CN116927846A - Multi-branch combined air volume regulation and control system and method for mine ventilation network - Google Patents

Abstract

The invention discloses a multi-branch combined air quantity regulating and controlling system and method for a mine ventilation network, wherein the air quantity regulating and controlling system comprises the following components: a ventilation monitoring subsystem configured for monitoring downhole ventilation parameters in real-time; the ventilation network real-time resolving subsystem is configured to resolve characteristic curves, operation condition parameters, roadway air quantity and ventilation resistance of each fan in the mine in real time based on the ventilation parameters; the air volume demand dynamic calculation subsystem is configured to calculate the optimal proper air volume of the stope in real time based on concentration data, personnel number, temperature and humidity data and human body comfort data of gas, carbon dioxide and other harmful gases of each stope in the pit; the air quantity optimizing and regulating subsystem is configured to analyze the mutual disturbance relation when multiple branches are simultaneously regulated in a combined mode based on the position of a stope network and the air quantity requirement, and calculate the optimal high-efficiency regulating branch set, the wind resistance adjustable range, the air quantity schedulable and the on-demand regulating scheme.

Description

Multi-branch combined air volume regulation and control system and method for mine ventilation network

Technical Field

The invention relates to the technical field of mine ventilation, in particular to a multi-branch combined air quantity regulation and control system and method for a mine ventilation network.

Background

Mine ventilation systems are one of the most important systems in coal mine production systems, and are main measures for guaranteeing safe production, and the importance of the mine ventilation systems is self-evident. As mine extraction work proceeds, mine ventilation networks become increasingly complex. With the dynamic changes of underground environments such as mine excavation succession, unconventional gas gushes, catastrophe smoke discharge, spontaneous combustion ignition, worker comfort level and the like, the air volume requirements of all air utilization places are continuously changed. Therefore, in order to realize safe, efficient, green and energy-saving production of the mine, the air quantity of the mine should be regulated and controlled dynamically according to the requirements. The current coal mine air quantity regulating measures mainly can be divided into ground main fan air regulation and underground ventilation facility air regulation. The ground main fan can cause equal proportion change of the air quantity of all the roadways of the whole mine, so that not only can the useless energy consumption of the fan be increased, but also the air speed of some roadways is over-limited to cause raise dust or lower than the air quantity of gas discharge, and the potential safety hazard is increased. But underground ventilation facilities are adjusted to reasonably distribute ventilation network air flow, so that accurate regulation and control of air quantity are realized. In particular to a complex ventilation network, a single branch is regulated to not meet the large air quantity required by a plurality of specific air branches, and the air quantity requirement can be realized by utilizing the simultaneous joint regulation and control of a plurality of regulating branches. However, the mine ventilation system is a multi-variable complex system with high correlation degree, and the ventilation state of other branches can be changed due to the change of ventilation parameters at a certain position inside the system. At present, the selection of regulating branches in a mine of a complex ventilation network is blind, the air quantity is difficult to regulate and control accurately and efficiently by combining multiple branches according to the needs, and if the regulation is improper, serious casualties are easily caused. The intelligent ventilation system with the functions of intelligent sensing, abnormal analysis, accurate decision regulation and control of ventilation parameters and the like is realized in a mine, the ventilation efficiency can be improved, the safety risk is reduced, the accident occurrence is reduced, the intelligent ventilation system has the advantages of accurate regulation and control of the climate environment of a mining working face, the ventilation is safe and stable, energy conservation and consumption reduction, and has important significance for daily accurate wind control and abnormal failure disaster risk prevention of the ventilation system. In recent years, along with the progress of mine intelligent ventilation construction, the realization of dynamic on-demand regulation and control of the mine air quantity is an important technical means for coal mine intellectualization. Therefore, the multi-branch combined air volume regulation system and method for the complex ventilation network of the mine can provide an efficient, accurate, on-demand and intelligent multi-branch combined air volume regulation method for intelligent air volume regulation decision of the mine.

Disclosure of Invention

The technical aim can be achieved by adopting the following technical characteristics, and other technical effects are brought about.

One object of the present invention is to provide a multi-branch combined air volume control system for a mine ventilation network, comprising:

a ventilation monitoring subsystem configured for monitoring downhole ventilation parameters in real-time;

the ventilation network real-time resolving subsystem is configured to resolve characteristic curves, operation condition parameters, roadway air quantity and ventilation resistance of each fan in the mine in real time based on the ventilation parameters;

the air volume demand dynamic calculation subsystem is configured to calculate the optimal proper air volume of the stope in real time based on concentration data, personnel number, temperature and humidity data and human body comfort data of gas, carbon dioxide and other harmful gases of each stope in the pit;

the air quantity optimizing and regulating subsystem is configured to analyze the mutual disturbance relation when multiple branches are simultaneously regulated in a combined mode based on the position of a stope network and the air quantity requirement, and calculate the optimal high-efficiency regulating branch set, the wind resistance adjustable range, the air quantity schedulable and the on-demand regulating scheme.

In addition, the multi-branch combined air quantity regulation and control system of the mine ventilation network can also have the following technical characteristics:

in one example of the present invention, the ventilation network real-time solution subsystem includes:

the fan working condition real-time calculation module is configured to calculate characteristic curves of all main ventilators of the mine and monitor real-time operation working conditions of all the main ventilators based on fan parameters;

and the real-time wind network parameter calculation module is configured to calculate the wind quantity, density, resistance and natural wind pressure of all the roadways of the mine in real time based on the ventilation parameters of the key roadways and by using a loop wind quantity method.

In one example of the present invention, the air volume on-demand optimization regulation subsystem includes:

the adjusting branch set optimizing module is configured to analyze and adjust mutual disturbance rules among branches based on a sensitivity matrix and a second-order sensitivity matrix according to air quantity constraint of each stope and tunnel, and rapidly select an optimal adjusting branch set to meet air demand of each branch with minimum wind resistance adjustment quantity;

the rapid calculation module of the regulation scheme is configured to calculate the adjustable range of each branch of the regulation branch set and the air quantity of the required air branch in the regulation process according to the regulation branch set, and rapidly calculate the regulation scheme in the adjustable range of the regulation branch set; the regulation and control scheme comprises a regulation mode and a regulation amount so as to meet the air quantity constraint of all branches and the air consumption requirement of a stope.

