CN113877715A - MSET-based method for monitoring abrasion state of easily-abraded part of coal mill of intelligent power plant - Google Patents

MSET-based method for monitoring abrasion state of easily-abraded part of coal mill of intelligent power plant Download PDF

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CN113877715A
CN113877715A CN202111315142.3A CN202111315142A CN113877715A CN 113877715 A CN113877715 A CN 113877715A CN 202111315142 A CN202111315142 A CN 202111315142A CN 113877715 A CN113877715 A CN 113877715A
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coal
coal mill
mill
pebble
early warning
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CN113877715B (en
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吴业成
袁伟中
金宏伟
孙永平
张震伟
屠海彪
陆金奇
王豆
姜志锋
傅骏伟
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Zhejiang Zheneng Taizhou No2 Power Generation Co ltd
Zhejiang Energy Group Research Institute Co Ltd
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Zhejiang Zheneng Taizhou No2 Power Generation Co ltd
Zhejiang Energy Group Research Institute Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
    • B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
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Abstract

The invention relates to a method for monitoring the abrasion state of an easily-abraded part of a coal mill of an intelligent power plant based on MSET (modeling, simulation and engineering) and comprises the following steps: acquiring historical data of measuring points capable of reflecting the abrasion state of the easily-abraded part of the coal mill under different working conditions; the method comprises the steps of establishing a coal mill wear state monitoring and early warning model by using a multivariate state estimation technology, wherein the established model is mainly used for monitoring the wear state of a wear-prone part of the coal mill; and evaluating the state of the abrasion clearance of the coal mill according to the emission rate of pebble coal of the coal mill, sending out corresponding early warning, and overhauling and adjusting an abrasion part in the coal mill. The invention has the beneficial effects that: the pebble coal is quantitatively measured, and external monitoring data of the internal wear state of the coal mill can be obtained; the discharge amount of the pebble coal is monitored in real time, the proper discharge rate of the pebble coal can be controlled, the increase of power consumption for powder preparation caused by too little discharge of the pebble coal is prevented, the abrasion of a coal mill is aggravated, and the resource waste caused by too much discharge of the pebble coal is prevented.

Description

MSET-based method for monitoring abrasion state of easily-abraded part of coal mill of intelligent power plant
Technical Field
The invention belongs to the field of safe and economic operation of coal-fired power plant coal mills, and particularly relates to an intelligent monitoring method for the abrasion state of an easily-abraded part in an intelligent power plant coal mill based on MSET.
Background
The pebble coal quantity of the medium-speed coal mill can reflect the wearing degree of easily worn parts in the coal mill, such as a grinding roller, a grinding bowl lining plate and a wind ring dynamic and static gap, so that the pebble coal quantity is a very important monitoring parameter, however, the existing easily worn parts lack an effective monitoring method, and the power plant only accumulates to a certain height at the pebble coal bucket and triggers the alarm of the high switching value of the material level to remind an operator to discharge the pebble coal. The abrasion degree of the easily-abraded part can be obtained by internal measurement after the coal mill is stopped, the quantity of pebble coal only depends on the experience of operators, and although the abrasion degree of the easily-abraded part in the coal mill can be roughly judged, the fine management requirement of maintenance of the coal mill cannot be met. Quantitative measurement of pebble coal can obtain externally monitored data of the internal wear state of the coal mill.
At present, the control of the discharge rate of the pebble coal tends to be reduced, the discharge rate of the pebble coal is low, the pebble coal can be repeatedly milled, the power consumption of a coal mill is increased, the abrasion is aggravated, and meanwhile, the pebble coal with a low calorific value is conveyed into a hearth to be combusted more favorably. On the other hand, the large discharge rate of the pebble coal can cause coal particles to be entrained, and the coal consumption is increased.
