Optimal fan selection method, device and equipment for multi-stage station ventilation system

Abstract

The present application discloses a method, device, and equipment for optimizing the selection of fans for a multi-stage station ventilation system. The method includes: determining the installed air volume value of each installed branch based on the installed air volume range and the set air volume interval of the installed branches of the multi-stage station ventilation system; performing ventilation network solution on the installed air volume value of each installed branch using a fixed air volume solution algorithm to determine the initial installed air volume distribution value and the initial installed branch adjustment air pressure value; determining the virtual installed air volume and virtual installed air pressure of each fan based on the initial installed air volume distribution value, the initial installed branch adjustment air pressure value, and the set optimization model; and obtaining a fan selection scheme based on the virtual installed air volume and virtual installed air pressure of each fan. In this way, the fan selection scheme for the multi-stage station ventilation system can be intelligently determined, and the air pressure balance requirements of the multi-stage station ventilation system can be met, thereby effectively improving the operational reliability of the system.

Description

Technical Field

[0001] The present application relates to the field of ventilation control, and in particular to a method, device and equipment for optimizing fan selection in a multi-stage station ventilation system. Background Art

[0002] The purpose of mine ventilation is to supply sufficient fresh air to the mining area, discharge the polluted air underground to the surface in time, improve the mine ventilation environment, strengthen safety production standards, and create a good and comfortable working environment for underground workers.

[0003] Compared with the large main fan ventilation system, the multi-fan station ventilation system is more controllable and is more commonly used in metal mines. The multi-fan station ventilation system refers to an engineering facility system in which multiple air intake and return air stations pressurize fresh air from the ground into the working mining area and discharge polluted air out of the mine. Among them, the series and parallel connection of multiple fans and the cascade connection of multiple stations make the ventilation system more adjustable and controllable, improve the efficiency of the ventilation system and reduce ventilation energy consumption. However, the multi-stage station ventilation system also introduces new problems. When optimizing the ventilation fans, the optimization of the multi-stage station fans needs to deal with the problem of unbalanced wind pressure redistribution.

[0004] In the prior art, fixed air volume algorithms distribute all unbalanced wind pressures to the residual tree branches of the fixed air volume type, while the spanning tree branches of the fixed air volume type are not assigned unbalanced wind pressure values. As a result, the spanning tree branches of the fixed air volume type are not qualified for installation and degenerate into general branches, thus failing to achieve the goal of selecting fans for multi-stage stations. Furthermore, if the installation location is not set properly or the unbalanced wind pressure distribution is not reasonable, some fans may experience negative pressure, making fan selection impossible. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a method, device and equipment for optimizing the selection of fans for a multi-stage station ventilation system, aiming to effectively improve the wind pressure balance of the multi-stage station ventilation system.

[0006] The technical solution of the embodiment of the present application is implemented as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for optimizing fan selection in a multi-stage station ventilation system, comprising:

[0008] Determine the installed air volume value of each installed branch according to the installed air volume range and the set air volume interval of the installed branch of the multi-stage station ventilation system;

[0009] Use the fixed air volume solution algorithm to perform ventilation network solution on the installed air volume value of each installed branch, and determine the initial installed air volume distribution value and the initial installed branch adjustment air pressure value;

[0010] Determining a virtual installed air volume and a virtual installed air pressure of each wind turbine based on the initial installed air volume distribution value, the initial installed branch adjustment air pressure value, and a set optimization model;

[0011] Obtaining a fan selection scheme based on the virtual installed air volume and the virtual installed air pressure of each fan;

[0012] Among them, the set optimization model is a multi-objective optimization function, which includes the following optimization objectives: minimum ventilation fan power target, negative pressure balance target of the same-level station and best matching fan pressure target. The decision variables of the multi-objective optimization function include: optimal fan pressure range deviation of the installed branch, upper limit deviation of the optimal fan pressure range of the installed branch, lower limit deviation of the optimal fan pressure range of the installed branch, positive deviation of the installed wind pressure and negative deviation of the installed fan.