In one example of the invention, the expression for the best set of adjustment branches is as follows:

wherein: i is the number of the branch needing wind; j is the number of the regulating branch; d (D) _i,j Values for the ith row, jth column position in the sensitivity matrix; ZDX _j All values for the j-th column in the sensitivity matrix.

Another object of the present invention is to provide a method for adjusting and controlling air volume of a multi-branch combined air volume adjusting and controlling system of a mine ventilation network, which includes the following steps:

s10: introducing a mine ventilation network topological structure, tunnel node elevation information, state parameters of each ventilation facility and a main ventilator characteristic curve into a ventilation volume regulating and controlling system, and rapidly calculating a tunnel wind resistance value based on ventilation parameters obtained by a ventilation monitoring subsystem;

s20: the ventilation network real-time resolving subsystem rapidly calculates a fan characteristic curve and working condition parameters, the ventilation monitoring and monitoring subsystem obtains temperature and humidity data to calculate air density of each tunnel, and the ventilation network real-time resolving subsystem resolves air quantity of each tunnel in real time based on the fan characteristic curve, the working condition parameters and the air density of each tunnel; the air volume demand of the underground main air consumption place is calculated in real time through an air volume demand dynamic calculation subsystem;

S30: judging the current calculated air quantity of the main air consumption location, if the difference value between the current calculated air quantity and the required air quantity of the main air consumption location is not in a threshold value range, analyzing the mutual disturbance relation when multiple branches are simultaneously and jointly regulated by an air quantity on-demand optimization regulation subsystem based on the position of a stope network and the air quantity requirement, and calculating the optimal efficient regulation branch set, the wind resistance adjustable range, the air quantity schedulable and on-demand regulation scheme; if the difference between the current calculated air volume and the required air volume of the main air-using location is within the threshold range, the step S10 is carried out.

In one example of the present invention, in the step S30, if the difference between the current calculated air volume and the required air volume of the primary air-using site is not within the threshold value range, the method specifically includes:

s310: according to the topological structure and the wind resistance value of the ventilation network, analyzing the sensitivity change rule in the multi-branch combined wind regulation process, establishing a sensitivity matrix and a second-order sensitivity matrix, and calculating an optimal regulation branch set by utilizing a regulation branch set optimization module;

s320: according to the second-order sensitivity matrix, fully considering the mutual disturbance rule among branches in the multi-branch combined wind regulation process, and calculating the wind resistance adjustable range of the regulating branch set; when the preferred adjusting branch set is used for combined air adjustment, calculating the schedulable air quantity of the air-requiring branch, if the air quantity value of the air-requiring branch is within the schedulable air quantity of the air-requiring branch, turning to step S330, otherwise turning to step S310;

S330: and inputting the optimal regulation branch set into a regulation scheme rapid calculation module, and rapidly calculating an optimal wind resistance regulation scheme by taking the minimum energy consumption of each main ventilator as a target according to the air quantity constraint of each branch, the high-efficiency operation area constraint of the fan and the regulation branch set adjustable range, if the scheme is feasible, accurately regulating the position of a wind regulation facility, otherwise, turning to step S310 to reselect the regulation branch set.

In one example of the present invention, in step S310, establishing the sensitivity matrix and the second order sensitivity matrix includes the steps of:

let j branch wind resistance R _j Occurrence of DeltaR _j When changing, the air quantity value Q of branch i _i Corresponding change to DeltaQ _i When |DeltaR _j When l goes to 0, the expression of sensitivity is:

the larger the value of the sensitivity is,the more obvious the influence of the wind resistance change of the branch j on the air quantity of the branch i is shown; the positive and negative sensitivity values only indicate the air quantity Q of the branch i _i Wind resistance R along with branch j _j The change trend of (2) is irrelevant to the influence degree;

the sensitivity value of each branch is written as a matrix form to be a sensitivity matrix, and for a ventilation network with N branches, after solving a sensitivity partial differential equation system, the sensitivity matrix of the ventilation network with N multiplied by N dimensions can be calculated as follows:

If a plurality of regulating branches exist, the sensitivity of other regulating branches to the air quantity of the air needing branch is affected by the change of the resistance of one branch; if different adjusting branch sets are selected, the change of the wind branch required wind quantity also has larger difference; therefore, the second-order sensitivity is utilized to represent the mutual influence relationship between the two regulation branches when the two regulation branches are combined for regulating wind; the second order sensitivity expression is:

the second-order sensitivity value is expressed as the mutual influence degree of two regulating branches, and can also be expressed as the influence degree of two regulating branches on the air quantity at the same time; the positive value indicates that the two regulating branches j and k mutually promote wind regulation, and the negative value indicates that the two regulating branches mutually inhibit wind regulation;

writing the second-order sensitivity of all branches into a matrix form which is a second-order sensitivity matrix, and solving a partial derivative on the basis of the sensitivity matrix to calculate and obtain the second-order sensitivity matrix; for a ventilation network with N branches, its second order sensitivity matrix is an nxnxn three-dimensional matrix, whose three-dimensional matrix form is:

in one example of the present invention, in step S310, calculating the optimal adjustment branch set using the adjustment branch set preference module includes the steps of:

S311: respectively selecting a plurality of double-branch combinations with positive sensitivity and mutually promoted second-order sensitivity according to the sensitivity matrix and the second-order sensitivity matrix, and taking intersection sets of branch numbers of all combinations as standby adjusting branch sets;

s312: the spare regulating branch set and the roadway of the mine settable regulating facility are intersected, the combination of regulating branch number, fixed fan number and fan number which are larger than the chord number of the remaining tree in the selected branch combination is deleted, and the selected branch number is used as the input parameter of the discrete particle swarm combination optimizing algorithm;

s313: and (3) calculating an optimal adjustment branch combination by utilizing a discrete particle swarm optimization algorithm according to the optimal target.