The most fundamental index for judging the state of the equipment by the Multivariate State Estimation Technology (MSET) is the residual error between an observation vector and an estimation vector. When the working state of the equipment changes and the hidden trouble occurs, the input observation vector deviates from the normal working space, and the residual error between the observed value and the estimated value is increased. The wearing and tearing degree aggravation in wearing and tearing part grinding roller, grinding bowl welt, wind ring sound clearance in the coal pulverizer, the clearance increase, the increase of stone coal emission, skew normal operating condition, the state monitoring model of the easy wearing and tearing of coal pulverizer based on many first state estimation techniques can carry out real-time supervision, real-time early warning.
Therefore, it is very important to provide a method for monitoring the wear state of the easily-worn part of the coal mill of the intelligent power plant based on the MSET.
Disclosure of Invention
The invention aims to provide a method for monitoring the abrasion state of an easily-abraded part of a coal mill in an intelligent power plant based on MSET (modeling, simulation and development) aiming at the current situation that the easily-abraded part of the coal mill is difficult to monitor on line, and the problem that the easily-abraded part of the coal mill is difficult to monitor on line is solved.
The method for monitoring the abrasion state of the easily-abraded part of the intelligent power plant coal mill based on the MSET comprises the following steps:
step 1, acquiring historical data of measuring points capable of reflecting the abrasion state of an easily-abraded part of a coal mill under different working conditions;
step 2, establishing a coal mill wear state monitoring and early warning model by using a multivariate state estimation technology, wherein the established model is mainly used for monitoring the wear state of a wear-prone part of the coal mill;
step 3, inputting DCS real-time operation data into a coal mill wear state monitoring and early warning model, after other interference factors are eliminated, enabling the discharge rate of pebble coal of the coal mill to reflect the state of an easily worn part in the coal mill in real time under a certain condition, evaluating the wear gap state of the coal mill according to the discharge rate of the pebble coal of the coal mill, sending out corresponding early warning, and overhauling and adjusting the worn part in the coal mill;
step 3.1, determining the discharge rate of pebble coal of the coal mill as a monitoring index;
3.2, when the emission rate of pebble coal of the coal mill in the step 3.1 exceeds a set range, firstly, detecting whether the coal type corresponding to the characteristic parameter of the coal type is the coal type corresponding to the modeling parameter of the monitoring and early warning model of the wear state of the coal mill by the monitoring and early warning model of the wear state of the coal mill; if the characteristic parameters of the coal types are changed greatly, the coal mill wear state monitoring and early warning model gives out a prompt that the coal type parameter difference is large; if the detected coal type is the coal type corresponding to the modeling parameter in the coal mill wear state monitoring and early warning model, an early warning that the gap between a grinding bowl of the grinding roller is too large or the gap between a wind ring and a static ring is too large is sent out;
step 3.3, a maintainer inquires a grinding roller and grinding bowl gap adjustment record, and if the early warning sent by the coal mill wear state monitoring early warning model is caused by that the grinding roller and grinding bowl gap is not adjusted in time, the grinding roller and grinding bowl gap is adjusted; and if the gap between the grinding bowl and the grinding roller is regulated according to the standard, the worn parts in the coal grinding machine are overhauled.
Preferably, step 1 specifically comprises the following steps:
step 1.1, selecting a modeling parameter from monitoring parameters of a coal mill; or expanding the dimension of the monitoring parameters of the coal mill to obtain modeling parameters; the monitoring parameters of the coal mill comprise parameters influencing the pebble coal emission rate, coal characteristic parameters and characteristic parameters of a grinding bowl gap and a wind ring dynamic and static gap (namely the state of an easily-worn part in the coal mill);
and step 1.2, calculating partial modeling parameters (coal amount accumulated in a coal mill hour, pebble coal emission rate increase rate, coal mill wind ring wind speed and coal mill inlet wind average density) to serve as historical data of measuring points of the abrasion state of the easily-abraded part of the coal mill.