[0013] In some embodiments, obtaining a wind turbine selection scheme based on the virtual installed air volume and the virtual installed air pressure of each wind turbine includes:

[0014] Determining a roadway wind resistance characteristic curve of an installed branch based on the virtual installed air volume and the virtual installed wind pressure of each fan;

[0015] According to the wind resistance characteristic curve of the roadway of the installed branch, the air volume-wind pressure characteristic curves of the fans in the fan library are queried one by one to determine the actual operating conditions of the fans;

[0016] Selecting a candidate wind turbine at the installation location based on the actual operating conditions;

[0017] Rank candidate fans based on fan performance indicators;

[0018] Based on the sorting results, the air distribution effect of the multi-stage station ventilation system is verified to obtain a fan selection scheme that meets the selection requirements. The fan selection scheme includes at least one of the following: fan model, installation angle, number of fans and series-parallel arrangement.

[0019] In some embodiments, the set optimization model is as follows:

[0020]

[0021]

[0022] Among them, Z is the optimization target, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, F is the set of all installed branches f, q f is the fan air volume of the installed branch f, h f is the fan pressure of the installed branch f, is the positive deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station, is the negative deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station, is the upper limit deviation of the optimal fan pressure range for installed branch f, h f is the lower limit deviation of the optimal fan pressure range of the installed branch f, N is the number of branches of the ventilation network, M is the number of independent circuits of the ventilation network, b ij Indicates the relationship between the installed branch and the independent circuit, h j is the algebraic sum of the wind pressures of the j-th installed branch, Δh j is the wind pressure adjustment value of the j-th installed branch, Δh j,fmin is the lower limit of the adjustable fan pressure of the j-th installed branch, Δh j,fmax is the upper limit of the adjustable fan pressure of the j-th installed branch, h f,max is the upper limit of the optimal fan pressure for the installed branch f, h f,min is the lower limit of the optimal fan pressure for the installed branch f, q f′ is the fan air volume at the reference installation point f′, h f′ is the wind pressure of the fan at the reference installation point f′.

[0023] In some embodiments, selecting a candidate wind turbine at the installation location based on the actual operating conditions includes:

[0024] Based on the actual operating conditions, a fan whose operating efficiency is greater than or equal to a first threshold and whose operating wind pressure is less than or equal to a second threshold is selected as a candidate fan.

[0025] In some embodiments, ranking the candidate wind turbines based on wind turbine performance indicators includes:

[0026] The candidate fans are ranked based on at least one of the fan operation efficiency, the fan operating air volume, and the fan operating air pressure.

[0027] In some embodiments, verifying the air distribution effect of the multi-stage station ventilation system based on the sorting results to obtain a fan selection scheme that meets the selection requirements includes:

[0028] Based on the sorting results, a fan selection scheme to be confirmed is generated, and the air volume distribution calculation is performed on the fan selection scheme to be confirmed to determine whether the calculation result meets the installed air volume value of each installed branch. If so, the fan selection scheme to be confirmed is used as the fan selection scheme.

[0029] In some embodiments, the method further comprises:

[0030] Output the fan selection plan.

[0031] In a second aspect, an embodiment of the present application provides a device for optimizing fan selection in a multi-stage station ventilation system, comprising:

[0032] A first determining module is configured to determine an installed air volume value of each installed branch according to an installed air volume range of the installed branches of the multi-stage station ventilation system and a set air volume interval;

[0033] The second determination module is used to perform ventilation network solution on the installed air volume value of each installed branch using a fixed air volume solution algorithm to determine an initial installed air volume distribution value and an initial installed branch regulated air pressure value;

[0034] a third determining module, configured to determine a virtual installed air volume and a virtual installed air pressure of each wind turbine based on the initial installed air volume allocation value, the initial installed branch adjustment air pressure value, and a set optimization model;

[0035] a fan selection module, configured to obtain a fan selection scheme based on the virtual installed air volume and the virtual installed air pressure of each fan;

[0036] Among them, the set optimization model is a multi-objective optimization function, which includes the following optimization objectives: minimum ventilation fan power target, negative pressure balance target of the same-level station and best matching fan pressure target. The decision variables of the multi-objective optimization function include: optimal fan pressure range deviation of the installed branch, upper limit deviation of the optimal fan pressure range of the installed branch, lower limit deviation of the optimal fan pressure range of the installed branch, positive deviation of the installed wind pressure and negative deviation of the installed fan.

