CN114707334B - Operation method of twin model of coal mill - Google Patents
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Abstract
A coal mill twin model operation method is based on the working principle and the heat balance relation of a double-inlet double-outlet steel ball coal mill, and combines expert experience to create a coal mill twin model; simulating the operation of a double-inlet and double-outlet steel ball coal mill in an actual power plant unit by utilizing a twin DCS; the transmission direction of the unit operation data is that the coal-fired unit DCS transmits the unit operation data to the big data server in a one-way, and then the big data server transmits the unit operation data to the twin DCS, and the coal-fired unit operation data is led into a coal mill twin model through the twin DCS; calculating the predicted value of the outlet temperature of the future double-inlet double-outlet steel ball coal mill, the pressure difference of the current material level height of the reaction coal mill and the outlet wind speed of the double-inlet double-outlet steel ball coal mill by utilizing real-time outlet temperature of the coal mill and various measurable parameters and by means of a twin model of the coal mill according to the internal heat relation of the coal mill; and returning the calculation result to the twin DCS to realize the data interconnection of the twin model of the coal mill and the twin DCS. And establishing an early warning mechanism of the safety of the coal-fired unit.
Description
Technical Field
The invention relates to the technical field of digital twin modeling, in particular to a coal mill twin model operation method for completing data interconnection with a real unit.
Background
The double-inlet and double-outlet coal mill has high continuous operation rate; the coal type application range is wide; the load response is fast; the powder storage capacity is strong; stable output and fineness of coal powder, low ratio of wind to coal, etc. In addition, the coal pulverizing system of the double-inlet double-outlet coal mill has the characteristics of nonlinearity, strong coupling, large time lag and the like, can not continuously and stably input automatic control, and has the influence on the operation safety and stability. Therefore, mathematical modeling is performed on the coal pulverizing system of the double-inlet double-outlet coal pulverizer, dynamic characteristics of the coal pulverizing system are revealed on a mechanism level, a good mathematical foundation is laid for control of the coal pulverizing system, and particularly, a high-precision model which takes real-time data or historical data as a drive and can complete interaction between the model and actual running data of a unit is built.
The double-inlet double-outlet coal mill is characterized in that coal is fed by a nearly completely symmetrical driving end and a non-driving end simultaneously, and the separator discharges powder, so that the double-inlet double-outlet coal mill is provided with two symmetrical and independent coal grinding loops. The barrel of the coal mill is internally provided with a certain amount of steel balls for impacting and grinding raw coal. The steel balls are lifted to a certain height along with the rotation of the coal mill, and then are freely thrown down to impact raw coal, so that the raw coal is crushed into coal particles; in addition, the coal between the steel balls and the cylinder liner plate can be extruded and ground to be finally ground into coal dust. The outlet temperature of the coal mill is used as an important monitoring quantity in the operation of the coal mill and must be maintained in a reasonable range, the combustion efficiency of a boiler is affected by too low temperature, and the problems of coal dust explosion and the like are easily caused by too high temperature. Whether the coal mill has proper coal quantity or not is an important monitoring parameter for safe operation of the coal mill, but the material level is difficult to directly measure, so that a high-precision twin model needs to be established to complete the monitoring function. The influence factors of the coal mill output are numerous and are difficult to reflect through a simple mechanism model, so that the model and actual operation data are connected through data communication, and the method has important significance for improving the model precision. The three key parameters have strong guiding functions for fault early warning, abnormal monitoring and replay of the operation of the coal mill, improve model precision through connection with big data, carry out data statistics analysis, reveal dynamic characteristics of the coal mill on a mechanism level, strengthen automatic control and play an important role in realizing stable operation of a power plant and the coal mill.