Preferably, step 2 specifically comprises the following steps:
step 2.1, acquiring sufficient normal operation condition data (generally taking 2 years) according to the historical data of the measuring points of the wear state of the easy-to-wear part of the coal mill obtained in the step 1: the new coal mill and the coal mill after major repair are adopted, the grinding roller and the grinding bowl lining plate in the coal mill are in a brand new state, the clearance between the grinding roller and the grinding bowl is normal (smaller), and the dynamic and static clearance of the air ring is smaller; taking data within a set time length when the coal mill normally operates as training data of a coal mill wear state monitoring and early warning model; obtaining a data matrix taking time as a sequence as a model training parameter;
2.2, performing data screening, data cleaning, training and testing on the model training parameters by using a multivariate state estimation technology to obtain an operation condition data set at the initial stage of abrasion of a grinding roller, a grinding bowl lining plate and a wind ring dynamic and static gap in the coal mill;
and 2.3, establishing a coal mill wear state monitoring and early warning model.
Preferably, in step 1.1: parameters influencing the pebble coal emission rate comprise the coal flow of a coal feeder, the air speed of an air ring of a coal mill, the rotating speed of a rotating separator, the coal type, the loading force of a grinding roller and the gap between grinding bowls of the grinding roller; the coal characteristic parameters comprise a coal Hardgrove grindability index, coal ash content, coal moisture and coal mill current; the characteristic parameters of the grinding roller and bowl gap and the air ring dynamic and static gap comprise the discharge amount of pebble coal, the discharge rate of the pebble coal and the discharge growth rate of the pebble coal.
Preferably, in step 1.1, the loading force of the grinding roller is not generally adjusted during the operation of the coal mill, and the selected modeling parameters include: the coal type Hardgrove grindability index, the coal type ash content, the coal type moisture content, the coal mill current, the coal flow of a coal feeder, the air speed of an air ring of the coal mill, the rotating speed of a rotary separator, the discharge amount of pebble coal, the discharge rate of the pebble coal and the discharge growth rate of the pebble coal.
Preferably, step 1.2 is specifically:
calculating the hour accumulated coal amount of the coal mill:
Figure BDA0003343374390000031
in the above formula, q is the coal mass flow of the coal feeder, and the unit is ton/h; t represents time in hours;
calculating the pebble coal emission rate:
Figure BDA0003343374390000032
in the above formula, M (t) is the cumulative weight of the pebble coal of the coal mill in tons/h; qc is the coal amount accumulated by the coal mill in hours;
calculating the increasing rate of the pebble coal emission rate:
Figure BDA0003343374390000033
in the above formula, sp1 represents the pebble coal emission rate when the wear loss is small at the initial stage of the operation of the coal mill; sp2 represents the pebble coal emission rate when the abrasion loss is large after the coal mill is operated for a period of time;
calculating the wind speed v of the wind ring of the coal milla：
Figure BDA0003343374390000034
Figure BDA0003343374390000041
In the above formula, vaFor the wind velocity of the wind ring of the coal mill, QaThe inlet air quantity of the coal mill is rhoaThe average density of the inlet air of the coal mill is expressed in kg/m3；taThe average temperature of air at the inlet of a coal mill is measured in units of ℃; p is a radical ofeThe average static pressure of air at an inlet of a coal mill is Pa; assuming the area of the wind ring is A, the unit is m2(ii) a Assuming that the area of the dynamic and static gaps of the wind ring is S, the unit is m2。
Preferably, the cumulative weight m (t) of the coal pulverizer pebble coal in hours is obtained by: the weighing and metering device for the pebble coal of the coal mill is fixedly arranged on a ground plane below a pebble coal operation box of the coal mill, the weighing and metering device for the pebble coal has the function of accumulative metering of the weight of the pebble coal, a microcomputer device is used for displaying the real-time weight m and the hour accumulated weight M (t) of the pebble coal in the current operation box of the pebble coal of the coal mill, and the microcomputer device also has the function of inquiry.
Preferably, in the step 2.1, because the designed service life of the HP medium-speed grinding roller and the grinding bowl lining plate part is 15000h, the previous 1500h data of the normal operation of the coal mill is taken as the training data of the coal mill wear state monitoring and early warning model.
Preferably, in step 3.1: and when the discharge rate of the pebble coal of the coal mill is less than 5 per thousand, the discharge rate of the pebble coal of the coal mill is in a set range.
