-like reasoning model instead of a mathematical model.

Preferably, the self-learning self-correcting model adopts a human-like algorithm or an artificial intelligence algorithm, the effectiveness of the adjusting model is analyzed according to the action relation between the adjusting instruction and the adjusting effect, the related adjusting algorithm and instruction are corrected, and the adjusting effect of the intelligent adjusting part reaches an expected state in continuous learning and correction.

Preferably, the self-learning self-correcting correction model has the following relationship:

W{Xi(t)}＝ΣC_w(t){Xi(t)}{Yj(t)} (4-1)

wherein xi (t) is associated with OT (t), SP (t), PD (t), CL (t);

R{Yj(t)}＝ΣC_R(t){Yj(t)}{Xi(t)} (4-2)

wherein Yj (t) and HA_j(t)，RA_j(t)，CF_j(t) correlating;

F{Zk(t)}＝ΣC_F(t){Xk(t)}{Yj(t)} (4-3)

wherein, Zk (t) and OT_k(t)，SP_k(t)，PD_k(t)，CL_k(t) correlating;

c in the formulae 4-1, 4-2, 4-3_w(t)、C_R(t)、C_FAnd (t) correction algorithms of the correction models are based on the human-like intelligent reasoning relation of the fuzzy description.

Preferably, the ball milling powder making system is a three-input four-output MIMO system, and the output PP of the system is obtained through four outputs through an output conversion algorithm.

Preferably, the main regulating functions of the three input quantities are: the coal feeding quantity CF is mainly used for controlling and adjusting the inlet and outlet pressure difference PD and the mill load CL, the hot air HA is mainly used for controlling and adjusting the outlet temperature OT, and the recirculation air RA is mainly used for controlling and adjusting the inlet negative pressure SP. However, the system has a strong coupling relation, and any input quantity has an influence on other output quantities besides the corresponding output quantity. The ideal control effect of the ball milling and pulverizing system is to produce qualified coal powder with maximum efficiency under the condition of ensuring the safety of the system and the quality of the coal powder. The inlet negative pressure SP of the system ensures that the ball mill can run safely without powder leakage, the outlet temperature OT maintains the drying of the coal powder to ensure the dryness quality of the coal powder, and the full-load running of the ball mill ensures high powder yield under unit power consumption. Therefore, only by coordinating and controlling four parameters, namely outlet temperature OT, inlet negative pressure SP, load CL + PD and the like, the ball milling powder making system can achieve a satisfactory control effect, the powder making efficiency is improved, and the unit power consumption is reduced.

The output conversion algorithm is a part of an intelligent control model, and can convert parameters of outlet temperature OT, inlet negative pressure SP, inlet and outlet pressure difference PD and mill load CL into product powder output PP of a ball mill powder making system. The reason why the output conversion is needed to calculate the powder output is that no method for directly detecting the powder output of the ball milling system exists at present. The yield transformation algorithm may be dynamically adjusted by the self-learning self-correcting model.

The intelligent control model is a control model which is arranged in a cloud center and has a self-learning and self-correcting function. The model receives information fed back by the site controller and the output conversion algorithm and gives a control adjustment scheme and instructions. The working path of the intelligent control model for realizing system adjustment is as follows: data processing, parameter analysis, deductive reasoning, decision correction, decision determination and transmission. The working path of the intelligent control model for realizing self-learning self-correction is as follows: data processing, decision effect analysis, decision rule comparison, effective decision historical data analysis, model correction judgment and processing, and establishment of a new control intelligent regulation model.

The invention has the beneficial effects that: the system adopts artificial intelligence and fuzzy control technology, analyzes and judges data acquired by the field controller through the intelligent control model at the cloud end, gives a control decision, and guides the field controller to effectively adjust the ball mill. The method is a distributed control method of a nonlinear MIMO model, and adopts an intelligent regulation control model which is arranged in a cloud center and has a self-learning self-correcting function.

According to the invention, the outlet temperature OT, the inlet negative pressure SP, the inlet-outlet pressure difference PD and the mill load CL are correspondingly changed by adjusting the hot air HA, the recirculation air RA and the coal supply CF, so that the adjustment of the powder output PP of the ball milling powder making system is realized, and the purpose of achieving the expected powder output with the lowest energy consumption is achieved. The invention can realize effective automatic regulation and control on the operation of the ball mill, thereby reducing energy consumption under the condition of the same output, improving the production benefit of ball milling powder preparation, and providing support for realizing a related Internet of things system.

The features and advantages of the present invention will be described in detail by embodiments in conjunction with the accompanying drawings.

[ description of the drawings ]

Fig. 1 is a schematic diagram of a system structure of an effective control method of a ball milling system based on a cloud intelligent model according to the present invention;

fig. 2 is a control system block diagram of an effective control method of a ball milling system based on a cloud intelligent model.

