CN113361192A - Boiler internal heating surface wall temperature safety monitoring and evaluating system - Google Patents
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Abstract
The invention discloses a boiler internal heating surface wall temperature safety monitoring and evaluating system which comprises a furnace tube wall temperature online calculating module and a wall temperature safety evaluating and displaying module, wherein the furnace tube wall temperature online calculating module is used for calculating the wall temperature of a furnace tube, and the wall temperature safety evaluating and displaying module is used for carrying out real-time wall temperature displaying, overtemperature area early warning displaying and historical data comprehensive displaying on the result obtained by the furnace tube wall temperature online calculating module. The system adopts a method of combining mechanism model, mathematical model, numerical simulation and field test, utilizes test data to correct numerical simulation results, and calculates the temperature of each metal pipe wall by comprehensively considering thermal deviation and hydraulic deviation by means of data processing optimization methods such as neural network and particle swarm algorithm, so as to realize real-time monitoring and operation evaluation of wall temperature of different regions under different working conditions, thereby greatly improving the safety and economy of power plant operation.
Description
Technical Field
The invention belongs to the technical field of boiler safety monitoring, and particularly relates to a boiler tube safety state evaluation system for monitoring the temperature of the inner wall of a boiler.
Background
In recent years, the power industry is developed at a high speed, a coal-fired unit of a power plant is rapidly developing to high capacity and high parameter, due to the large capacity of a boiler, after steam parameters are improved, the water wall and the superheater pipeline are easy to generate local thermal deviation due to the non-uniformity of smoke parameter distribution and the non-uniformity of steam, the problem of overtemperature explosion leakage of the water wall and the superheater on the upper part of a boiler hearth is increasingly prominent, the safe operation of the unit is seriously influenced, huge economic loss is caused to the power plant, and therefore, the on-line monitoring technology of the wall temperature of the boiler is very important.
The prior art is searched and found, and the prior art aiming at the boiler wall temperature on-line monitoring at present comprises the following steps: (1) the sensor in the furnace directly measures the point. Taking the chinese patent ZL 201110428271.3 as an example, it optimizes the measurement point arrangement by directly designing the position where the sensor is easily over-temperature. However, the sensor arranged on the heating surface is easy to lose efficacy due to overhigh temperature, and the over-temperature situation of the heating surface cannot be comprehensively monitored because the over-temperature position which is easy to exceed the temperature is selected by experience and only the temperature of a limited measuring point can be obtained; (2) and (6) fitting the data to a model. Taking the chinese patent ZL 201610070727.6 as an example, the fitting of data such as metal pipe wall temperature neural network training is performed according to historical operation data such as boiler inlet smoke temperature, smoke velocity, steam temperature, and the like. However, the method can only fit the temperature of the limited measuring points, and the data fitting precision is not high; (3) the exterior points are combined with wall temperature calculations. Taking the chinese patent ZL 201710752198.2 as an example, a real-time database of power plant boiler operation is formed by setting a metal tube outer wall temperature measuring point, and real-time calculation of the furnace tube wall temperature is performed. However, the measurement and calculation result of the method is not accurate enough, and the wall temperature change can not be accurately fed back on line; (4) numerical simulation is combined with thermodynamic and hydrodynamic deviation calculation. Taking the chinese patent ZL 201210234175.X as an example, numerical simulation is used to obtain flue gas temperature and velocity distribution, which are used as initial conditions for thermodynamic and hydrodynamic calculation, thermodynamic and hydrodynamic calculation and check calculation of wall temperature are performed, and a result is output. The method has the defects that the real-time performance cannot be achieved, the numerical simulation result is not verified, and the reliability of the result cannot be determined.
Disclosure of Invention
In order to solve the problems, the invention provides a boiler internal heating surface wall temperature safety monitoring and evaluating system, which adopts a method of combining mechanism model, mathematical model, numerical simulation and field test in four dimensions, corrects the numerical simulation result by using test data, calculates the temperature of each metal pipe wall by comprehensively considering thermal deviation and hydraulic deviation by means of data processing optimization methods such as neural network and particle swarm algorithm, realizes real-time monitoring and operation evaluation of wall temperature of different areas under different working conditions, and greatly improves the safety and economy of power plant operation.