Preferably, the abrasion parts in the coal mill in the step 3.3 comprise a grinding roller, a grinding bowl, a wind ring and a side machine body.
The invention has the beneficial effects that:
the invention carries out quantitative measurement on the pebble coal and can obtain the external monitoring data of the internal wear state of the coal mill; the discharge amount of the pebble coal is monitored in real time, the proper discharge rate of the pebble coal can be controlled, the increase of power consumption for powder preparation caused by too little discharge of the pebble coal is prevented, the abrasion of a coal mill is aggravated, and the resource waste caused by too much discharge of the pebble coal is prevented.
The invention discloses a model for monitoring the abrasion state of an easily-abraded part of a coal mill based on the pebble coal amount, which can monitor the abrasion state of the easily-abraded part of the coal mill in real time, can find abnormal abrasion working conditions in time, is favorable for adjusting the gap of a grinding bowl in time, improves the economical efficiency of the operation of the coal mill, and is suitable for the fine management of the operation and maintenance of coal mill equipment.
Drawings
FIG. 1 is a flow chart of a method for monitoring the abrasion state of an easily-abraded part of a coal mill of an intelligent power plant based on MSET.
Detailed Description
The present invention will be further described with reference to the following examples. The following examples are set forth merely to aid in the understanding of the invention. It should be noted that, for a person skilled in the art, several modifications can be made to the invention without departing from the principle of the invention, and these modifications and modifications also fall within the protection scope of the claims of the present invention.
Example one
The embodiment of the application provides a method for monitoring the wear state of an easily-worn part of a coal mill of an intelligent power plant based on MSET (modeling, simulation and engineering) as shown in figure 1:
step 1, acquiring historical data of measuring points of the abrasion state of an easily-abraded part of a coal mill under different working conditions;
step 1.1, selecting modeling parameters from monitoring parameters of an HP type medium-speed coal mill in a table 1; or expanding the dimension of the monitoring parameters of the coal mill to obtain modeling parameters; the monitoring parameters of the coal mill comprise parameters influencing the pebble coal emission rate, coal characteristic parameters and characteristic parameters of a grinding bowl gap and a wind ring dynamic and static gap of the grinding roller; the modeling parameters are shown in table 2;
TABLE 1 HP type Medium speed coal pulverizer main monitoring parameter table
Parameter name Unit of Parameter name Unit of
Current of coal mill A Upper and lower differential pressure of grinding bowl kPa
Inlet air quantity of coal mill t/h Differential pressure between sealing air and lower part of grinding bowl kPa
Inlet air pressure of coal mill kPa Coal quantity of coal feeder t/h
Inlet temperature of coal mill ℃ Speed of rotating separator r/min
Coal mill outlet pressure kPa Bearing temperature of input shaft of gear box of coal mill ℃
Coal mill outlet temperature ℃ Coal mill gearbox thrust pad bearing temperature ℃
TABLE 2 coal pulverizer abrasion State monitoring model parameter Table
Serial number Parameter name Unit of Remarks for note
1 Hardgrove grindability index HGI of coal / Coal type parameter
2 Ash content A of coalar ％ Coal type parameter
3 Moisture M of coal ％ Coal type parameter
4 Coal mill current I A Coal type parameter
5 Coal flow q of coal feeder t/h
6 Rotational speed n of the rotating separator r/min
7 Wind velocity v of wind ringa m/s
8 Discharge amount of pebble coal M (t) t/h
9 Pebble coal emission rate sp ％
10 Pebble coal emission rate increase rate x ％
Step 1.2, calculating partial modeling parameters to serve as measuring point historical data of the abrasion state of the easily-abraded part of the coal mill;
calculating the hour accumulated coal amount of the coal mill:
Figure BDA0003343374390000061
in the above formula, q is the coal mass flow of the coal feeder, and the unit is ton/h; t represents time in hours;
calculating the pebble coal emission rate:
Figure BDA0003343374390000062