[0037] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein the processor, when running the computer program, executes the steps of the method described in the first aspect of the embodiment of the present application.

[0038] In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method described in the first aspect of the embodiment of the present application are implemented.

[0039] The technical solution provided by the embodiment of the present application determines the installed air volume value of each installed branch according to the installed air volume range of the installed branches of the multi-stage station ventilation system and the set air volume interval; uses a fixed air volume solution algorithm to perform ventilation network solution on the installed air volume value of each installed branch to determine the initial installed air volume allocation value and the initial installed branch adjustment air pressure value; based on the initial installed air volume allocation value, the initial installed branch adjustment air pressure value and the set optimization model, determines the virtual installed air volume and virtual installed air pressure of each fan; and obtains a fan selection scheme based on the virtual installed air volume and virtual installed air pressure of each fan. In this way, the fan selection scheme of the multi-stage station ventilation system can be intelligently determined, and the air pressure balance requirement of the multi-stage station ventilation system can be met, thereby effectively improving the operational reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for optimizing fan selection in a multi-stage station ventilation system according to an embodiment of the present application;

[0041] Figure 2 This is a flow chart of a method for optimizing fan selection in a multi-stage station ventilation system according to an embodiment of the present application;

[0042] Figure 3 This is a schematic diagram of the ventilation network structure of a multi-stage station ventilation system in an application example of this application;

[0043] Figure 4 This is a schematic diagram of the structure of a fan optimization selection device for a multi-stage station ventilation system according to an embodiment of the present application;

[0044] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] The present application will be described in further detail below with reference to the accompanying drawings and embodiments.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0047] The embodiment of the present application provides a method for optimizing the selection of fans for a multi-stage station ventilation system, which can be applied to electronic devices with data processing capabilities, such as notebooks, desktop computers, or servers, to intelligently determine the fan selection scheme for a multi-stage station ventilation system. Figure 1 As shown, the method includes:

[0048] Step 101 : determining the installed air volume value of each installed branch according to the installed air volume range of the installed branches of the multi-stage station ventilation system and the set air volume interval.

[0049] It is understood that a multi-stage station ventilation system may include multiple stages of stations, each of which may include at least one installed branch (also known as a fan branch). When planning and designing a multi-stage station ventilation system, the number of installed branches and the set air volume interval for each stage of the station may be predetermined. Based on the total air volume requirement of the system, the installed air volume range of each installed branch may be obtained. The installed air volume value of each installed branch may then be determined based on the installed air volume range of each installed branch and the set air volume interval.

[0050] Step 102 : Using a fixed air volume solution algorithm to perform ventilation network solution on the installed air volume value of each installed branch, and determine an initial installed air volume distribution value and an initial installed branch regulated air pressure value.

[0051] Here, a fixed air volume solution algorithm can be used, and according to the installed air volume value of each installed branch determined in step 101, the ventilation network of the multi-stage station ventilation system is solved to obtain the initial installed air volume distribution value and the initial installed branch adjustment air pressure value, that is, to determine the initial state of the air volume distribution of the ventilation network and the initial adjustment air pressure value of the installed branch.

[0052] Step 103 : determining the virtual installed air volume and virtual installed air pressure of each wind turbine based on the initial installed air volume distribution value, the initial installed branch adjustment air pressure value, and the set optimization model.

[0053] In an embodiment of the present application, the set optimization model is a multi-objective optimization function, which includes the following optimization objectives: minimum ventilation fan power target, negative pressure balance target of the same-level station and best matching fan pressure target. The decision variables of the multi-objective optimization function include: optimal fan pressure range deviation of the installed branch, upper limit deviation of the optimal fan pressure range of the installed branch, lower limit deviation of the optimal fan pressure range of the installed branch, positive deviation of the installed pressure and negative deviation of the installed fan.

[0054] It can be understood that in the embodiment of the present application, the set optimization model takes the fan air volume as a given condition, and takes the minimum ventilation fan power, negative pressure balance of the same level stations and the best matching fan pressure as the comprehensive optimization goals to determine the virtual installed air volume and virtual installed air pressure of each fan.

[0055] Step 104 : obtaining a fan selection scheme based on the virtual installed air volume and the virtual installed air pressure of each fan.