In one example of the present invention, in step S320, calculating the wind resistance adjustable range of the adjustment branch set and the schedulable for the required wind branch includes the steps of:

s321: calculating a sensitivity matrix according to the wind network parameter real-time calculation module, wherein 10% of the initial sensitivity is used as a critical value of each branch wind-adjusting sensitive area;

s322: comparing whether the difference between the current air quantity sensitivity value and the sensitivity area critical value meets the set precision; if yes, outputting the wind resistance value of the regulating branch and the wind quantity value of the wind branch at the moment; if not, increasing the wind resistance of the corresponding adjusting branch, and recalculating the sensitivity matrix and the wind quantity;

S323: and iterating continuously until the difference between the air quantity sensitivity value and the critical value of the sensitive area meets the set precision or reaches the maximum iteration number, and outputting the adjustable range of the wind resistance of all the adjusting branches and the schedulable of the wind demand branches.

In one example of the present invention, the step S330 includes:

s331: inputting the optimized regulation branch set, the wind resistance adjustable range and the wind demand of a main underground wind application place into a regulation scheme rapid calculation module, setting the air quantity constraint ranges of other branches except the regulation branch and the wind demand branch and the high-efficiency operation range of the fan, and establishing a ventilation network optimization model by taking the lowest power consumption of the fan as a target;

s332: the method comprises the steps that the air quantity and the wind resistance of each branch obtained by an air network parameter real-time calculation module are used as iteration initial values, the equality constraint of the node air quantity, the loop air pressure and the wind resistance is processed by using an interior point penalty function method, and the inequality constraint processing mode is that the feasible space movement is limited in the particle iteration process; the particle swarm optimization algorithm can be utilized to realize the regulation and control scheme which not only meets the wind demand requirement, but also realizes the lowest power consumption of the wind turbine in the feasible scheme.

Preferred embodiments for carrying out the present invention will be described in more detail below with reference to the attached drawings so that the features and advantages of the present invention can be easily understood.

Drawings

In order to more clearly illustrate the technical solution of the embodiments of the present invention, the following description will briefly explain the drawings of the embodiments of the present invention. Wherein the showings are for the purpose of illustrating some embodiments of the invention only and not for the purpose of limiting the same.

FIG. 1 is a schematic diagram of a multi-branch combined air volume regulation system of a mine ventilation network according to an embodiment of the invention;

FIG. 2 is a flow chart of a preferred method for regulating branches of a multi-branch joint regulation of a mine ventilation network in accordance with an embodiment of the present invention;

FIG. 3 is a flow chart of a method for adjusting the wind resistance adjusting range and the air quantity schedulable by multi-branch combined regulation of a mine ventilation network according to an embodiment of the present invention;

fig. 4 is a flowchart of a method for multi-branch joint air volume regulation of a mine ventilation network according to an embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the technical solutions of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of specific embodiments of the present invention. Like reference numerals in the drawings denote like parts. It should be noted that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.

Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like in the description and in the claims, are not used for any order, quantity, or importance, but are used for distinguishing between different elements. Likewise, the terms "a" or "an" and the like do not necessarily denote a limitation of quantity. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.

A multi-branch joint air volume regulation system of a mine ventilation network according to a first aspect of the present invention, as shown in fig. 1, includes:

A ventilation monitoring subsystem configured for monitoring downhole ventilation parameters in real-time;

the ventilation network real-time resolving subsystem is configured to resolve characteristic curves, operation condition parameters, roadway air quantity and ventilation resistance of each fan in the mine in real time based on the ventilation parameters;

the air volume demand dynamic calculation subsystem is configured to calculate the optimal proper air volume of the stope in real time based on concentration data, personnel number, temperature and humidity data and human body comfort data of gas, carbon dioxide and other harmful gases of each stope in the pit;

the air quantity optimizing and regulating subsystem is configured to analyze the mutual disturbance relation when multiple branches are simultaneously regulated in a combined mode based on the position of a stope network and the air quantity requirement, and calculate the optimal high-efficiency regulating branch set, the wind resistance adjustable range, the air quantity schedulable and the on-demand regulating scheme.

Firstly, introducing a mine ventilation network topological structure, tunnel node elevation information, state parameters of each ventilation facility and a main ventilator characteristic curve into a ventilation control air volume system, and rapidly calculating a tunnel wind resistance value based on ventilation parameters obtained by a ventilation monitoring subsystem; then, a ventilation network real-time resolving subsystem rapidly calculates a fan characteristic curve and working condition parameters, temperature and humidity data obtained by a ventilation monitoring subsystem calculate air density of each roadway, and the ventilation network real-time resolving subsystem real-time resolves air quantity of each roadway based on the fan characteristic curve, the working condition parameters and the air density of each roadway; the air volume demand of the underground main air consumption place is calculated in real time through an air volume demand dynamic calculation subsystem; finally, if the calculated air quantity of the main air consumption place is smaller than the air quantity demand, the air quantity on-demand optimization regulation subsystem analyzes the mutual disturbance relation when multiple branches are simultaneously combined to regulate and control based on the stope network position and the air quantity demand, and calculates the optimal efficient regulation branch set, the air resistance adjustable range, the air quantity schedulable and on-demand regulation scheme. The intelligent control method for the mine intelligent ventilation system realizes the accurate, efficient, stable and dynamic intelligent control of multiple main wind areas by multiple branches at the same time, and provides a multi-branch combined intelligent control method for the construction of the mine intelligent ventilation system.

The ventilation system for the combined operation of the main ventilators fully considers the mutual influence characteristics of the main ventilators and the dynamic air quantity demand of the underground key air consumption place, realizes the accurate, stable, real-time and rapid intelligent regulation and control of the underground branch air quantity by the main ventilators, monitors the working conditions of the main ventilators and the ventilation parameters of the underground key branches in real time based on the coal mine monitoring and controlling system, analyzes the harmful gas and the personnel respiratory demand in real time, dynamically calculates the air quantity demand of the air consumption place, and immediately starts the multi-ventilator combined regulation and control scheme when the difference between the current air quantity and the air quantity demand is monitored to exceed the set threshold value. And establishing a fan curve library of 0-50Hz by using a fan frequency conversion technology. And (3) rapidly resolving working condition parameters to be regulated of a plurality of main ventilators by using a loop wind pressure resolving method, searching the required frequencies of the ventilators in a curve library of the ventilators in real time, simulating working condition parameters of each ventilator after the on-demand variable frequency regulation of the plurality of ventilators and ventilation parameters of each underground roadway by using a wind network resolving technology in advance, immediately alarming when the air quantity of each underground roadway is not in an air quantity allowable range or the working condition of the ventilator is operated in an unstable region, and uploading analysis results to a ground monitoring center for personnel decision analysis. Thus realizing the accurate, stable, real-time and rapid intellectualization of a plurality of main ventilators to the underground branch air quantity.