in the above formula, M (t) is the cumulative weight of the pebble coal of the coal mill in tons/h; qcAccumulating the coal amount for the coal mill hour;
calculating the increasing rate of the pebble coal emission rate:
Figure BDA0003343374390000063
in the above formula, Sp1 represents the pebble coal emission rate when the abrasion loss is small at the initial operation stage of the coal mill; sp2 represents the pebble coal emission rate when the abrasion loss is large after the coal mill is operated for a period of time;
calculating the wind speed v of the wind ring of the coal milla：
Figure BDA0003343374390000064
Figure BDA0003343374390000065
In the above formula, vaFor the wind velocity of the wind ring of the coal mill, QaThe inlet air quantity of the coal mill is rhoaThe average density of the inlet air of the coal mill is expressed in kg/m3；taThe average temperature of air at the inlet of a coal mill is measured in units of ℃; p is a radical ofeThe average static pressure of air at an inlet of a coal mill is Pa; assuming the area of the wind ring is A, the unit is m2(ii) a Assuming that the area of the dynamic and static gaps of the wind ring is S, the unit is m2；
Step 2, establishing a coal mill wear state monitoring and early warning model by using a multivariate state estimation technology;
step 2.1, acquiring sufficient normal operation condition data according to the measuring point historical data of the wear state of the easy-wear part of the coal mill acquired in the step 1: taking data within a set time length when the coal mill normally operates as training data of a coal mill wear state monitoring and early warning model; in the step 1, parameters 1-10 of the coal mill wear state monitoring model in the step 2 are both model input parameters and model output parameters, and both participate in model training, wherein parameter deviation early warning is not output when the parameters 1-7 deviate from a threshold range, and parameter deviation early warning is output when the parameters 8-10 deviate from the threshold range. When the operating condition data of the coal mill is collected, a group of data is taken every 30min, namely 10 data are taken every 30min, and a data matrix taking time as a sequence is obtained and is used as a model training parameter;
the method for screening model training data, namely the normal (small) working condition data of the gaps between the easily worn parts of the coal mill, comprises the following steps: the new coal mill and the coal mill after overhaul have the advantages that the grinding roller and the grinding bowl lining plate in the coal mill are in a brand new state, the clearance between the grinding roller and the grinding bowl is normal (smaller), and the dynamic and static clearance of the air ring is smaller. As the design life of the grinding roller and the grinding bowl lining plate of the HP medium-speed grinding mill is 15000h, the previous 1500h data of the normal running of the coal mill is taken as the training data of the wear state monitoring and early warning model of the coal mill.
2.2, performing data screening, data cleaning, training and testing on the model training parameters by using a multivariate state estimation technology to obtain an operation condition data set at the initial stage of abrasion of a grinding roller, a grinding bowl lining plate and a wind ring dynamic and static gap in the coal mill;
step 2.3, establishing a coal mill wear state monitoring and early warning model;
step 3, inputting DCS real-time operation data into a coal mill abrasion state monitoring and early warning model, evaluating the abrasion gap state of the coal mill according to the discharge rate of pebble coal of the coal mill, sending out corresponding early warning, and overhauling and adjusting an abrasion part in the coal mill;
step 3.1, determining the discharge rate of pebble coal of the coal mill as a monitoring index; according to the standard of DL/T467-plus 2019 power station coal mill and powder making system performance test, the discharge amount of pebble coal of the coal mill is not more than 5 per thousand of rated output within the normal output working condition range. When the equipment condition and the operation working condition of the coal mill are normal, the discharge rate of the pebble coal is less than 5 per thousand, so that the discharge rate of the pebble coal is not more than 5 per thousand and can be used as a monitoring index.