[ detailed description ] embodiments

Fig. 1 is a schematic structural diagram of a system of the present invention, which mainly includes an intelligent control model disposed in a cloud center, a field controller, an output conversion algorithm (a part of the intelligent control model), and a ball milling system.

The adjustment operation of the invention is completed by the operation of the cloud center, and the cloud center can adopt a distribution control method to simultaneously control a plurality of ball milling powder making systems. The method can also be used as a control method of the equipment of the Internet of things.

Referring to fig. 2, the objective of the present invention is to effectively adjust the powder output of the ball milling system, and the adjustment relationship and output conversion algorithm corresponding to the control system are as follows:

the concrete description is as follows:

PP(t)＝W{X1(t),X2(t),X3(t),X4(t)}*{X1(t),X2(t),X3(t),X4(t)}^T

＝W{OT(t),SP(t),PD(t),CL(t)}*{OT(t),SP(t),PD(t),CL(t)}^T (1-2)

formulas 1-1 and 1-2 are mainly based on a weight model W { Xi } of a yield transformation algorithm. Wherein PP (t) is the powder output of the system, X1(t) is the outlet temperature OT (t), X2(t) is the inlet negative pressure SP (t), X3(t) is the inlet-outlet pressure difference PD (t), and X4(t) is the onboard load CL (t).

Before explaining this relationship, there is a condition that must be clarified that the output of the system is the powder output pp (t) of the ball milling operation, but there is no method that can directly measure the powder output at present, limited to the technical condition. In this system, the powder output is calculated. The way of calculation is: the amount of coal fed per unit time-the amount of coal stored in the mill-is the amount of coal removed. The expressions 1-1 and 1-2 (hereinafter referred to as the expression 1) are output conversion algorithms of the ball milling powder system. The algorithm is carried out on the premise of ensuring the quality of the output pulverized coal, and the powder output PP is obtained through comprehensive calculation of four parameters and a weight function W { xi (t) } of different output conversion algorithms. In the specific operation, X1(t) outlet temperature OT (t), X2(t) inlet negative pressure SP (t), X3(t) outlet-inlet pressure difference PD (t) and X4(t) onboard load CL (t) are involved. The airborne load CL (t) is the real-time coal storage amount in the mill measured by a vibration frequency method.

From equation 1, if the powder discharge amount pp (t) is desired to be adjusted, the outlet temperature ot (t), the inlet negative pressure sp (t), the inlet-outlet pressure difference pd (t), and the onboard load cl (t) in the function are required to be obtained.

The specific correlation and coupling will be understood from the following description in order to obtain dynamic values for the four parameters that will involve a multivariate, strongly coupled, nonlinear correlation function.

As can be seen from fig. 1, for the site controller, the input quantities of the system are: the hot air HA (t), the recycle air RA (t) and the coal supply amount CF (t) are regulated. The output parameters outlet temperature OT (t), inlet negative pressure SP (t), inlet and outlet pressure difference PD (t), onboard load CL (t), input quantity adjusting hot air HA (t), recirculation air RA (t) and coal supply quantity CF (t) are in the following relation:

Xk(t)＝R{Yij(t)}{Yj(t)}^T (2-1)

OT(t)＝R{HA₁(t),RA₁(t),CF₁(t)}*{HA(t),RA(t),CF(t)}^T (2-2)

SP(t)＝R{HA₂(t),RA₂(t),CF₂(t)}*{HA(t),RA(t),CF(t)}^T (2-3)

PD(t)＝R{HA₃(t),RA₃(t),CF₃(t)}*{HA(t),RA(t),CF(t)}^T (2-4)

CL(t)＝R{CFL(t)}*CF(t)+R{CFL(t)}*F{CL(t)}*CL(t-1) (2-5)

equations 2-1, 2-2, 2-3, 2-4, and 2-5 (hereinafter, abbreviated as equation 2) are based on the system's regulation model R { yj (t) }. From the equations, it can be seen that each of the three outputs ot (t), sp (t), pd (t) is the result of the correlation of the three input quantities ha (t), ra (t), cf (t) for the adjustment functions R { hai (t), rai (t), cfi (t) }. Another output parameter CL (t) is the effect of the input quantity CF (t) on the regulation function R { CFL (t) + the effect of the value CL (t-1) of the preceding period of time of CL itself on the feedback function F { CL (t)) } and on the regulation R { CFL (t)) }.

The three inputs of the system, HA (t), RA (t), CF (t), are not simply given, and are described in detail below.

