CN111829124A - Dry coal shed ventilation system and ventilation method - Google Patents
Dry coal shed ventilation system and ventilation method Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F7/00—Ventilation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Abstract
The invention discloses an intelligent ventilation system and method for a large-scale dry coal shed, relates to a wind environment optimization technology for large-scale industrial buildings, and can solve the problems of working comfort and environmental protection of personnel in a large-scale storage yard dry coal shed structure. The system mainly comprises an anemorumbometer, an electric shutter and a control device thereof, a harmful gas and particle concentration monitor and a wind environment database. The method comprises the steps of establishing boundary conditions of the dry coal shed based on a Computational Fluid Dynamics (CFD) numerical simulation technology, simulating a wind environment under basic working conditions, storing a ventilation frequency simulation result into a wind environment database, supplementing, correcting, training and predicting simulation data by combining field wind speed and direction, harmful gas and particle actual measurement data, and sending a control instruction to regulate and control the opening and closing angle of the electric shutter, so that the optimal ventilation effect of the large-span dry coal shed is achieved.
Description
Technical Field
The invention relates to the field of ventilation of coal sheds, in particular to a ventilation system and a ventilation method of a dry coal shed.
Background
The large-span dry coal shed structure belongs to large-scale industrial buildings, and has higher requirement on internal wind environment due to the process and operation requirements. The large-space industrial building needs to guarantee certain ventilation times, properly improves the air flow speed in the space, ensures the working comfort of personnel, is beneficial to the emission of harmful gas and heat, and plays a certain role in reducing the spontaneous combustion probability of coal. In addition, in order to meet the requirement of environmental protection, the number of times of ventilation and air change is not more than a specific number, so that dust is not diffused, and air pollution is caused. The large dry coal shed structure gable wall adopts certain opening or open holes to ensure certain ventilation performance, and when the external wind speed is large, the area of the opening or open holes needs to be reduced to ensure that dust is not blown out, so that the opening rate of the gable wall needs to be regulated and controlled to a certain degree. In the prior art, the ventilation parameters are often controlled in an empirical control mode.
Disclosure of Invention
The invention provides a ventilation system and a ventilation method for a dry coal shed, aiming at solving the problem of rough control caused by the adoption of an artificial experience mode in the conventional dry coal shed control mode.
In a first aspect, an embodiment of the invention provides a dry coal shed ventilation system, which comprises a dry coal shed, an anemoclinograph positioned outside the dry coal shed, an electric shutter positioned on a gable wall of the dry coal shed, and a control system; the anemoclinograph is used for sending collected wind speed and wind direction information to the control system, the control system is used for inputting the wind speed and wind direction information and preset target ventilation times into a ventilation times prediction model, reversely solving the opening and closing angle of the electric shutter, and controlling the electric shutter to ventilate by utilizing the opening and closing angle of the electric shutter; the output information of the ventilation frequency prediction model comprises ventilation frequency, and the input information comprises the opening and closing angle of the shutter, wind speed information and wind direction information.
And further, the control system is also used for receiving the concentration value of the pollution source particles detected by the monitor after solving the opening and closing angle of the electric shutter, and controlling the electric shutter to ventilate according to the maximum opening and closing angle when judging that the concentration value of the pollution source particles is greater than or equal to the threshold value.
Further, the pre-trained ventilation times prediction model is obtained by the following method:
carrying out wind environment simulation of different incoming flow conditions and open conditions on the dry coal shed under the geometric boundary conditions by utilizing a computational fluid mechanics numerical simulation method, obtaining simulation results of internal and external wind environments, and forming a wind environment database, wherein data in the wind environment database comprise wind speeds and wind directions of internal and external reference points of the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed and ventilation times;
and training and cross-verifying the data in the wind environment database by adopting an artificial neural network, and finally obtaining the pre-trained ventilation frequency prediction model.
Further, the control system is further configured to control the electric louver to ventilate by using the opening and closing angle of the electric louver when the concentration value of the pollution source particles is judged to be smaller than a threshold value.
Further, in the process of forming the wind environment database, the ventilation times are obtained by the following method:
and acquiring the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the ventilation times R.