In one example of the present invention, the ventilation monitoring and monitoring subsystem includes:

the monitoring substation is configured to adjust and set states of wind speed, wind pressure, temperature and humidity, air doors and wind windows, and collect wind quantity and wind pressure data of a fan; specifically, the monitoring substation comprises a wind speed sensor, a wind pressure sensor, a temperature and humidity sensor and other sensors, and corresponding wind speed, wind pressure, temperature and humidity information and the like are respectively monitored through the sensors so as to obtain comprehensive ventilation parameters;

the industrial Ethernet ring network module is configured to interact transmission of ventilation parameters acquired by the monitoring substation and the controller instruction;

the ground monitoring center is configured to display and store ventilation parameters and comprehensively display control schemes;

the database is configured to store all monitoring data, calculation data and scheme control information for each subsystem to call;

through the ventilation monitoring subsystem, underground ventilation parameters can be effectively monitored, monitoring data monitored by each sensor and controller instructions are interactively transmitted and stored, and the ventilation parameters and a control scheme are comprehensively displayed.

In one example of the present invention, the ventilation network real-time solution subsystem includes:

The fan working condition real-time calculation module is configured to calculate characteristic curves of all main ventilators of the mine and monitor real-time operation working conditions of all the main ventilators based on fan parameters;

the real-time wind network parameter calculation module is configured to calculate the wind quantity, density, resistance and natural wind pressure of all the roadways of the mine in real time based on the ventilation parameters of the key roadways by using a loop wind quantity method;

it can be understood that the key roadways comprise a main air inlet main roadway, an air return main roadway, air inlet roadways of each mining area, air return roadways of each mining area and positions of each main mining working face of the mine.

In one example of the present invention, the air volume on-demand optimization regulation subsystem includes:

the adjusting branch set optimizing module is configured to analyze and adjust mutual disturbance rules among branches based on a sensitivity matrix and a second-order sensitivity matrix according to air quantity constraint of each stope and tunnel, and rapidly select an optimal adjusting branch set to meet air demand of each branch with minimum wind resistance adjustment quantity;

the rapid calculation module of the regulation scheme is configured to calculate the adjustable range of each branch of the regulation branch set and the air quantity of the required air branch in the regulation process according to the regulation branch set, and rapidly calculate the regulation scheme in the adjustable range of the regulation branch set; the regulation and control scheme comprises a regulation mode and a regulation quantity so as to meet the air quantity constraint of all branches and the air consumption requirement of a stope;

In particular, as shown in fig. 2, the regulation branch set preference module specifically regulates the principle as follows:

assuming that the regulation mode is resistance-increasing regulation, firstly, respectively selecting a plurality of double-branch combinations with positive sensitivity and mutually promoted second-order sensitivity according to the sensitivity matrix and the second-order sensitivity matrix, and taking intersection sets as standby regulation branch sets for branch numbers of all combinations. And secondly, the standby adjusting branch set and a roadway of a mine settable adjusting facility are intersected, a combination of which the sum of the adjusting branch number, the fixed fan number and the fan number is larger than the chord number of the remaining tree in the selected branch combination is deleted, and the selected branch number is used as an input parameter of a discrete particle swarm optimization algorithm, so that the optimal adjusting branch set is ensured to be a branch of a mutual promotion relationship, the high efficiency of an adjusting and controlling process is ensured, and finally, the optimal adjusting branch combination is obtained by utilizing the discrete particle swarm optimization algorithm according to an optimal target.

The method for adjusting the wind resistance adjustable range of the branch set and the schedulable calculation method of the wind demand branch, as shown in fig. 3, comprises the following steps:

take the way of resistance increasing adjustment as an example. Firstly, calculating a sensitivity matrix according to a real-time wind network parameter calculation module, and taking 10% of initial sensitivity as a critical value of a wind-adjusting sensitive area of each branch. And secondly, comparing whether the difference between the current air quantity sensitivity value and the sensitivity area critical value meets the set precision. If yes, outputting the wind resistance value of the regulating branch and the wind quantity value of the wind branch at the moment; if not, increasing the wind resistance of the corresponding adjusting branch, and recalculating the sensitivity matrix and the wind quantity. And iterating until the difference between the air quantity sensitivity value and the critical value of the sensitive area meets the set precision or the maximum iteration number is reached. And finally outputting the wind resistance adjustable range of all the adjusting branches and the schedulable of the wind branches.

Specifically, the specific regulation principle of the regulation scheme rapid calculation module is as follows: :

firstly, inputting the optimized regulation branch set, the wind resistance adjustable range and the wind demand of a main underground wind application place into a regulation scheme rapid calculation module, setting the air quantity constraint ranges of other branches except the regulation branch and the wind demand branch and the high-efficiency operation range of a fan, and establishing a ventilation network optimization model by taking the lowest power consumption of the fan as a target. And secondly, taking the air quantity and the wind resistance of each branch obtained by the wind network parameter real-time resolving module as iteration initial values, and processing the equality constraint of the node air quantity, the loop air pressure and the wind resistance by using an internal point penalty function method, wherein the inequality constraint processing mode is to limit the feasible space movement in the particle iteration process. The particle swarm optimization algorithm can be utilized to realize the regulation and control scheme which not only meets the wind demand requirement, but also realizes the lowest power consumption of the wind turbine in the feasible scheme.