[0056] It can be understood that the method of the embodiment of the present application can select fans based on the virtual installed air volume and virtual installed air pressure of each fan obtained by the aforementioned optimization model to obtain a fan selection scheme, and then intelligently determine the fan selection scheme for the multi-stage station ventilation system, and meet the comprehensive optimization goals of wind pressure balance, minimum ventilation fan power and optimal matching of fan wind pressure of the multi-stage station ventilation system, thereby effectively improving the operating reliability of the system.

[0057] In some embodiments, the set optimization model is as follows:

[0058]

[0059]

[0060] Where Z is the optimization target, ω1 is the first weight coefficient (i.e., the weight coefficient of the ventilation fan power minimum target), ω2 is the second weight coefficient (i.e., the weight coefficient of the negative pressure balance target of the same-level station), ω3 is the third weight coefficient (i.e., the weight coefficient of the best matching fan wind pressure target), F is the set of all installed branches f, q f is the fan air volume of the installed branch f, h f is the fan pressure of the installed branch f, is the positive deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station, is the negative deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station, is the upper limit deviation of the optimal fan pressure range for installed branch f, h f is the lower limit deviation of the optimal fan pressure range of the installed branch f, N is the number of branches of the ventilation network, M is the number of independent circuits of the ventilation network, b ij Indicates the relationship between the installed branch and the independent circuit, h j is the algebraic sum of the wind pressures of the j-th installed branch, Δh j is the wind pressure adjustment value of the j-th installed branch, Δh j,fmin is the lower limit of the adjustable fan pressure of the j-th installed branch, Δh j,fmax is the upper limit of the adjustable fan pressure of the j-th installed branch, h f,max is the upper limit of the optimal fan pressure for the installed branch f, h f,min is the lower limit of the optimal fan pressure for installed branch f.

[0061] It should be noted that in the above formula, st is the abbreviation of subject to (such that), which means subject to constraints. f′ is the fan air volume at the reference installation point f′, h f′ is the wind pressure of the fan at the reference installation point f′.

[0062] In one implementation, the optimization model of the multi-stage station ventilation system can be expressed as:

[0063] min Z=ω1z1+ω2z2+ω3z3+ω4z4 (1)

[0064] Among them, z1 represents the minimum power target of the ventilation fan;

[0065] z2 represents the negative pressure balance target of the same-level station;

[0066] z3 represents the best matching fan wind pressure target;

[0067] z4 represents the best matching fan air volume target;

[0068] ω1 represents the weight coefficient of the ventilation fan power minimum target;

[0069] ω2 represents the weight coefficient of the negative pressure balance target of the same-level station;

[0070] ω3 represents the weight coefficient of the best matching wind turbine wind pressure target;

[0071] ω4 represents the weight coefficient of the optimal matching fan air volume target.

[0072] The following are descriptions of the above-mentioned priority objectives:

[0073] (1) Minimum ventilation fan power target

[0074] The minimum ventilation fan power target can be expressed as

[0075]

[0076] Where F is the set of all installed branches f, including main fans and auxiliary fans;

[0077] q f is the fan air volume of the installed branch f;

[0078] h f is the wind pressure of the fan in the installed branch f.

[0079] (2) Negative pressure balance targets at all levels of stations

[0080] The negative pressure balance target of each level of stations can be expressed as

[0081]

[0082] in, is the positive deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station;

[0083] It is the negative deviation variable between the installed wind pressure at the installation point of the installed branch f in each level of stations and the installed wind pressure at the reference installation point f′.

[0084] When the negative pressure of the station is determined proportionally according to the installed air volume, and Satisfy the following constraints

[0085]

[0086] In particular, the negative pressure balance objective function of the same-level stations can be expressed as

[0087]

[0088] Wherein, L is the number of stages of the multi-stage station;

[0089] K is the number of installed points of the same-level station;

[0090] is the positive deviation variable between the installed wind pressure at installation point k and the installed wind pressure at installation point K in the level l station;

[0091] It is the negative deviation variable between the installed wind pressure at installation point k and the installed wind pressure at installation point K in the level l station.

[0092] and Satisfy the following constraints

[0093]

[0094] (3) Best matching of fan wind pressure target

[0095] The optimal matching wind turbine wind pressure target can be expressed as

[0096]

[0097] Where Δh f Indicates the deviation of the optimal fan pressure range of the fth branch (installed branch).