The technical scheme of the invention is as follows:
a boiler internal heating surface wall temperature safety monitoring and evaluating system comprises a furnace tube wall temperature on-line calculating module and a wall temperature safety evaluating and displaying module.
The furnace tube wall temperature on-line calculating module is used for calculating the furnace tube wall temperature and comprises the following specific steps:
s1: collecting boiler operation real-time state parameters: flue gas temperature, flue gas flow, working medium temperature, working medium flow and working medium pressure;
s2: substituting the parameters into an on-line prediction model of the distribution of the flue gas flow field in the boiler based on full-working-condition numerical simulation and a neural network to obtain inlet flue gas conditions including the average flue gas temperature and the radiant heat of the inlet of the heating surface;
s3: performing online thermal calculation according to the inlet flue gas condition to obtain a furnace width and a furnace high thermal deviation condition;
s4: performing on-line hydraulic calculation by using the average parameters of the working medium at the inlet of the heating surface obtained in the step S1 to obtain a hydraulic deviation condition;
s5: and (4) performing online calculation of the wall temperature of the furnace tube according to the furnace width and high heat deviation condition of the furnace obtained in the step (S3) and the hydraulic deviation condition obtained in the step (S4), and outputting the wall temperature distribution result of the furnace tube at each specific position of the heating surface.
The wall temperature safety evaluation display module is used for carrying out real-time wall temperature display, overtemperature area early warning display and historical data comprehensive display on the result obtained by the furnace tube wall temperature online calculation module, and the specific method comprises the following steps:
(1) and (3) displaying the wall temperature in real time: establishing a three-dimensional model of boiler heating surface layout, collecting furnace tube wall temperature data output by a furnace tube wall temperature online calculation module at regular intervals (data acquisition intervals can be selected according to engineering and actual requirements of users), and displaying the furnace tube wall temperature data in the three-dimensional model in real time;
(2) early warning display of an overtemperature area: calculating the average value T of the wall temperature of the furnace tube once at regular intervals (the specific time can be selected according to the actual requirements of projects and users), and when T ismaxWhen T is larger than a critical value (the critical value is related to a specific material, and can be obtained according to the specific material by a person skilled in the art), performing red early warning on a corresponding area of the three-dimensional model, and displaying T, TmaxThe furnace tube operation safety value;
(3) comprehensively displaying historical data: and drawing furnace tube wall temperature data output by the furnace tube wall temperature online calculation module into historical operation data, and identifying extreme point data on the curve.
Preferably, the boiler flue gas flow field distribution online prediction model based on full-working-condition numerical simulation and the neural network comprises the following steps:
s1: collecting related size structures of the boiler, air volume, coal volume and coal burning characteristic data of different loads and different working conditions in the daily operation process, and establishing a three-dimensional frame structure of the boiler and carrying out grid division on the basis of Computational Fluid Dynamics (CFD);
s2: establishing a turbulent flow gas-solid two-phase flow model, a combustion model, a heat transfer model and a volatile component separation model of a target boiler in CFD, giving values of input variables of various working conditions by adopting a Latin hypercube sampling method, namely inputting variable data sets, sampling in the value range of the variables, performing numerical simulation by taking various working condition parameters obtained by sampling as boundary conditions, and obtaining smoke temperature fields and velocity field distributions of different sections under various working conditions;
s3: discretizing the required section, dividing the section into a plurality of regions as required, counting a required flue gas temperature and speed data set by utilizing a multi-working-condition numerical simulation result in the step S2, providing sufficient samples for building a flue gas temperature and flue gas speed prediction model, training the samples by adopting a machine learning algorithm to obtain the flue gas temperature and flue gas speed prediction model, predicting the flue gas temperature and the flue gas speed, and simulating according to the predicted flue gas temperature and flue gas speed to obtain flue gas flow field distribution;
s4: dividing the heating surface in the boiler into a plurality of layers along the height direction (the number of layers depends on engineering limitation), arranging a sensor on a required section, measuring the smoke temperature and the smoke speed of the sensor, and correcting the simulation result of smoke flow field distribution by using the measured value to obtain a corrected numerical simulation data set of the actual all-condition of the boiler;
s5: constructing a proxy model, randomly selecting a training sample set and a test sample set from the corrected data set obtained in the step S4, ensuring that the selected training sample set covers most of the load range operation conditions of the boiler, training the sample set for multiple times aiming at different activation functions and hidden layers, fitting the functional relationship between each condition parameter and the section flue gas flow field, selecting the parameter with the highest accuracy as the final training parameter, and adjusting the weight and the threshold of the proxy model by using an optimization algorithm to obtain the optimized flue gas distribution online prediction model of the boiler required section under different conditions.