In a second aspect, an embodiment of the present invention provides a dry coal shed ventilation method using the above dry coal shed ventilation system, including the steps of:
acquiring wind speed and wind direction information acquired by an anemorumbometer;
inputting the wind speed and wind direction information and preset target ventilation times into a pre-trained ventilation times prediction model, and reversely solving the opening and closing angle of the electric shutter, wherein the output information of the ventilation times prediction model comprises the ventilation times, and the input information comprises the opening and closing angle of the shutter, the wind speed information and the wind direction information;
and controlling the electric shutter to ventilate by utilizing the opening and closing angle of the electric shutter.
Further, after the opening and closing angle of the electric blind is solved, the method further comprises the following steps:
receiving a concentration value of pollution source particles detected by a monitor positioned inside the dry coal shed;
and when the concentration value of the pollution source particles is judged to be larger than or equal to the threshold value, controlling the electric shutter to ventilate according to the maximum opening and closing angle.
Further, the method also comprises the following steps:
carrying out wind environment simulation of different incoming flow conditions and open conditions on the dry coal shed under the geometric boundary conditions by utilizing a computational fluid mechanics numerical simulation method, obtaining simulation results of internal and external wind environments, and forming a wind environment database, wherein data in the wind environment database comprise wind speeds and wind directions of internal and external reference points of the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed and ventilation times;
and training and cross-verifying data in a wind environment database by adopting an artificial neural network, and finally obtaining the ventilation frequency prediction model.
And further, when the concentration value of the pollution source particles is judged to be smaller than a threshold value, the opening and closing angle of the electric shutter is utilized to control the electric shutter to ventilate.
Further, in the process of forming the wind environment database, the ventilation times are obtained by the following method:
and acquiring the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the ventilation times R.
According to the embodiment of the invention, the boundary condition of the dry coal shed is established based on a Computational Fluid Dynamics (CFD) numerical simulation technology, the wind environment of the basic working condition is simulated, the simulation result of the air change times is stored in a wind environment database, the simulation data is supplemented, corrected, trained and predicted by combining the field wind speed and direction, harmful gas and particle actual measurement data, and a control instruction is sent to regulate and control the opening and closing angle of the electric shutter, so that the optimal ventilation effect of the large-span dry coal shed is realized.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a schematic diagram of the overall architecture of the control system of the dry coal shed ventilation system in some embodiments of the invention;
FIG. 2 is a schematic illustration of the coordinated control of hardware and software in the dry coal shed ventilation system in some embodiments of the invention;
FIG. 3 is a schematic diagram of the system components of a dry coal shed ventilation system in some embodiments of the invention;
FIG. 4 is a schematic flow diagram of a method of ventilating a dry coal shed in some embodiments of the invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
As shown in fig. 1 and 2, an embodiment of the present invention provides a ventilation system for a large dry coal shed in a bulk storage yard, such as a coal yard and a port, so as to ensure the ventilation times and environmental protection and dust suppression effects of the large dry coal shed.
The ventilation system of the large-span dry coal shed is in linkage control of a hardware system by a software system. Wherein the hardware system consists of (1) an anemorumbometer, (2) an electric shutter and a control device thereof, and (3) a harmful gas and particle concentration monitor (optional); the software system consists of (4) a wind environment database, (5) an intelligent ventilation frequency prediction module and (6) an electric shutter opening and closing angle control module. The system is set up as follows:
(1) several anemometers are located on top of the dry coal shed or at external reference locations, providing external reference meteorological data (primarily wind speed).
(2) The electric shutter is arranged on the gable wall or the skylight of the dry coal shed, and the opening and closing angle of the electric shutter can be controlled through a programmable controller.
(3) A plurality of harmful gas and particle concentration monitors are arranged at key reference positions inside the dry coal shed, monitoring threshold data of key pollution sources inside are provided, and the monitoring threshold data are selectable reference items.
(4) The wind environment database adopts a Computational Fluid Dynamics (CFD) numerical simulation method (or wind tunnel test) to simulate the wind environment under the typical working conditions of different incoming flow conditions and open situations based on the boundary conditions such as the geometric dimension of the dry coal shed. The data in the database includes, but is not limited to, wind speed and wind direction of an external reference point in the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed, and ventilation times.
(5) The intelligent ventilation frequency prediction module takes a wind environment database as a data base, takes the opening and closing angle alpha of the shutter and the wind speed U and the wind direction beta of an external reference point as input variables, takes the ventilation frequency R as output variables, adopts an artificial neural network to train and cross-verify the data, finally obtains an intelligent wind environment prediction model which is expressed as R ═ f (alpha, U and beta), and can repeatedly train and update the model according to the supplement of actually measured data.