In one example of the invention, the expression for the best set of adjustment branches is as follows:

wherein: i is the number of the branch needing wind; j is the number of the regulating branch; d (D) _i,j Values for the ith row, jth column position in the sensitivity matrix; ZDX _j All values for the j-th column in the sensitivity matrix;

the optimal target of the optimal regulation branch set is a branch combination with the largest percentage of the position value of the branch needing wind to the sum of all regulation branch columns in the sensitivity matrix, which can ensure that the branch combination and the resistance regulation make the change of the branch needing wind quantity most efficient.

The invention provides a multi-branch combined regulation and control air quantity system of a complex mine ventilation network, which has the advantages of multi-branch cooperative regulation and control, high air regulation sensitivity, high regulation precision, high regulation efficiency and good use effect, wherein in the complex ventilation network, the ventilation parameters of a key tunnel are monitored in real time by utilizing a ventilation monitoring subsystem, and the topology structure of an induced air network is used for realizing the real-time calculation of branch air quantity; and dynamically calculating the air quantity requirement of the stope based on requirements of harmful gas, personnel respiration, worker comfort and the like. If the difference between the real-time calculated air quantity and the required air quantity is large, the air quantity of each stope is regulated and controlled in a multi-branch cooperative mode by utilizing a regulation and control scheme quick calculation module. And the sensitivity matrix is utilized to select an adjusting branch with high wind adjusting sensitivity for adjustment, and the wind quantity of the stope is adjusted as required by smaller wind resistance adjustment quantity. If the air quantity at a plurality of positions needs to be regulated and controlled simultaneously, multi-branch cooperative regulation is needed, but the wind regulation sensitivity of other regulating branches is changed to different degrees by single-branch regulation. And (3) taking the maximum percentage of the position value of the branch needing wind in the sensitivity matrix to the sum of all the adjustment branch columns as a preferable target, and applying a discrete combination optimization algorithm to optimize a plurality of adjustment branch combinations with mutually promoted sensitivity in the wind adjustment process. Meanwhile, the wind resistance adjustable range of the adjusting branch set and the air quantity schedulable of the adjusting stope are calculated, and whether the adjusting branch set can meet the air quantity requirement can be verified in advance. And finally, establishing a ventilation network optimization model by using a particle swarm optimization algorithm, and further calculating the wind resistance adjustment quantity of each adjustment branch. Finally, a reasonable regulation and control scheme is formed, so that the accurate, efficient, stable and dynamic intelligent regulation and control of multiple main wind utilization areas are realized at the same time, and a multi-branch combined intelligent regulation and control method is provided for the construction of an intelligent ventilation system of a mine.

According to a second aspect of the present invention, a method for controlling air volume of a multi-branch combined air volume control system of a mine ventilation network, as shown in fig. 4, comprises the following steps:

s10: introducing a mine ventilation network topological structure, tunnel node elevation information, state parameters of each ventilation facility and a main ventilator characteristic curve into a ventilation volume regulating and controlling system, and rapidly calculating a tunnel wind resistance value based on ventilation parameters obtained by a ventilation monitoring subsystem;

s20: the ventilation network real-time resolving subsystem rapidly calculates a fan characteristic curve and working condition parameters, the ventilation monitoring and monitoring subsystem obtains temperature and humidity data to calculate air density of each tunnel, and the ventilation network real-time resolving subsystem resolves air quantity of each tunnel in real time based on the fan characteristic curve, the working condition parameters and the air density of each tunnel; the air volume demand of the underground main air consumption place is calculated in real time through an air volume demand dynamic calculation subsystem;

s30: judging the current calculated air quantity of the main air consumption location, if the difference value between the current calculated air quantity and the required air quantity of the main air consumption location is not in a threshold value range, analyzing the mutual disturbance relation when multiple branches are simultaneously and jointly regulated by an air quantity on-demand optimization regulation subsystem based on the position of a stope network and the air quantity requirement, and calculating the optimal efficient regulation branch set, the wind resistance adjustable range, the air quantity schedulable and on-demand regulation scheme; if the difference between the current calculated air volume and the required air volume of the main air-using location is within the threshold range, the step S10 is carried out.

The method for regulating and controlling the air quantity aims at a ventilation system in which a plurality of main ventilators are operated in a combined mode, fully considers the mutual influence characteristics of the plurality of ventilators and the air quantity dynamic requirement of an underground key air consumption place, realizes accurate, stable, real-time and rapid intelligent regulation and control of the underground branch air quantity by the plurality of main ventilators, monitors the working conditions of the plurality of ventilators and the ventilation parameters of the underground key branches in real time based on a coal mine monitoring and controlling system, analyzes the harmful gas and the personnel respiratory requirement in real time, dynamically calculates the air quantity required by the air consumption place, and immediately starts the multi-ventilator combined regulation and control scheme when the difference between the current air quantity and the required air quantity is monitored to exceed a set threshold value. And establishing a fan curve library of 0-50Hz by using a fan frequency conversion technology. And (3) rapidly resolving working condition parameters to be regulated of a plurality of main ventilators by using a loop wind pressure resolving method, searching the required frequencies of the ventilators in a curve library of the ventilators in real time, simulating working condition parameters of each ventilator after the on-demand variable frequency regulation of the plurality of ventilators and ventilation parameters of each underground roadway by using a wind network resolving technology in advance, immediately alarming when the air quantity of each underground roadway is not in an air quantity allowable range or the working condition of the ventilator is operated in an unstable region, and uploading analysis results to a ground monitoring center for personnel decision analysis. Thus realizing the accurate, stable, real-time and rapid intellectualization of a plurality of main ventilators to the underground branch air quantity.