According to the international unified definition, the digital twin is a simulation process integrating multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities by fully utilizing data such as a physical model, sensor update and operation history. Digital twinning is the digitized representation of a particular physical entity or process with a data connection that can guarantee the same rate convergence between physical and virtual states and provide an integrated view of the entire lifecycle of the physical entity or process, helping to optimize overall performance. Digital twinning has the following typical characteristics: (1) Interoperability, physical objects and digital space in digital twinning can be mapped bi-directionally, interacted dynamically, and connection implemented. (2) The digital twin technology has the capability of integrating, adding and replacing digital models, and can expand the model contents of multiple scales, multiple physics and multiple layers. (3) In real-time, digital twinning requires digitization, i.e., managing data in a computer-recognizable and processable manner to characterize physical entities that vary over time. (4) Fidelity, digital twin fidelity refers to the proximity of describing a digital virtual body model and a physical entity. The virtual body and the entity are required to not only maintain a high degree of simulation of geometry, but also in state, phase and temporal. (5) The digital virtual body in digital twin is used for describing a visual model and an internal mechanism of a physical entity so as to monitor, analyze and infer state data of the physical entity, optimize technological parameters and operation parameters and realize a decision function, namely endowing the digital virtual body and the physical entity with a brain.
At present, the traditional coal mill research focuses on monitoring and controlling important parameters, but no intensive research exists on the change rule of the key parameters. The simulation of parameters is mostly focused on mechanism modeling, offline simulation, but cannot interact with actual operation data, and derailment is realized with actual operation of a power plant. In fact, the operation of the coal mill depends in part on the working conditions and equipment conditions in the field, so that historical data and real-time data have great guiding significance for the change of key parameters of the coal mill. The communication of real-time data of the twin model of the coal mill and the running state of the coal mill is completed, and the method has important effects of improving the model precision, perfecting the monitoring method and developing the early warning mechanism. Is also a key step for realizing digitization of a power plant.
Disclosure of Invention
Based on the background, in order to realize stable input automatic control of a coal mill pulverizing system and complete early warning and replay of faults and anomalies in advance, the invention provides a coal mill twin model operation method aiming at the technical problem that the traditional simulation cannot be connected with real-time data.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a coal mill twin model operation method comprises the following steps:
step 1: based on the working principle of the double-inlet double-outlet steel ball coal mill and the heat balance relation in the double-inlet double-outlet steel ball coal mill, combining expert experience, creating a coal mill twin model, wherein the coal mill twin model comprises three modules, namely a coal mill outlet temperature twin model, a coal mill material level twin model and a coal mill output twin model;
wherein, coal pulverizer outlet temperature twin model is as follows:
wherein: t is t out : current coal mill outlet temperature, B gm : single end coal feed, δw: variation of raw coal moisture mass fraction, t, in coal grinding process g : the coal feeding temperature, c gq : specific heat capacity of steel ball M gq : steel ball loading, c m : specific heat capacity of pulverized coal M m : amount of stored coal, K bs : model identification parameters;
the coal mill material level twin model is established as follows:
the material level modeling adopts differential pressure to indirectly reflect the height of the coal level, and the relation between the differential pressure coal level and the height coal level is as follows:
δP＝k*H m *(5+G f 2 )
wherein: h m : current level height of coal mill, μ: coal mill wind-coal ratio, G r : hot air volume, G l : cold air volume, G mf : sealed air volume, L: length ρ of coal mill barrel m : the coal density of the coal mill is R, the inner radius of the cylinder body, delta P: differential pressure coal level, k: differential pressure coefficient of coal mill, G f : primary air inlet flow;