Preferably, the proxy model takes boiler load, primary air volume, secondary air volume, primary air temperature, secondary air temperature, coal feeding quantity and damper adjusting baffle feedback as input variables, and takes smoke temperature and flow speed at each discrete point of the section of the hearth as output quantities.
Preferably, the agent model is trained by using ann (artificial Neural network), and the optimization algorithm is a pso (particle Swarm optimization) method.
The invention has the beneficial effects that:
1. the thermal power deviation and the hydraulic power deviation are comprehensively considered, and the nonuniformity of the flue gas and the hydraulic power is considered, so that the wall temperature calculation is close to the actual result, and the calculation accuracy is improved;
2. the measurement result of the smoke temperature measuring point in the supplementary furnace is used for checking and correcting the numerical simulation calculation result of the smoke temperature field and the speed field in the furnace, so that the monitoring deviation caused by the failure of the sensor is reduced, and the accuracy and the reliability of the result are improved;
3. a method of mutual check of numerical simulation and big data fitting is adopted, so that the uneven rule of the flue gas is disclosed more accurately;
4. the method combines a mechanism model, numerical simulation, a mathematical model and a field test to obtain the temperature field and the speed field in the boiler in different running states of the boiler, and wall temperature measuring points with different densities are arranged according to actual requirements, so that comprehensive monitoring and early warning of the overheating of the heating surface can be realized, and historical big data analysis and optimization are performed by means of a neural network and a particle swarm algorithm, so that accurate online real-time feedback of wall temperature change is realized;
5. the system adopts multi-modular safety state evaluation, three-dimensionally shows the over-temperature position in the boiler, and simultaneously can form an over-temperature report and an operation check result, thereby improving the operation and maintenance efficiency and safety of the power plant.
Drawings
FIG. 1 is a furnace tube wall temperature on-line calculation module according to the present invention.
Fig. 2 is a schematic diagram illustrating an ANN agent model establishment according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
A boiler internal heating surface wall temperature safety monitoring and evaluating system comprises a furnace tube wall temperature on-line calculating module and a wall temperature safety evaluating and displaying module.
The furnace tube wall temperature on-line calculating module comprises the following specific steps:
s1: acquiring real-time state parameters of boiler operation through a remote monitoring platform, wherein the parameters comprise hearth structure parameters, coal quality parameters, the commissioning condition of a burner, flue gas temperature, flue gas flow, working medium temperature, working medium flow and working medium pressure;
s2: substituting the parameters into an on-line prediction model of the distribution of the flue gas flow field in the boiler based on full-working-condition numerical simulation and a neural network to obtain inlet flue gas conditions including the average flue gas temperature and the radiant heat of the inlet of the heating surface;
s3: performing online thermal deviation calculation according to the inlet flue gas condition to obtain a furnace width and a furnace high thermal deviation condition;
s4: performing on-line hydraulic deviation calculation by using the average parameters of the working medium at the inlet of the heating surface obtained in the step S1 to obtain a hydraulic deviation condition;
s5: and (4) performing online calculation of the wall temperature of the furnace tube according to the furnace width and high heat deviation condition of the furnace obtained in the step (S3) and the hydraulic deviation condition obtained in the step (S4), and outputting the wall temperature distribution result of the furnace tube at each specific position of the heating surface.