In one example of the present invention, in the step S30, if the difference between the current calculated air volume and the required air volume of the primary air-using site is not within the threshold value range, the method specifically includes:

s310: according to the topological structure and the wind resistance value of the ventilation network, analyzing the sensitivity change rule in the multi-branch combined wind regulation process, establishing a sensitivity matrix and a second-order sensitivity matrix, and calculating an optimal regulation branch set by utilizing a regulation branch set optimization module;

s320: according to the second-order sensitivity matrix, fully considering the mutual disturbance rule among branches in the multi-branch combined wind regulation process, and calculating the wind resistance adjustable range of the regulating branch set; when the preferred adjusting branch set is used for combined air adjustment, calculating the schedulable air quantity of the air-requiring branch, if the air quantity value of the air-requiring branch is within the schedulable air quantity of the air-requiring branch, turning to step S330, otherwise turning to step S310;

s330: and inputting the optimal regulation branch set into a regulation scheme rapid calculation module, and rapidly calculating an optimal wind resistance regulation scheme by taking the minimum energy consumption of each main ventilator as a target according to the air quantity constraint of each branch, the high-efficiency operation area constraint of the fan and the regulation branch set adjustable range, if the scheme is feasible, accurately regulating the position of a wind regulation facility, otherwise, turning to step S310 to reselect the regulation branch set.

In one example of the present invention, in step S310, establishing the sensitivity matrix and the second order sensitivity matrix includes the steps of:

in step S310, the step of establishing the sensitivity matrix and the second-order sensitivity matrix includes the steps of:

let j branch wind resistance R _j Occurrence of DeltaR _j When changing, the air quantity value Q of branch i _i Corresponding change to DeltaQ _i When |DeltaR _j When l goes to 0, the expression of sensitivity is:

the larger the sensitivity value is, the more obvious the influence of the wind resistance change of the branch j on the air quantity of the branch i is shown; the positive and negative sensitivity values only indicate the air quantity Q of the branch i _i Wind resistance R along with branch j _j The change trend of (2) is irrelevant to the influence degree;

the sensitivity value of each branch is written as a matrix form to be a sensitivity matrix, and for a ventilation network with N branches, after solving a sensitivity partial differential equation system, the sensitivity matrix of the ventilation network with N multiplied by N dimensions can be calculated as follows:

if a plurality of regulating branches exist, the sensitivity of other regulating branches to the air quantity of the air needing branch is affected by the change of the resistance of one branch; if different adjusting branch sets are selected, the change of the wind branch required wind quantity also has larger difference; therefore, the second-order sensitivity is utilized to represent the mutual influence relationship between the two regulation branches when the two regulation branches are combined for regulating wind; the second order sensitivity expression is:

The second-order sensitivity value is expressed as the mutual influence degree of two regulating branches, and can also be expressed as the influence degree of two regulating branches on the air quantity at the same time; the positive value indicates that the two regulating branches j and k mutually promote wind regulation, and the negative value indicates that the two regulating branches mutually inhibit wind regulation;

writing the second-order sensitivity of all branches into a matrix form which is a second-order sensitivity matrix, and solving a partial derivative on the basis of the sensitivity matrix to calculate and obtain the second-order sensitivity matrix; for a ventilation network with N branches, its second order sensitivity matrix is an nxnxn three-dimensional matrix, whose three-dimensional matrix form is:

in one example of the present invention, in step S310, calculating the optimal adjustment branch set using the adjustment branch set preference module includes the steps of:

s311: respectively selecting a plurality of double-branch combinations with positive sensitivity and mutually promoted second-order sensitivity according to the sensitivity matrix and the second-order sensitivity matrix, and taking intersection sets of branch numbers of all combinations as standby adjusting branch sets;

s312: the spare regulating branch set and the roadway of the mine settable regulating facility are intersected, the combination of regulating branch number, fixed fan number and fan number which are larger than the chord number of the remaining tree in the selected branch combination is deleted, and the selected branch number is used as the input parameter of the discrete particle swarm combination optimizing algorithm;

S313: and (3) calculating an optimal adjustment branch combination by utilizing a discrete particle swarm optimization algorithm according to the optimal target.

In one example of the present invention, in step S320, calculating the wind resistance adjustable range of the adjustment branch set and the schedulable for the required wind branch includes the steps of:

s321: calculating a sensitivity matrix according to the wind network parameter real-time calculation module, wherein 10% of the initial sensitivity is used as a critical value of each branch wind-adjusting sensitive area;

s322: comparing whether the difference between the current air quantity sensitivity value and the sensitivity area critical value meets the set precision; if yes, outputting the wind resistance value of the regulating branch and the wind quantity value of the wind branch at the moment; if not, increasing the wind resistance of the corresponding adjusting branch, and recalculating the sensitivity matrix and the wind quantity;

s323: and iterating continuously until the difference between the air quantity sensitivity value and the critical value of the sensitive area meets the set precision or reaches the maximum iteration number, and outputting the adjustable range of the wind resistance of all the adjusting branches and the schedulable of the wind demand branches.

In one example of the present invention, the step S330 includes:

s331: inputting the optimized regulation branch set, the wind resistance adjustable range and the wind demand of a main underground wind application place into a regulation scheme rapid calculation module, setting the air quantity constraint ranges of other branches except the regulation branch and the wind demand branch and the high-efficiency operation range of the fan, and establishing a ventilation network optimization model by taking the lowest power consumption of the fan as a target;

S332: the method comprises the steps that the air quantity and the wind resistance of each branch obtained by an air network parameter real-time calculation module are used as iteration initial values, the equality constraint of the node air quantity, the loop air pressure and the wind resistance is processed by using an interior point penalty function method, and the inequality constraint processing mode is that the feasible space movement is limited in the particle iteration process; the particle swarm optimization algorithm can be utilized to realize the regulation and control scheme which not only meets the wind demand requirement, but also realizes the lowest power consumption of the wind turbine in the feasible scheme.