the coal mill output twin model is established as follows:
when G f >5P i >1000
When G f >5P i <1000
Wherein: v (V) f : outlet wind speed, K f : conversion coefficient, P f : hearth pressure, P i : primary air pressure;
step 2: simulating the operation of a double-inlet and double-outlet steel ball coal mill in an actual power plant unit by utilizing a twin system of the coal-fired unit DCS, namely the twin DCS; the transmission direction of the unit operation data is that the coal-fired unit DCS transmits the unit operation data to the big data server in a one-way, and then the big data server transmits the unit operation data to the twin DCS, and the coal-fired unit operation data is led into a coal mill twin model through the twin DCS;
step 3: the outlet temperature of the real-time double-inlet double-outlet steel ball coal mill and various measurable parameters are utilized, and the predicted value t of the outlet temperature of the future double-inlet double-outlet steel ball coal mill is predicted by the twin model of the coal mill in step 1 through the internal heat relation of the coal mill f Differential pressure delta P of current material level height of reaction coal mill and outlet wind speed V of double-inlet double-outlet steel ball coal mill f Calculating the real-time working condition to realize the prediction function of the twin model of the coal mill;
the calculation process comprises the following steps:
heat of moisture absorption during coal grinding: q (Q) w ＝B gm *δw*(2491+1.884*t out -4.19t g )
Heat generated in the coal grinding process: q (Q) j ＝41.57*B gm
Coal mill air and coal input heat: q (Q) in ＝B gm *c m *t g +G f *c f *t f +Q j
Wherein: g f : primary air flow, c f : specific heat capacity of primary air, t f : primary air temperature;
output heat of coal mill air and coal:
Q out ＝B m *c m *t out +(G f +B gm *δw)*c f *t out +Q w
wherein: b (B) m The output of the coal mill;
internal energy relationship of coal mill:
energy change, unit conversion:
wherein: dt (dt) out : rate of change of outlet temperature of coal mill, M cm : coal mill coal storage quality c gm : specific heat capacity of coal;
predicting outlet temperature of a coal mill: t is t f ＝t out +T*dt out
t f Predicted value T of outlet temperature of coal mill: time interval
Calculating the pressure difference delta P of the current material level height of the reaction coal mill through a coal mill material level twin model; calculating the outlet wind speed V through a coal mill output twin model f ；
Step 4: and 3, predicting the outlet temperature t of the future double-inlet and double-outlet steel ball coal mill calculated in the step 3 f Differential pressure δP and outlet wind speed V f And returning the result to the twin DCS to realize the data interconnection of the twin model of the coal mill and the twin DCS.
The invention has the beneficial effects that:
the invention establishes a twin model operation method of the coal mill, which is interacted with the data of the coal-fired unit, and because the construction of the twin model is based on a mechanism, the simulation precision is greatly improved compared with the traditional simulator by combining a mathematical method, and the high-precision simulation of the real unit can be achieved. And for the simulation prediction of the outlet temperature, the material level and the output of the coal mill, a pre-warning mechanism of the safety of the coal-fired unit can be established.
Drawings
FIG. 1 is a flow chart of real-time data transmission under an OPC communication architecture.
FIG. 2 is a comparison of a time-varying curve obtained by performing simulation prediction on the outlet temperature of the coal mill by the twin model under the real-time working condition of the coal-fired unit and an actual outlet temperature curve of the coal mill.
FIG. 3 is a comparison of a time-varying curve obtained by simulated prediction of the coal mill level differential pressure by the twinning model under real-time conditions of the coal-fired unit with an actual coal mill level differential pressure curve.
FIG. 4 is a comparison of a time-varying curve obtained by simulated prediction of the outlet wind speed of the coal mill by the twinning model under real-time conditions of the coal-fired unit with an actual outlet wind speed curve of the coal mill.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Taking a double-inlet double-outlet steel ball coal mill of a certain power plant as an example, the embodiment of the invention is described in detail.