(6) The electric shutter opening and closing angle control module takes an intelligent ventilation frequency prediction model R ═ f (alpha, U, beta) as a constraint condition, inputs a target ventilation frequency R0, and calculates a shutter opening and closing angle alpha 0 required to be regulated according to meteorological wind speed and direction (U0, beta 0) conditions. And sends out instructions to the electric shutter controller to realize the regulation and control of the opening and closing angle of the electric shutter controller. And when the monitoring value of the internal key pollution source exceeds a set threshold value, all the pollution sources are started until the monitoring value is lower than the threshold value.
The implementation steps of the large-span dry coal shed ventilation system comprise the following steps:
the method comprises the following steps: determining basic information (geometric dimension, position and area of the electric shutter) and meteorological data (such as wind, rose and the like) of the dry coal shed construction scheme.
Step two: installing and debugging a hardware system, mainly comprising installing an anemorumbometer on an external reference point, installing a harmful gas and particle concentration monitor (optional) on an internal reference point, and realizing the connection of data and a control computer; the electric blind window is installed, and the programmable controller is connected with the control computer.
Step three: and establishing a wind environment database of the dry coal shed. The specific method is that a Computational Fluid Dynamics (CFD) numerical simulation method (or wind tunnel test) is adopted to simulate the results of the boundary conditions such as the geometric dimension of the dry coal shed on the internal and external wind environments under the typical working conditions of different incoming flow conditions and open situations. Constructing a three-dimensional structure model of the large-span dry coal shed in a computer by utilizing GAMBIT software or ICEMCFD software; and (3) leading the three-dimensional structure model of the large-span dry coal shed into FLUENT software or CFX software, and setting external wind environment parameters including wind speed U and wind direction beta and an opening and closing angle alpha of the electric shutter. And outputting the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the empirical ventilation times R.
The data in the database includes, but is not limited to, the wind speed and the wind direction of an external reference point in the dry coal shed, and the wind speed flux on each geometric boundary of the dry coal shed, so that the ventilation times are calculated.
For example, in the above step, the wind speed is set to 1m/s, 3m/s, 5m/s, 7m/s, 10 m/s; the wind direction is sequentially set to N, NNE, NE, NEE, E, SEE, SE, SSE, S, SSW, SW, SWW, W, NWW, NW and NNW; the adjustment of the opening and closing angle alpha of the electric shutter is selected at intervals within the range of 0-180 degrees and is set to be 0 degrees, 30 degrees, 60 degrees, 90 degrees, 120 degrees, 150 degrees and 180 degrees in sequence.
Step four: and training data in the wind environment database to obtain an intelligent ventilation frequency prediction model. The method comprises the steps of training data and cross-verifying by using an artificial neural network with a shutter opening and closing angle alpha and a wind speed U wind direction beta of an external reference point as input variables and ventilation times R as output variables to finally obtain a wind environment intelligent prediction model expressed as R ═ f (alpha, U, beta), and repeatedly training and updating the model according to supplement of actually measured data.
The artificial neural network model construction method can be obtained by a BP-based neural network model construction method, a generalized regression neural network model construction method or other model construction methods; establishing a BP neural network model in MATLAB software and initializing, wherein the topological structure of the BP neural network model comprises an input layer, a hidden layer and an output layer; the number of neurons of an input layer is 3, the number of neurons of an output layer is 1, and the number of neurons of a hidden layer is an integer between 4 and 12; expressing the weight and the threshold of the BP neural network by using a vector xi ═ V1, W1, B1 and B2, wherein V1 is a weight of the connection between a hidden layer and an input layer, W1 is a weight of the connection between an output layer and the hidden layer, B1 is a neuron threshold of the hidden layer, and B2 is a neuron threshold of the output layer; v1, W1, B1 and B2 are random numbers in the interval of [ -0.5, 0.5 ];
step five: the optimal opening and closing angle of the blind window is determined by utilizing an intelligent ventilation frequency prediction model, a target ventilation frequency R0 is input by taking the intelligent ventilation frequency prediction model R ═ f (alpha, U, beta) as a constraint condition, and the opening and closing angle alpha 0 of the blind window to be regulated is calculated according to the conditions of meteorological wind speed and direction (U0, beta 0). And sends out instructions to the electric shutter controller to realize the regulation and control of the opening and closing angle of the electric shutter controller. And when the monitoring value of the internal key pollution source exceeds a set threshold value, all the pollution sources are started until the monitoring value is lower than the threshold value.