3.2, when the emission rate of pebble coal of the coal mill in the step 3.1 exceeds a set range, firstly, detecting whether the coal type corresponding to the characteristic parameter of the coal type is the coal type corresponding to the modeling parameter of the monitoring and early warning model of the wear state of the coal mill by the monitoring and early warning model of the wear state of the coal mill; if the characteristic parameters of the coal types are changed greatly, the coal mill wear state monitoring and early warning model gives out a prompt that the coal type parameter difference is large; if the detected coal type is the coal type corresponding to the modeling parameter in the coal mill wear state monitoring and early warning model, an early warning that the gap between a grinding bowl of the grinding roller is too large or the gap between a wind ring and a static ring is too large is sent out;
step 3.3, a maintainer inquires a gap adjustment record of a grinding roller and a grinding bowl, and if the early warning sent by the coal mill wear state monitoring early warning model is caused by the fact that the gap of the grinding roller and the grinding bowl is not adjusted, the gap of the grinding roller and the grinding bowl is adjusted; and if the gap between the grinding bowl and the grinding roller is regulated according to the standard, the worn parts in the coal grinding machine are overhauled.
Example two
On the basis of the first embodiment, the second embodiment of the present application provides an application of the method for monitoring the wear state of the wear-prone member of the coal mill of the intelligent power plant based on the MSET in the first embodiment in an actual situation:
the utility model provides a model HP1203 coal pulverizer, fixes the pebble coal weighing and metering device in the ground plane under the coal pulverizer pebble coal operation case, and the pebble coal weighing and metering device has the cumulative metering function of pebble coal weight, realizes the display of the real-time weight m of pebble coal in the current operation case and the display of hour cumulative weight M (t) and relevant inquiry function through microcomputer device.
The method for monitoring the wear state of the easily worn part of the coal mill based on the pebble coal amount comprises the following steps:
(a) firstly, an HP1203 coal mill with a grinding roller, a grinding bowl lining plate, an air ring and a side machine body in a brand-new state is adjusted to a certain common coal type, a pulverizing system is put into an automatic mode, the primary air volume offset of an inlet of the coal mill is set to be 0, the coal feeding amount of the coal mill is kept at 45-75 t/h, and all operating parameters of the previous 300h in the operating state are obtained and used for training a multivariate state estimation model to obtain relevant parameters in the model, wherein the relevant parameters are shown in a table 2. Wherein, the Hardgrove grindability index, ash content, moisture content and coal mill current of the coal are taken as the characteristic parameters of the coal, the pebble coal discharge rate and the pebble coal discharge growth rate are taken as the characteristic parameters of the grinding bowl gap and the air ring dynamic and static gap of the grinding roller, the coal flow q of the coal feeder, the rotating speed n of the rotary separator and the air velocity v of the air ringaIs the relevant state parameter.
It should be noted that although the present invention employs a multivariate state estimation technique prediction model, the present invention is not limited to this algorithm, and neural networks, deep learning, etc. models can also be used as mathematical tools for establishing a wear state prediction model of a wear member in a coal mill.
(b) As shown in fig. 1, after the model training is completed, the model can be used to predict the state of the easily worn parts in the coal mill according to the coal mill operation parameters and the pebble coal discharge rate, so as to provide a basis for coal mill maintenance, and the aim is to control the pebble coal discharge rate of the coal mill to be less than 5 per thousand under the state of inputting appropriate air volume and outputting qualified coal powder fineness. When the emission rate of pebble coal of the coal mill is more than 5 per thousand, the coal mill wear state monitoring and early warning model firstly detects whether the coal type parameter is the coal type in the model, if the coal type parameter is changed greatly, the model sends out a prompt that the coal type parameter difference is large, and if the coal type is detected to be the coal type in the model, an early warning that the gap between a grinding roller and a grinding bowl is too large or the gap between a wind ring and a moving ring is too large is sent out. The maintainer adjusts the record by inquiring the gap of the grinding roller and the grinding bowl, if the early warning is caused by the fact that the gap of the grinding roller and the grinding bowl is not adjusted in time, the gap of the grinding roller and the grinding bowl is adjusted, and if the gap of the grinding roller and the grinding bowl is adjusted according to the standard, the wear parts (the grinding roller, the grinding bowl, the air ring and the side machine body) in the coal grinding machine are overhauled.
When the coal type parameters are changed greatly, maintenance personnel need to adjust the loading force of the grinding roller in time, model training of new coal types is carried out by using the model training method (a), and a state monitoring model of a quick-wear part in the coal grinding machine under the condition of the new coal types is established.