A coal mill twin model operation method comprises the following steps:
step 1: based on the working principle of the double-inlet double-outlet steel ball coal mill and the heat balance relation in the double-inlet double-outlet steel ball coal mill, combining expert experience, creating a coal mill twin model, wherein the coal mill twin model comprises three modules, namely a coal mill outlet temperature twin model, a coal mill material level twin model and a coal mill output twin model;
wherein, coal pulverizer outlet temperature twin model is as follows:
wherein: t is t out : current coal mill outlet temperature, B gm : single end coal feed, δw: variation of raw coal moisture mass fraction, t, in coal grinding process g : coal feedingTemperature, c gq : specific heat capacity of steel ball M gq : steel ball loading, c m : specific heat capacity of pulverized coal M m : amount of stored coal, K bs : model identification parameters;
the coal mill material level twin model is established as follows:
the material level modeling adopts differential pressure to indirectly reflect the height of the coal level, and the relation between the differential pressure coal level and the height coal level is as follows:
δP＝k*H m *(5+G f 2 )
wherein: h m : current level height of coal mill, μ: coal mill wind-coal ratio, G r : hot air volume, G l : cold air volume, G mf : sealed air volume, L: length ρ of coal mill barrel m : the coal density of the coal mill is R, the inner radius of the cylinder body, delta P: differential pressure coal level, k: differential pressure coefficient of coal mill, G f : primary air inlet flow;
the coal mill output twin model is established as follows:
when G f >5 P i >1000
When G f >5 P i <1000
Wherein: v (V) f : outlet wind speed, K f : conversion coefficient, P f : hearth pressure, P i : primary air pressure;
step 2: realize the data transmission of the coal-fired unit DCS and the twin DCS. The transmission direction is that the coal-fired unit DCS transmits the data to the big data server in a unidirectional way, the big data server and the coal-fired unit DCS transmit real-time data or historical data of the operation of the double-in double-out steel ball coal mill to the big data server through a DCS exchanger, an OPC server, a protocol conversion gateway, a firewall and a net gate, and the big data server transmits the data to the twin DCS in a unidirectional way to finish the initialization of the twin DCS. And importing the data of the coal mill into a coal mill twin model through the twin DCS. The data synchronization process of the twin DCS is shown in the attached figure 1;
step 3: the outlet temperature of the real-time double-inlet double-outlet steel ball coal mill and various measurable parameters are utilized, and the predicted value t of the outlet temperature of the future double-inlet double-outlet steel ball coal mill is predicted by the twin model of the coal mill in step 1 through the internal heat relation of the coal mill f Differential pressure delta P of current material level height of reaction coal mill and outlet wind speed V of double-inlet double-outlet steel ball coal mill f Calculating the real-time working condition to realize the prediction function of the twin model of the coal mill;
the calculation process comprises the following steps:
heat of moisture absorption during coal grinding: q (Q) w ＝B gm *δw*(2491+1.884*t out -4.19t g )
Heat generated in the coal grinding process: q (Q) j ＝41.57*B gm
Coal mill air and coal input heat: q (Q) in ＝B gm *c m *t g +G f *c f *t f +Q j
Wherein: g f : primary air flow, c f : specific heat capacity of primary air, t f : primary air temperature;
output heat of coal mill air and coal:
Q out ＝B m *c m *t out +(G f +B gm *δw)*c f *t out +Q w
wherein: b (B) m The output of the coal mill;
internal energy relationship of coal mill:
energy conversionAfter conversion, the unit is converted:
wherein: dt (dt) out : rate of change of outlet temperature of coal mill, M cm : coal mill coal storage quality c gm : specific heat capacity of coal;
predicting outlet temperature of a coal mill: t is t f ＝t out +T*dt out
t f Predicted value T of outlet temperature of coal mill: time interval
Calculating the pressure difference delta P of the current material level height of the reaction coal mill through a coal mill material level twin model; calculating the outlet wind speed V through a coal mill output twin model f ；
Step 4: and 3, predicting the outlet temperature t of the future double-inlet and double-outlet steel ball coal mill calculated in the step 3 f Differential pressure δP and outlet wind speed V f And returning the result to the twin DCS to realize the data interconnection of the twin model of the coal mill and the twin DCS.