The particle concentration of the key pollution source in the dry coal shed is obtained at the same interval time by utilizing the harmful gas and particle concentration monitor arranged in the dry coal shed, and whether the particle concentration exceeds a set threshold value is judged:
1) when the particle concentration of the key pollution source is less than a set threshold value, the current opening and closing angle of the electric shutter is defaulted to be the optimal opening and closing angle;
2) when the particle concentration of the key pollution source is larger than or equal to the set threshold value, electric shutters arranged at all positions of the dry coal shed are adjusted to be in the state of the maximum opening and closing angle.
Step six: the wind speed and the wind direction of an external reference point, harmful gas and particle concentration monitoring data of the internal reference point are connected with the opening and closing angle control module of the electric shutter, and the optimal opening and closing angle instruction is connected with the electric shutter controller, so that the linkage of software and hardware of the system is realized.
As shown in fig. 3, an embodiment of the present invention further provides a dry coal shed ventilation system 100, which includes a dry coal shed 110, an anemoscope 120 located outside the dry coal shed 110, an electric blind 130 located on a gable or a skylight of the dry coal shed 110, and a control system 140; the anemorumbometer 120 is configured to send collected wind speed and wind direction information to the control system 140, and the control system 140 is configured to input the wind speed and wind direction information and a preset target ventilation frequency into a pre-trained ventilation frequency prediction model, reversely solve the opening and closing angle of the electric blind, and control the electric blind 130 to ventilate by using the opening and closing angle of the electric blind; the output information of the ventilation frequency prediction model comprises ventilation frequency, and the input information comprises the opening and closing angle of the shutter, wind speed information and wind direction information. Through the number of times of taking a breath intelligent prediction model, utilize the number of times of taking a breath can solve out concrete shutter angle that opens and shuts, control meticulously, realize automatic control.
Further, the system also comprises a monitor 150 located inside the dry coal shed 110, and the control system 140 is further configured to receive a concentration value of the pollution source particles detected by the monitor 150 after solving the opening and closing angle of the electric shutter, and control the electric shutter 130 to ventilate according to the maximum opening and closing angle when judging that the concentration value of the pollution source particles is greater than or equal to a threshold value. The bottom pocket control method is arranged, and control can be guaranteed not to be invalid.
Further, the pre-trained ventilation times prediction model is obtained by the following method:
carrying out wind environment simulation of different incoming flow conditions and open conditions on the dry coal shed under the geometric boundary conditions by utilizing a computational fluid mechanics numerical simulation method, obtaining simulation results of internal and external wind environments, and forming a wind environment database, wherein data in the wind environment database comprise wind speeds and wind directions of internal and external reference points of the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed and ventilation times;
and training and cross-verifying the data in the wind environment database by adopting an artificial neural network, and finally obtaining a ventilation frequency prediction model.
Further, the control system 140 is further configured to control the electric blind 130 to ventilate by using the opening and closing angle of the electric blind when determining that the concentration value of the pollutant source particles is smaller than a threshold value.
Further, in the process of forming the wind environment database, the ventilation times are obtained by the following method:
and acquiring the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the ventilation times R.
As shown in fig. 4, an embodiment of the present invention further provides a dry coal shed ventilation method using the above dry coal shed ventilation system, including the steps of:
s110, acquiring wind speed and wind direction information acquired by an anemorumbometer;
s120, inputting the wind speed and wind direction information and preset target ventilation times into a ventilation time prediction model, and reversely solving the opening and closing angle of the electric shutter, wherein the output information of the ventilation time prediction model comprises the ventilation times, and the input information comprises the opening and closing angle of the shutter, the wind speed information and the wind direction information;
and S130, controlling the electric shutter to ventilate by utilizing the opening and closing angle of the electric shutter.
Further, after the opening and closing angle of the electric blind is solved, the method further comprises the following steps:
s140, receiving a concentration value of pollution source particles detected by a monitor positioned in the dry coal shed;
s150, when the concentration value of the pollution source particles is judged to be larger than or equal to the threshold value, controlling the electric shutter to ventilate according to the maximum opening and closing angle.
And S160, when the concentration value of the pollution source particles is judged to be smaller than a threshold value, controlling the electric shutter to ventilate by utilizing the opening and closing angle of the electric shutter.