According to the air quantity regulating and controlling method of the multi-branch combined air quantity regulating and controlling system of the mine ventilation network, provided by the invention, the multi-branch combined air quantity regulating and controlling system of the mine complex ventilation network is provided, the multi-branch combined air quantity regulating and controlling system is high in air regulating sensitivity, high in regulating accuracy, high in regulating efficiency and good in using effect, in the complex ventilation network, the ventilation parameters of key roadways are monitored in real time by utilizing a ventilation monitoring subsystem, and the topology structure of an induced air network is used for realizing the real-time calculation of branch air quantity; and dynamically calculating the air quantity requirement of the stope based on requirements of harmful gas, personnel respiration, worker comfort and the like. If the difference between the real-time calculated air quantity and the required air quantity is large, the air quantity of each stope is regulated and controlled in a multi-branch cooperative mode by utilizing a regulation and control scheme quick calculation module. And the sensitivity matrix is utilized to select an adjusting branch with high wind adjusting sensitivity for adjustment, and the wind quantity of the stope is adjusted as required by smaller wind resistance adjustment quantity. If the air quantity at a plurality of positions needs to be regulated and controlled simultaneously, multi-branch cooperative regulation is needed, but the wind regulation sensitivity of other regulating branches is changed to different degrees by single-branch regulation. And (3) taking the maximum percentage of the position value of the branch needing wind in the sensitivity matrix to the sum of all the adjustment branch columns as a preferable target, and applying a discrete combination optimization algorithm to optimize a plurality of adjustment branch combinations with mutually promoted sensitivity in the wind adjustment process. Meanwhile, the wind resistance adjustable range of the adjusting branch set and the air quantity schedulable of the adjusting stope are calculated, and whether the adjusting branch set can meet the air quantity requirement can be verified in advance. And finally, establishing a ventilation network optimization model by using a particle swarm optimization algorithm, and further calculating the wind resistance adjustment quantity of each adjustment branch. Finally, a reasonable regulation and control scheme is formed, so that the accurate, efficient, stable and dynamic intelligent regulation and control of multiple main wind utilization areas are realized at the same time, and a multi-branch combined intelligent regulation and control method is provided for the construction of an intelligent ventilation system of a mine.

While exemplary embodiments of the multi-branch joint air volume control system and method for mine ventilation networks of the present invention have been described in detail hereinabove with reference to preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to the specific embodiments described above without departing from the spirit of the invention, and various technical features and structures of the present invention may be combined without departing from the scope of the invention, which is defined in the appended claims.

Claims (10)

1. A multi-branch combined air volume regulation and control system for a mine ventilation network, comprising:

a ventilation monitoring subsystem configured for monitoring downhole ventilation parameters in real-time;

the ventilation network real-time resolving subsystem is configured to resolve characteristic curves, operation condition parameters, roadway air quantity and ventilation resistance of each fan in the mine in real time based on the ventilation parameters;

the air volume demand dynamic calculation subsystem is configured to calculate the optimal proper air volume of the stope in real time based on concentration data, personnel number, temperature and humidity data and human body comfort data of gas, carbon dioxide and other harmful gases of each stope in the pit;

The air quantity optimizing and regulating subsystem is configured to analyze the mutual disturbance relation when multiple branches are simultaneously regulated in a combined mode based on the position of a stope network and the air quantity requirement, and calculate the optimal high-efficiency regulating branch set, the wind resistance adjustable range, the air quantity schedulable and the on-demand regulating scheme.

2. The multi-branch joint air volume control system of a mine ventilation network according to claim 1, wherein,

the ventilation network real-time solution subsystem comprises:

the fan working condition real-time calculation module is configured to calculate characteristic curves of all main ventilators of the mine and monitor real-time operation working conditions of all the main ventilators based on fan parameters;

and the real-time wind network parameter calculation module is configured to calculate the wind quantity, density, resistance and natural wind pressure of all the roadways of the mine in real time based on the ventilation parameters of the key roadways and by using a loop wind quantity method.

3. The multi-branch joint air volume control system of a mine ventilation network according to claim 1, wherein,

the air quantity on-demand optimization regulation subsystem comprises:

the adjusting branch set optimizing module is configured to analyze and adjust mutual disturbance rules among branches based on a sensitivity matrix and a second-order sensitivity matrix according to air quantity constraint of each stope and tunnel, and rapidly select an optimal adjusting branch set to meet air demand of each branch with minimum wind resistance adjustment quantity;

The rapid calculation module of the regulation scheme is configured to calculate the adjustable range of each branch of the regulation branch set and the air quantity of the required air branch in the regulation process according to the regulation branch set, and rapidly calculate the regulation scheme in the adjustable range of the regulation branch set; the regulation and control scheme comprises a regulation mode and a regulation amount so as to meet the air quantity constraint of all branches and the air consumption requirement of a stope.

4. The multi-branch joint air volume control system for mine ventilation network according to claim 3, wherein,

the expression for the best regulatory branch set is as follows:

wherein: i is the number of the branch needing wind; j is the number of the regulating branch; d (D) _i,j Values for the ith row, jth column position in the sensitivity matrix; ZDX _j All values for the j-th column in the sensitivity matrix.

5. A method for regulating air quantity of a multi-branch joint air quantity regulating system of a mine ventilation network according to any one of claims 1 to 4, comprising the steps of:

s10: introducing a mine ventilation network topological structure, tunnel node elevation information, state parameters of each ventilation facility and a main ventilator characteristic curve into a ventilation volume regulating and controlling system, and rapidly calculating a tunnel wind resistance value based on ventilation parameters obtained by a ventilation monitoring subsystem;

S20: the ventilation network real-time resolving subsystem rapidly calculates a fan characteristic curve and working condition parameters, the ventilation monitoring and monitoring subsystem obtains temperature and humidity data to calculate air density of each tunnel, and the ventilation network real-time resolving subsystem resolves air quantity of each tunnel in real time based on the fan characteristic curve, the working condition parameters and the air density of each tunnel; the air volume demand of the underground main air consumption place is calculated in real time through an air volume demand dynamic calculation subsystem;

s30: judging the current calculated air quantity of the main air consumption location, if the difference value between the current calculated air quantity and the required air quantity of the main air consumption location is not in a threshold value range, analyzing the mutual disturbance relation when multiple branches are simultaneously and jointly regulated by an air quantity on-demand optimization regulation subsystem based on the position of a stope network and the air quantity requirement, and calculating the optimal efficient regulation branch set, the wind resistance adjustable range, the air quantity schedulable and on-demand regulation scheme; if the difference between the current calculated air volume and the required air volume of the main air-using location is within the threshold range, the step S10 is carried out.