FIG. 2 is a comparison of a time-varying curve obtained by carrying out simulation prediction on the outlet temperature of the coal mill by the twin model under the real-time working condition of the coal-fired unit and an actual outlet temperature curve of the coal mill, and can be seen from the graph: the outlet temperature twin model simulates and predicts the outlet temperature of the coal mill and the temperature error of the actual measuring point is smaller, which indicates that the outlet temperature twin model has higher precision, can realize the monitoring function of the outlet temperature of the coal mill, and prevents the combustion efficiency of the boiler from being influenced by the too low temperature or the safety accident caused by the explosion of the coal dust from being caused by the too high temperature. .
FIG. 3 is a comparison of a time-varying curve obtained by performing simulation prediction on the material level pressure difference of the coal mill by the twin model under the real-time working condition of the coal-fired unit and an actual material level pressure difference curve of the coal mill, and can be seen from the graph: the actual material level differential pressure has oscillation characteristics, and the twin model prediction result can reflect the oscillation range of the discharge level differential pressure more accurately, so that a reference is provided for the feeding control of the coal mill. FIG. 4 is a comparison of a time-varying curve obtained by carrying out simulation prediction on the outlet wind speed of the coal mill by the twin model under the real-time working condition of the coal-fired unit and an actual outlet wind speed curve of the coal mill, and can be seen from the graph: the output twin model of the coal mill accurately reflects the variation trend of the outlet wind speed of the coal mill, and has reference value for controlling the primary wind quantity, the opening degree of the cold and hot wind valve and the coal feeding quantity of the coal mill and analyzing the energy consumption characteristic of the coal mill.
Claims (2)
1. A coal mill twin model operation method is characterized in that: the method comprises the following steps:
step 1: based on the working principle of the double-inlet double-outlet steel ball coal mill and the heat balance relation in the double-inlet double-outlet steel ball coal mill, combining expert experience, creating a coal mill twin model, wherein the coal mill twin model comprises three modules, namely a coal mill outlet temperature twin model, a coal mill material level twin model and a coal mill output twin model;
wherein, coal pulverizer outlet temperature twin model is as follows:
wherein: t is t out : current coal mill outlet temperature, B gm : single end coal feed, δw: variation of raw coal moisture mass fraction, t, in coal grinding process g : the coal feeding temperature, c gq : specific heat capacity of steel ball M gq : steel ball loading, c m : specific heat capacity of pulverized coal M m : amount of stored coal, K bs : model identification parameters;
the coal mill material level twin model is established as follows:
the material level modeling adopts differential pressure to indirectly reflect the height of the coal level, and the relation between the differential pressure coal level and the height coal level is as follows:
δP＝k*H m *(5+G f 2 )
wherein: h m : current level height of coal mill, μ: air coal of coal millRatio of G r : hot air volume, G l : cold air volume, G mf : sealed air volume, L: length ρ of coal mill barrel m : coal density in coal mill, R: barrel inside radius, δp: differential pressure coal level, k: differential pressure coefficient of coal mill, G f : primary air inlet flow;
the coal mill output twin model is established as follows:
when G f ＞5 P i ＞1000
When G f ＞5 P i ＜1000
Wherein: v (V) f : outlet wind speed, K f : conversion coefficient, P f : hearth pressure, P i : primary air pressure;
step 2: simulating the operation of a double-inlet and double-outlet steel ball coal mill in an actual power plant unit by utilizing a twin system of the coal-fired unit DCS, namely the twin DCS; the transmission direction of the unit operation data is that the coal-fired unit DCS transmits the unit operation data to the big data server in a one-way, and then the big data server transmits the unit operation data to the twin DCS, and the coal-fired unit operation data is led into a coal mill twin model through the twin DCS;