The model is established as follows:
carrying out wind environment simulation of different incoming flow conditions and open conditions on the dry coal shed under the geometric boundary conditions by utilizing a computational fluid mechanics numerical simulation method, obtaining simulation results of internal and external wind environments, and forming a wind environment database, wherein data in the wind environment database comprise wind speeds and wind directions of internal and external reference points of the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed and ventilation times;
and training and cross-verifying the data in the wind environment database by adopting an artificial neural network, and finally obtaining a ventilation frequency prediction model.
In the process of forming the wind environment database, the ventilation times are obtained by the following method:
and acquiring the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the ventilation times R.
The embodiment of the invention establishes the boundary condition of the dry coal shed based on a Computational Fluid Dynamics (CFD) numerical simulation technology, simulates the wind environment of the basic working condition, stores the simulation result of the air change times into a wind environment database, supplements, corrects, trains and predicts the simulation data by combining the actual measurement data of the field wind speed, the wind direction, the harmful gas and the particles, and sends a control instruction to regulate and control the opening and closing angle of the electric shutter, thereby realizing the optimal ventilation effect of the large-span dry coal shed.
In the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The term "plurality" means two or more unless expressly limited otherwise.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A ventilation system of a dry coal shed is characterized by comprising the dry coal shed, an anemorumbometer positioned outside the dry coal shed, an electric shutter positioned on a gable wall of the dry coal shed and a control system; the anemoclinograph is used for sending collected wind speed and wind direction information to the control system, the control system is used for inputting the wind speed and wind direction information and preset target ventilation times into a ventilation times prediction model, reversely solving the opening and closing angle of the electric shutter, and controlling the electric shutter to ventilate by utilizing the opening and closing angle of the electric shutter; the output information of the ventilation frequency prediction model comprises ventilation frequency, and the input information comprises the opening and closing angle of the shutter, wind speed information and wind direction information.
2. The dry coal shed ventilation system according to claim 1, further comprising a monitor located inside the dry coal shed, wherein the control system is further configured to receive a concentration value of a pollution source particle detected by the monitor after solving for the opening and closing angle of the electric shutter, and control the electric shutter to ventilate according to the maximum opening and closing angle when judging that the concentration value of the pollution source particle is greater than or equal to a threshold value.
3. The dry coal shed ventilation system as claimed in claim 1 or 2, wherein the ventilation times prediction model is obtained by:
carrying out wind environment simulation of different incoming flow conditions and open conditions on the dry coal shed under the geometric boundary conditions by utilizing a computational fluid mechanics numerical simulation method, obtaining simulation results of internal and external wind environments, and forming a wind environment database, wherein data in the wind environment database comprise wind speeds and wind directions of internal and external reference points of the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed and ventilation times;
and training and cross-verifying the data in the wind environment database by adopting an artificial neural network, and finally obtaining a ventilation frequency prediction model.
4. The dry coal shed ventilation system of claim 2, wherein the control system is further configured to control the electric louver to ventilate using the opening and closing angle of the electric louver when the concentration value of the pollution source particles is determined to be less than a threshold value.
5. The dry coal shed ventilation system of claim 3, wherein in creating the wind environment database, the number of air changes is obtained by:
and acquiring the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the ventilation times R.
6. A dry coal shed ventilation method using the dry coal shed ventilation system according to any one of claims 1 to 5, comprising the steps of:
acquiring wind speed and wind direction information acquired by an anemorumbometer;
inputting the wind speed and wind direction information and preset target ventilation times into a ventilation time prediction model, and reversely solving the opening and closing angle of the electric shutter, wherein the output information of the ventilation time prediction model comprises the ventilation times, and the input information comprises the opening and closing angle of the shutter, the wind speed information and the wind direction information;
and controlling the electric shutter to ventilate by utilizing the opening and closing angle of the electric shutter.
7. The method for ventilating a dry coal shed as claimed in claim 6, further comprising the steps of, after solving for the opening and closing angle of the electric louver:
receiving a concentration value of pollution source particles detected by a monitor positioned inside the dry coal shed;
and when the concentration value of the pollution source particles is judged to be larger than or equal to the threshold value, controlling the electric shutter to ventilate according to the maximum opening and closing angle.