The wall temperature safety evaluation display module is used for carrying out real-time wall temperature display, overtemperature area early warning display and historical data comprehensive display on the result obtained by the furnace tube wall temperature online calculation module, and the specific method comprises the following steps:
(1) and (3) displaying the wall temperature in real time: establishing a three-dimensional model of boiler heating surface layout, collecting furnace tube wall temperature data output by a furnace tube wall temperature online calculation module every 3 minutes, and displaying the furnace tube wall temperature data in the three-dimensional model in real time;
(2) early warning display of an overtemperature area: calculating the average value T of the wall temperature of the furnace tube for 3 minutes every 3 minutes, and calculating the average value T when T ismax-T is greater than criticalDuring the value, red early warning is carried out in the corresponding area of the three-dimensional model, and T, T are displayedmaxThe furnace tube operation safety value;
(3) comprehensively displaying historical data: and drawing furnace tube wall temperature data output by the furnace tube wall temperature online calculation module into historical operation data, and identifying extreme point data on the curve.
Preferably, the on-line prediction model of the distribution of the flue gas flow field in the boiler based on the full-condition numerical simulation and the neural network, as shown in fig. 1, comprises the following steps:
s1: collecting related size structures of the boiler and air volume, coal volume and coal burning characteristic data of different loads and different working conditions in the daily operation process, and establishing a three-dimensional frame structure of the boiler and carrying out grid division on the basis of Computational Fluid Dynamics (CFD) software;
s2: establishing a gas-solid multi-phase turbulent flow model, a combustion model, a heat transfer model and a volatilization analysis model of a target boiler in CFD, giving values of input variables of various working conditions by adopting a Latin hypercube sampling method, namely inputting variable data sets, sampling in the value range of the variables, and carrying out numerical simulation by taking various working condition parameters obtained by sampling as boundary conditions to obtain the distribution of flue gas temperature fields and velocity fields of different sections under various working conditions;
s3: discretizing the required section, dividing the section into a plurality of regions as required, and counting a required flue gas temperature and speed data set by using the multi-working-condition numerical simulation result in the step S2 to provide sufficient samples for establishing a flue gas temperature and flue gas speed prediction model;
s4: arranging sensors at grid points of a required section, measuring the smoke temperature and the smoke speed of the sensors, and correcting a smoke flow field distribution simulation result by using the measured values to obtain a corrected numerical simulation data set of the actual all-condition of the boiler;
s5: constructing a proxy model, as shown in fig. 2, randomly selecting a training sample set and a testing sample set from the final data set obtained in step S4, ensuring that the selected training sample set covers most of the load range operation conditions of the boiler, training the sample set for multiple times aiming at different activation functions and hidden layer numbers, fitting the functional relationship between each condition parameter and the cross-section flue gas flow field, selecting the parameter with the highest accuracy as the final training parameter, and adjusting the weight and the threshold of the proxy model by using an optimization algorithm to obtain the optimized flue gas flow field distribution online prediction model of the required cross section of the boiler under different conditions.
Preferably, the proxy model takes boiler load, primary air volume, secondary air volume, primary air temperature, secondary air temperature, coal feeding quantity and damper adjusting baffle feedback as input variables, and takes smoke temperature and flow speed at each discrete point of the section of the hearth as output quantities.