6. The method for multi-branch joint air volume regulation and control of a mine ventilation network of claim 5, wherein the method comprises the steps of,

in the step S30, if the difference between the current calculated air volume and the required air volume of the main air-using location is not within the threshold value range, the method specifically includes:

S310: according to the topological structure and the wind resistance value of the ventilation network, analyzing the sensitivity change rule in the multi-branch combined wind regulation process, establishing a sensitivity matrix and a second-order sensitivity matrix, and calculating an optimal regulation branch set by utilizing a regulation branch set optimization module;

s320: according to the second-order sensitivity matrix, fully considering the mutual disturbance rule among branches in the multi-branch combined wind regulation process, and calculating the wind resistance adjustable range of the regulating branch set; when the preferred adjusting branch set is used for combined air adjustment, calculating the schedulable air quantity of the air-requiring branch, if the air quantity value of the air-requiring branch is within the schedulable air quantity of the air-requiring branch, turning to step S330, otherwise turning to step S310;

s330: and inputting the optimal regulation branch set into a regulation scheme rapid calculation module, and rapidly calculating an optimal wind resistance regulation scheme by taking the minimum energy consumption of each main ventilator as a target according to the air quantity constraint of each branch, the high-efficiency operation area constraint of the fan and the regulation branch set adjustable range, if the scheme is feasible, accurately regulating the position of a wind regulation facility, otherwise, turning to step S310 to reselect the regulation branch set.

7. The method for multi-branch joint air volume regulation and control of a mine ventilation network of claim 6, wherein the method comprises the steps of,

In step S310, the step of establishing the sensitivity matrix and the second-order sensitivity matrix includes the steps of:

let j branch wind resistance R _j Occurrence of DeltaR _j When changing, the air quantity value Q of branch i _i Corresponding change to DeltaQ _i When |DeltaR _j When l goes to 0, the expression of sensitivity is:

the larger the sensitivity value is, the more obvious the influence of the wind resistance change of the branch j on the air quantity of the branch i is shown; the positive and negative sensitivity values only indicate the air quantity Q of the branch i _i Wind resistance R along with branch j _j The change trend of (2) is irrelevant to the influence degree;

the sensitivity value of each branch is written as a matrix form to be a sensitivity matrix, and for a ventilation network with N branches, after solving a sensitivity partial differential equation system, the sensitivity matrix of the ventilation network with N multiplied by N dimensions can be calculated as follows:

if a plurality of regulating branches exist, the sensitivity of other regulating branches to the air quantity of the air needing branch is affected by the change of the resistance of one branch; if different adjusting branch sets are selected, the change of the wind branch required wind quantity also has larger difference; therefore, the second-order sensitivity is utilized to represent the mutual influence relationship between the two regulation branches when the two regulation branches are combined for regulating wind; the second order sensitivity expression is:

the second-order sensitivity value is expressed as the mutual influence degree of two regulating branches, and can also be expressed as the influence degree of two regulating branches on the air quantity at the same time; the positive value indicates that the two regulating branches j and k mutually promote wind regulation, and the negative value indicates that the two regulating branches mutually inhibit wind regulation;

Writing the second-order sensitivity of all branches into a matrix form which is a second-order sensitivity matrix, and solving a partial derivative on the basis of the sensitivity matrix to calculate and obtain the second-order sensitivity matrix; for a ventilation network with N branches, its second order sensitivity matrix is an nxnxn three-dimensional matrix, whose three-dimensional matrix form is:

8. the method for multi-branch joint air volume regulation and control of a mine ventilation network of claim 6, wherein the method comprises the steps of,

in step S310, calculating the optimal adjustment branch set using the adjustment branch set optimization module includes the steps of:

s311: respectively selecting a plurality of double-branch combinations with positive sensitivity and mutually promoted second-order sensitivity according to the sensitivity matrix and the second-order sensitivity matrix, and taking intersection sets of branch numbers of all combinations as standby adjusting branch sets;

s312: the spare regulating branch set and the roadway of the mine settable regulating facility are intersected, the combination of regulating branch number, fixed fan number and fan number which are larger than the chord number of the remaining tree in the selected branch combination is deleted, and the selected branch number is used as the input parameter of the discrete particle swarm combination optimizing algorithm;

s313: and (3) calculating an optimal adjustment branch combination by utilizing a discrete particle swarm optimization algorithm according to the optimal target.

9. The method for multi-branch joint air volume control of a mine ventilation network of claim 6, wherein in step S320, calculating the adjustable range of wind resistance and the schedulability of the required wind branch of the set of adjustment branches comprises the steps of:

s321: calculating a sensitivity matrix according to the wind network parameter real-time calculation module, wherein 10% of the initial sensitivity is used as a critical value of each branch wind-adjusting sensitive area;

s322: comparing whether the difference between the current air quantity sensitivity value and the sensitivity area critical value meets the set precision; if yes, outputting the wind resistance value of the regulating branch and the wind quantity value of the wind branch at the moment; if not, increasing the wind resistance of the corresponding adjusting branch, and recalculating the sensitivity matrix and the wind quantity;

s323: and iterating continuously until the difference between the air quantity sensitivity value and the critical value of the sensitive area meets the set precision or reaches the maximum iteration number, and outputting the adjustable range of the wind resistance of all the adjusting branches and the schedulable of the wind demand branches.

10. The method for multi-branch joint air volume regulation and control of a mine ventilation network of claim 6, wherein the method comprises the steps of,

the step S330 includes:

s331: inputting the optimized regulation branch set, the wind resistance adjustable range and the wind demand of a main underground wind application place into a regulation scheme rapid calculation module, setting the air quantity constraint ranges of other branches except the regulation branch and the wind demand branch and the high-efficiency operation range of the fan, and establishing a ventilation network optimization model by taking the lowest power consumption of the fan as a target;

S332: the method comprises the steps that the air quantity and the wind resistance of each branch obtained by an air network parameter real-time calculation module are used as iteration initial values, the equality constraint of the node air quantity, the loop air pressure and the wind resistance is processed by using an interior point penalty function method, and the inequality constraint processing mode is that the feasible space movement is limited in the particle iteration process; the particle swarm optimization algorithm can be utilized to realize the regulation and control scheme which not only meets the wind demand requirement, but also realizes the lowest power consumption of the wind turbine in the feasible scheme.