8. A method of ventilating a dry coal shed as claimed in claim 6 or 7, further comprising the steps of:
carrying out wind environment simulation of different incoming flow conditions and open conditions on the dry coal shed under the geometric boundary conditions by utilizing a computational fluid mechanics numerical simulation method, obtaining simulation results of internal and external wind environments, and forming a wind environment database, wherein data in the wind environment database comprise wind speeds and wind directions of internal and external reference points of the dry coal shed, wind speed flux on each geometric boundary of the dry coal shed and ventilation times;
and training and cross-verifying the data in the wind environment database by adopting an artificial neural network, and finally obtaining a ventilation frequency prediction model.
9. The ventilation method for the dry coal shed according to claim 7, wherein when the concentration value of the pollution source particles is judged to be smaller than a threshold value, the opening and closing angle of the electric shutter is used for controlling the electric shutter to ventilate.
10. The dry coal shed ventilation method of claim 8, wherein in creating the wind environment database, the number of air changes is obtained by:
and acquiring the wind speed flux Q on each geometric boundary of the dry coal shed, and calculating the ratio of the sum sigma Q of the wind speed fluxes Q on each geometric boundary in the dry coal shed to the volume V of the internal space of the dry coal shed to obtain the ventilation times R.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115092561A (en) * | 2022-06-06 | 2022-09-23 | 国家能源集团国源电力有限公司 | Coal bunker spontaneous combustion prevention device and coal bunker |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63176942A (en) * | 1987-01-14 | 1988-07-21 | Toshiba Corp | Ventilation fan |
| CN102479339A (en) * | 2010-11-24 | 2012-05-30 | 香港理工大学 | Method and system for forecasting short-term wind speed of wind farm based on hybrid neural network |
| US20140209695A1 (en) * | 2013-01-30 | 2014-07-31 | Adjust A Vent, a partnership consisting of William E. Pickard, III and Richard D. Frank | Adjustable register vent and grill assembly designed to fit all size standard air distribution boot openings |
| CN104453975A (en) * | 2014-10-14 | 2015-03-25 | 卢新明 | Overall automatic regulation device and method of mine ventilation system |
| CN104864578A (en) * | 2015-05-15 | 2015-08-26 | 华南理工大学 | Sun louver capable of adjusting indoor air speed and indoor air direction of gymnasium |
| CN108590732A (en) * | 2018-07-03 | 2018-09-28 | 山东鼎安检测技术有限公司 | A kind of Air Throttle in Mine Pit air regulator real-time automatic monitoring control system and control method |
| CN109268054A (en) * | 2018-11-29 | 2019-01-25 | 中钢集团马鞍山矿山研究院有限公司 | It is a kind of for underground big cross section, the ventilating and purifying air system in long distance driving face |
-
2020
- 2020-07-17 CN CN202010692893.6A patent/CN111829124A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63176942A (en) * | 1987-01-14 | 1988-07-21 | Toshiba Corp | Ventilation fan |
| CN102479339A (en) * | 2010-11-24 | 2012-05-30 | 香港理工大学 | Method and system for forecasting short-term wind speed of wind farm based on hybrid neural network |
| US20140209695A1 (en) * | 2013-01-30 | 2014-07-31 | Adjust A Vent, a partnership consisting of William E. Pickard, III and Richard D. Frank | Adjustable register vent and grill assembly designed to fit all size standard air distribution boot openings |
| CN104453975A (en) * | 2014-10-14 | 2015-03-25 | 卢新明 | Overall automatic regulation device and method of mine ventilation system |
| CN104864578A (en) * | 2015-05-15 | 2015-08-26 | 华南理工大学 | Sun louver capable of adjusting indoor air speed and indoor air direction of gymnasium |
| CN108590732A (en) * | 2018-07-03 | 2018-09-28 | 山东鼎安检测技术有限公司 | A kind of Air Throttle in Mine Pit air regulator real-time automatic monitoring control system and control method |
| CN109268054A (en) * | 2018-11-29 | 2019-01-25 | 中钢集团马鞍山矿山研究院有限公司 | It is a kind of for underground big cross section, the ventilating and purifying air system in long distance driving face |
Non-Patent Citations (1)
| Title |
|---|
| 刘莉: "捕风系统通风性能的数值模拟研究", 《中国优秀硕士学位论文全文数据库（电子期刊） 工程科技II辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115092561A (en) * | 2022-06-06 | 2022-09-23 | 国家能源集团国源电力有限公司 | Coal bunker spontaneous combustion prevention device and coal bunker |
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