step 3: the outlet temperature of the real-time double-inlet double-outlet steel ball coal mill and various measurable parameters are utilized, and the predicted value t of the outlet temperature of the future double-inlet double-outlet steel ball coal mill is predicted by the twin model of the coal mill in step 1 through the internal heat relation of the coal mill f Differential pressure delta P of current material level height of reaction coal mill and outlet wind speed V of double-inlet double-outlet steel ball coal mill f Calculating the real-time working condition to realize the prediction function of the twin model of the coal mill;
the calculation process comprises the following steps:
heat of moisture absorption during coal grinding: q (Q) w ＝B gm *δw*(2491+1.884*t out -4.19t g )
Heat generated in the coal grinding process: q (Q) j ＝41.57*B gm
Coal mill air and coal input heat: q (Q) in ＝B gm *c m *t g +G f *c f *t f +Q j
Wherein: g f : primary air flow, c f : specific heat capacity of primary air, t f : primary air temperature;
output heat of coal mill air and coal:
Q out ＝B m *c m *t out +(G f +B gm *δw)*c f *t out +Q w
wherein: b (B) m : the output of the coal mill;
internal energy relationship of coal mill:
energy change, unit conversion:
wherein: dt (dt) out : rate of change of outlet temperature of coal mill, M cm : coal mill coal storage quality c gm : specific heat capacity of coal;
predicting outlet temperature of a coal mill: t is t f ＝t out +T*dt out
t f : coal mill outlet temperature prediction value T: time interval
Calculating the pressure difference delta P of the current material level height of the reaction coal mill through a coal mill material level twin model; calculating the outlet wind speed V through a coal mill output twin model f ；
Step 4: and 3, predicting the outlet temperature t of the future double-inlet and double-outlet steel ball coal mill calculated in the step 3 f Differential pressure δP and outlet wind speed V f The result returns to the twin DCS to realize the twin model and twin of the coal millAnd generating data interconnection of DCS.
2. The method for operating a twin model of a coal pulverizer as set forth in claim 1, wherein: the model identification parameter K described in step 1 bs And performing function fitting and parameter identification by utilizing a nonlinear least square method and relying on historical operation data of the coal-fired unit.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109977583A (en) * | 2019-04-03 | 2019-07-05 | 东南大学 | A kind of coal pulverizer simulation model dynamic parameter setting method of combination verifying model |
| CN113339787A (en) * | 2021-06-11 | 2021-09-03 | 华北电力大学（保定） | Fluidized bed boiler operation optimization method and system based on digital twinning |
| CN113552854A (en) * | 2021-07-04 | 2021-10-26 | 核新云科技(南京)有限公司 | Virtual twin system for power plant operation |
| RU2767723C1 (en) * | 2021-02-12 | 2022-03-18 | Акционерное общество "СУЭК-Кузбасс" | Use of digital twins in an automated system for virtual training of the user to work in the mine, an automated system for virtual training of the user to work in the mine and a method for virtual training of the user to work in the mine |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109977583A (en) * | 2019-04-03 | 2019-07-05 | 东南大学 | A kind of coal pulverizer simulation model dynamic parameter setting method of combination verifying model |
| RU2767723C1 (en) * | 2021-02-12 | 2022-03-18 | Акционерное общество "СУЭК-Кузбасс" | Use of digital twins in an automated system for virtual training of the user to work in the mine, an automated system for virtual training of the user to work in the mine and a method for virtual training of the user to work in the mine |
| CN113339787A (en) * | 2021-06-11 | 2021-09-03 | 华北电力大学（保定） | Fluidized bed boiler operation optimization method and system based on digital twinning |
| CN113552854A (en) * | 2021-07-04 | 2021-10-26 | 核新云科技(南京)有限公司 | Virtual twin system for power plant operation |
Non-Patent Citations (2)
| Title |
|---|
| 双进双出磨煤机制粉***机理建模与仿真;谷俊杰;陈见永;张岩;;动力工程学报;20170615(06);全文 * |
| 基于数字孪生的煤矿瓦斯事故安全管理;王佳奇;卢明银;;煤矿安全;20200814(08);全文 * |
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