Yk(t)＝F{Zij(t)}{Zj(t)}^T+B_k (3-1)

HA(t)＝F{OT₁(t),SP₁(t),PD₁(t)}*{OT(t-1),SP(t-1),PD(t-1)}^T+B_HA (3-2)

RA(t)＝F{OT₂(t),SP₂(t),PD₂(t)}*{OT(t-1),SP(t-1),PD(t-1)}^T+B_RA (3-3)

CF(t)＝F{OT₃(t),SP₃(t),PD₃(t),CL₃(t)}*{OT(t-1),SP(t-1),PD(t-1),CL(t-1)}^T+B_CF (3-4)

Equations 3-1, 3-2, 3-3, and 3-4 (hereinafter simply referred to as equation 3) are based on a feedback model F { zk (t) } of the system. As can be seen from the description, the input quantities HA (t), RA (t) are reference values B_HA、B_RAAnd the values OT (t-1), SP (t-1) and PD (t-1) of the previous time period of the three output OT (t), SP (t), PD (t) parameters to the feedback function F { OT { (t) }_i(t),SP_j(t),PD_k(t) } integration of the effects; the input quantity CF being a reference value B_RAAnd four previous-period values OT (t-1), SP (t-1), PD (t-1), CL (t-1) of the output OT (t), SP (t), PD (t), CL (t-1) parameters for a feedback function F { OT { T }_i(t),SP_j(t),PD_k(t),CL_l(t) } integration of the effects. That is, there is a close correlation between the three input quantities and the four output quantities of the system. It can be seen that it is impossible to build an effective mathematical control model based on the non-linear, strongly coupled, highly correlated multivariable characteristics of the system. In order to realize the automatic control of the system, an intelligent regulation model is required.

The intelligent control model of setting in high in the clouds of system includes two parts: the intelligent adjusting part and the self-learning self-correcting part. The above formulas 1, 2 and 3 all belong to the intelligent regulation part.

The weight function of the yield conversion algorithm of formula 1, the adjustment function of formula 2, and the feedback function of formula 3 are not mathematical models, but intelligent models established by the inference rule of IF-THEN. The description of the numerical values in the model adopts eleven-grade fuzzy description, and specifically comprises the following steps: particularly small ES, very small VS, small S, small MS, slightly small LS, median ZO, slightly large LL, large ML, large L, very large VL, particularly large EL.

There are a number of inference rules and inductive algorithms in the intelligent model, such as an action of equations 3-3, which can be expressed by the IF-THEN rule as:

IFOT(t-1)isMLTHENCF1(t)isLL；

IFSP(t-1)isMSTHENCF2(t)isLS；

IFPD(t-1)isLLTHENCF3(t)isLS；

IFCL(t-1)isMSTHENCF4(t)isML；

CF(t)＝CF1(t)+CF2(t)+CF3(t)+CF4(t)

＝LL+ES+LS+ML

＝LL

this result means that the CF adjustment value should be slightly larger for the amount of coal. According to different ball mill models and systems, the specific coal feeder adjustment amount corresponding to a slightly larger adjustment value is different.

The self-learning self-correcting part adopts a human-like algorithm or an artificial intelligence algorithm, analyzes the effectiveness of the adjusting model according to the action relation of the adjusting instruction and the adjusting effect, corrects the related adjusting algorithm and instruction, and enables the adjusting effect of the intelligent adjusting part to reach an expected state in continuous learning and correction.

The self-learning self-correcting model relationship is as follows:

W{Xi(t)}＝ΣC_w(t){Xi(t)}{Yj(t)} (4-1)

wherein xi (t) is associated with OT (t), SP (t), PD (t), CL (t).

R{Yj(t)}＝ΣC_R(t){Yj(t)}{Xi(t)} (4-2)

Wherein Yj (t) and HA_j(t)，RA_j(t)，CF_j(t) are correlated.

F{Zk(t)}＝ΣC_F(t){Xk(t)}{Yj(t)} (4-3)

Wherein, Zk (t) and OT_k(t)，SP_k(t)，PD_k(t)，CL_k(t) are correlated.

C in the formulae 4-1, 4-2, 4-3_w(t)、C_R(t)、C_FAnd (t) correction algorithms of the self-learning self-correcting model, which are all based on the human-like intelligent reasoning relation of the fuzzy description.

The above 4-1, 4-2 and 4-3 represent that in the self-learning self-calibration process, the weight model W { xi (t) }, the pulverizing adjustment model R { yj (t)) } and the feedback model F { zk (t)) } of the output conversion algorithm to be improved are reasonably adjusted by analyzing the input/output, adjustment and feedback action processes and results, so that the input and output of the ball milling pulverizing system are more optimized, more efficient and more beneficial.

The above embodiments are illustrative of the present invention, and are not intended to limit the present invention, and any simple modifications of the present invention are within the scope of the present invention.