Claims (10)

1. The method for monitoring the abrasion state of the easily-abraded part of the coal mill of the intelligent power plant based on the MSET is characterized by comprising the following steps of:
step 1, acquiring historical data of measuring points of the abrasion state of an easily-abraded part of a coal mill under different working conditions;
step 2, establishing a coal mill wear state monitoring and early warning model by using a multivariate state estimation technology;
step 3, inputting DCS real-time operation data into a coal mill abrasion state monitoring and early warning model, evaluating the abrasion gap state of the coal mill according to the discharge rate of pebble coal of the coal mill, sending out corresponding early warning, and overhauling and adjusting an abrasion part in the coal mill;
step 3.1, determining the discharge rate of pebble coal of the coal mill as a monitoring index;
3.2, when the emission rate of pebble coal of the coal mill in the step 3.1 exceeds a set range, firstly, detecting whether the coal type corresponding to the characteristic parameter of the coal type is the coal type corresponding to the modeling parameter of the monitoring and early warning model of the wear state of the coal mill by the monitoring and early warning model of the wear state of the coal mill; if the characteristic parameters of the coal types are changed greatly, the coal mill wear state monitoring and early warning model gives out a prompt that the coal type parameter difference is large; if the detected coal type is the coal type corresponding to the modeling parameter in the coal mill wear state monitoring and early warning model, an early warning that the gap between a grinding bowl of the grinding roller is too large or the gap between a wind ring and a static ring is too large is sent out;
step 3.3, a maintainer inquires a gap adjustment record of a grinding roller and a grinding bowl, and if the early warning sent by the coal mill wear state monitoring early warning model is caused by the fact that the gap of the grinding roller and the grinding bowl is not adjusted, the gap of the grinding roller and the grinding bowl is adjusted; and if the gap between the grinding bowl and the grinding roller is regulated according to the standard, the worn parts in the coal grinding machine are overhauled.
2. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method as claimed in claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1, selecting a modeling parameter from monitoring parameters of a coal mill; or expanding the dimension of the monitoring parameters of the coal mill to obtain modeling parameters; the monitoring parameters of the coal mill comprise parameters influencing the pebble coal emission rate, coal characteristic parameters and characteristic parameters of a grinding bowl gap and a wind ring dynamic and static gap of the grinding roller;
and step 1.2, calculating partial modeling parameters to serve as historical data of measuring points of the abrasion state of the easily abraded part of the coal mill.
3. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method as claimed in claim 1, wherein the step 2 specifically comprises the following steps:
step 2.1, acquiring sufficient normal operation condition data according to the measuring point historical data of the wear state of the easy-wear part of the coal mill acquired in the step 1: taking data within a set time length when the coal mill normally operates as training data of a coal mill wear state monitoring and early warning model; obtaining a data matrix taking time as a sequence as a model training parameter;
2.2, performing data screening, data cleaning, training and testing on the model training parameters by using a multivariate state estimation technology to obtain an operation condition data set at the initial stage of abrasion of a grinding roller, a grinding bowl lining plate and a wind ring dynamic and static gap in the coal mill;
and 2.3, establishing a coal mill wear state monitoring and early warning model.
4. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method according to claim 2, characterized in that in step 1.1: parameters influencing the pebble coal emission rate comprise the coal flow of a coal feeder, the air speed of an air ring of a coal mill, the rotating speed of a rotating separator, the coal type, the loading force of a grinding roller and the gap between grinding bowls of the grinding roller; the coal characteristic parameters comprise a coal Hardgrove grindability index, coal ash content, coal moisture and coal mill current; the characteristic parameters of the grinding roller and bowl gap and the air ring dynamic and static gap comprise the discharge amount of pebble coal, the discharge rate of the pebble coal and the discharge growth rate of the pebble coal.
5. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method as claimed in claim 4, wherein the modeling parameters selected in step 1.1 include: the coal type Hardgrove grindability index, the coal type ash content, the coal type moisture content, the coal mill current, the coal flow of a coal feeder, the air speed of an air ring of the coal mill, the rotating speed of a rotary separator, the discharge amount of pebble coal, the discharge rate of the pebble coal and the discharge growth rate of the pebble coal.
6. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method according to claim 2, characterized in that step 1.2 specifically comprises:
calculating the hour accumulated coal amount of the coal mill:
Figure FDA0003343374380000021
in the above formula, q is the coal mass flow of the coal feeder, and the unit is ton/h; t represents time in hours;
calculating the pebble coal emission rate:
Figure FDA0003343374380000022
in the above formula, M (t) is the cumulative weight of the pebble coal of the coal mill in tons/h; qcAccumulating the coal amount for the coal mill hour;
calculating the increasing rate of the pebble coal emission rate:
Figure FDA0003343374380000023
in the above formula, sp1 represents the pebble coal emission rate when the wear loss is small at the initial stage of the operation of the coal mill; sp2 represents the pebble coal emission rate when the abrasion loss is large after the coal mill is operated for a period of time;
calculating the wind speed v of the wind ring of the coal milla：
Figure FDA0003343374380000024
Figure FDA0003343374380000031
In the above formula, vaFor the wind velocity of the wind ring of the coal mill, QaIs the inlet air quantity of the coal mill, and rho a is the average density of the inlet air of the coal mill, and the unit is kg/m3；taThe average temperature of air at the inlet of a coal mill is measured in units of ℃; pe is the average static pressure of air at the inlet of the coal mill and has a unit of Pa; assuming the area of the wind ring is A, the unit is m2(ii) a Assuming that the area of the dynamic and static gaps of the wind ring is S, the unit is m2。
7. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method as claimed in claim 6, wherein the coal mill pebble coal hour accumulated weight M (t) is obtained by: the weighing and metering device for the pebble coal of the coal mill is fixedly arranged on a ground plane below a pebble coal operation box of the coal mill, the weighing and metering device for the pebble coal has the function of accumulative metering of the weight of the pebble coal, a microcomputer device is used for displaying the real-time weight m and the hour accumulated weight M (t) of the pebble coal in the current operation box of the pebble coal of the coal mill, and the microcomputer device also has the function of inquiry.
8. The MSET-based intelligent power plant coal mill wear state monitoring method is characterized in that data of 1500h before normal operation of a coal mill in step 2.1 is taken as training data of a coal mill wear state monitoring and early warning model.
9. The MSET-based intelligent power plant coal mill wearing part wear state monitoring method according to claim 1, characterized in that in step 3.1: and when the discharge rate of the pebble coal of the coal mill is less than 5 per thousand, the discharge rate of the pebble coal of the coal mill is in a set range.
10. The MSET-based intelligent power plant coal mill wear state monitoring method of claim 1, wherein in step 3.3, the wear parts in the coal mill comprise a grinding roller, a grinding bowl, a wind ring and a side body.
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BR0200055A (en) * 2001-01-25 2002-10-29 Abon Engineering Pty Ltd Material crushing equipment
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CN103406191A (en) * 2013-07-19 2013-11-27 国家电网公司 HP type medium-speed coal mill, and method for reducing pebble coal discharge amount of HP type medium-speed coal mill
CN112604776A (en) * 2020-11-30 2021-04-06 大唐黄岛发电有限责任公司 Medium-speed roller type coal mill overhaul early warning device and method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
BR0200055A (en) * 2001-01-25 2002-10-29 Abon Engineering Pty Ltd Material crushing equipment
CN201534101U (en) * 2009-10-28 2010-07-28 石福军 Bowel-type medium speed coal mill
CN101947484A (en) * 2010-09-10 2011-01-19 浙江省电力试验研究院 Method for reducing discharge capacity of cobble coal of intermediate-speed coal mill
CN103406191A (en) * 2013-07-19 2013-11-27 国家电网公司 HP type medium-speed coal mill, and method for reducing pebble coal discharge amount of HP type medium-speed coal mill
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