Preferably, the agent model is trained by using an ANN, and the optimization algorithm is a PSO method.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A boiler internal heating surface wall temperature safety monitoring and evaluating system is characterized by comprising a furnace tube wall temperature online calculation module and a wall temperature safety evaluation display module;
the furnace tube wall temperature on-line calculating module is used for calculating the furnace tube wall temperature, and comprises the following specific steps:
s1: collecting boiler operation real-time state parameters: flue gas temperature, flue gas flow, working medium temperature, working medium flow and working medium pressure;
s2: substituting the parameters into an on-line prediction model of the distribution of the flue gas flow field in the boiler based on full-working-condition numerical simulation and a neural network to obtain inlet flue gas conditions including the average flue gas temperature and the radiant heat of the inlet of the heating surface;
s3: performing on-line thermodynamic calculation according to the inlet flue gas conditions obtained in the step S2 to obtain furnace width and furnace high heat deviation conditions;
s4: performing on-line hydraulic calculation by using the average value of the heating surface inlet working medium parameters obtained in the step S1 to obtain a hydraulic deviation condition;
s5: performing online calculation of the wall temperature of the furnace tube according to the furnace width and high heat deviation condition of the furnace obtained in the step S3 and the hydraulic deviation condition obtained in the step S4, and outputting the wall temperature distribution result of the furnace tube at each specific position of the heating surface;
the wall temperature safety assessment display module is used for carrying out real-time wall temperature display, overtemperature area early warning display and historical data comprehensive display on the result obtained by the furnace tube wall temperature online calculation module, and comprises the following specific steps:
(1) and (3) displaying the wall temperature in real time: establishing a three-dimensional model of boiler heating surface layout, collecting furnace tube wall temperature data output by a furnace tube wall temperature online calculation module at regular intervals, and displaying the furnace tube wall temperature data in the three-dimensional model in real time;
(2) early warning display of an overtemperature area: calculating the average value T of the wall temperature of the furnace tube at regular intervals, and calculating the average value T of the wall temperature of the furnace tube when T is equalmaxWhen T is larger than a critical value, carrying out red early warning in a corresponding area of the three-dimensional model, and displaying T, TmaxThe furnace tube operation safety value;
(3) comprehensively displaying historical data: and drawing furnace tube wall temperature data output by the furnace tube wall temperature online calculation module into historical operation data, and identifying extreme point data on the curve.
2. The system for monitoring and evaluating the wall temperature of the heating surface in the boiler according to claim 1, wherein the model for online prediction of the distribution of the flue gas flow field in the boiler based on the full-condition numerical simulation and the neural network is constructed by the following steps:
s1: collecting related size structures of the boiler and air volume, coal volume and coal burning characteristic data of different loads and different working conditions in the daily operation process, and establishing a three-dimensional frame structure of the boiler and carrying out grid division on the basis of Computational Fluid Dynamics (CFD) software;
s2: establishing a target boiler model in CFD, giving values of input variables of various working conditions by adopting a Latin hypercube sampling method, namely inputting variable data sets, sampling in the value range of the variables, carrying out numerical simulation by taking various working condition parameters obtained by sampling as boundary conditions, and obtaining the distribution of flue gas temperature fields and velocity fields of different sections under various working conditions;
s3: discretizing the required section, dividing the section into a plurality of regions as required, counting a required flue gas temperature and speed data set by using the multi-working-condition numerical simulation result in the step S2, providing samples for predicting the flue gas temperature and the flue gas speed, and simulating according to the predicted flue gas temperature and the predicted flue gas speed to obtain the flue gas flow field distribution;
s4: arranging sensors on the required section, measuring the smoke temperature and the smoke speed of the sensors, and correcting the simulation result of the smoke flow field distribution by using the measured values to obtain a corrected numerical simulation data set of the actual all-condition of the boiler;
s5: and constructing a proxy model, fitting the functional relation between each working condition parameter and the section flue gas flow field, and adjusting the weight and the threshold of the proxy model by using an optimization algorithm to obtain an optimized boiler flue gas flow field distribution online prediction model based on full-working condition numerical simulation and a neural network.
3. The system for monitoring and evaluating the wall temperature of the heating surface in the boiler as claimed in claim 2, wherein the proxy model takes boiler load, primary air volume, secondary air volume, primary air temperature, secondary air temperature, coal feeding quantity and damper adjustment baffle feedback as input variables, and smoke temperature and smoke velocity at discrete points of a furnace section as output variables.
4. The system for monitoring and evaluating the wall temperature safety of the heating surface in the boiler as claimed in claim 2, wherein the agent model is trained by ANN, and the optimization algorithm is PSO method.
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