[0098] The deviation of the optimal fan pressure range of branch f (installed branch) can be calculated by the following formula

[0099]

[0100] Among them, h f,min is the lower limit of the optimal fan pressure of the fth branch (installed branch), satisfying h f,min >0;

[0101] h f,max is the upper limit of the optimal fan pressure of the fth branch (installed branch), satisfying hf,max ≥h f,min >0.

[0102] (a) When the actual fan pressure is required to be no less than the set pressure

[0103] h f,max >>Δh f =h f,min >0 (9)

[0104] Among them, q j,n Indicates the required air volume of the j-th branch (branch with air distribution according to demand).

[0105] (b) When the actual fan pressure is required to be no greater than the set pressure

[0106] h f,max =Δh f >>h f,min >0 (10)

[0107] (c) When the actual fan pressure is required to be approximately equal to the set pressure

[0108] h f,max ≈Δh f ≈h f,min >0 (11)

[0109] In order to solve the mathematical model, the optimal matching wind turbine wind pressure target needs to be converted into the following standard form

[0110]

[0111] in,

[0112] is the upper limit deviation of the optimal fan pressure range of the fth branch (installed branch);

[0113] h f It is the lower limit deviation of the optimal fan pressure range of the fth branch (installed branch).

[0114] Meet the following conditions

[0115]

[0116] h f Meet the following conditions

[0117]

[0118] Under the above conditions, there is and h f There must be an implicit constraint condition that is zero. When the wind pressure distribution value of the branch fan is within the optimal fan pressure range, the upper limit deviation of the optimal fan pressure range is Deviation from the lower limit of the optimal fan pressure range h f All are zero.

[0119] (4) Best matching fan air volume target

[0120] The optimal matching fan air volume target can be expressed as

[0121]

[0122] Where Δq f Indicates the deviation of the optimal fan air volume range of the fth branch (installed branch).

[0123] The deviation of the optimal fan air volume range of branch f (installed branch) can be calculated by the following formula

[0124]

[0125] Among them, q f,min The lower limit of the optimal fan air volume of the fth branch (installed branch) satisfies q f,min >0;

[0126] q f,max The upper limit of the optimal fan air volume of the fth branch (installed branch) satisfies q f,max ≥q f,min >0.

[0127] Similar to the optimal matching fan pressure objective function, the optimal matching fan air volume objective function needs to be converted into the following standard form

[0128]

[0129] in, is the upper limit deviation of the optimal fan pressure range of the fth branch (installed branch);

[0130] q f It is the lower limit deviation of the optimal fan pressure range of the fth branch (installed branch).

[0131] Meet the following conditions

[0132]

[0133] q f Meet the following conditions

[0134]

[0135] The constraints of the optimization model of formula (1) are explained as follows:

[0136] (1) Air volume balance constraint

[0137] The constraints of the mathematical model established according to the air volume balance law are as follows

[0138]

[0139] Where J is the number of nodes in the ventilation network; N is the number of branches in the ventilation network;

[0140] a ij Indicates the relationship between nodes and branches;

[0141] a ij satisfy

[0142] (2) Wind pressure balance constraint

[0143] The constraints of the mathematical model established according to the wind pressure balance law are as follows

[0144]

[0145] Where M is the number of independent circuits in the ventilation network, M = N-J+1;

[0146] h j is the algebraic sum of the wind pressure on the j-th branch,

[0147] r j is the wind resistance of the jth branch;

[0148] q j is the air volume of the j-th branch;

[0149] Δh j is the wind pressure adjustment value of the j-th branch;

[0150] h N,j is the natural wind pressure of the jth branch;

[0151] b ij Indicates the relationship between branches and loops;

[0152] b ij satisfy

[0153] (3) Installation branch constraints

[0154] Unbalanced wind pressure is only allowed to appear in the installed branch, and the installed branch can only be adjusted for energy boost. Therefore, the wind pressure adjustment value should meet

[0155]

[0156] (4) Wind pressure adjustment range constraints

[0157] The wind pressure adjustment range constraint of the installed branch fan should meet the following requirements:

[0158] Δh j,fmin ≤Δh j ≤Δh j,fmax ,j∈F (23)

[0159] Where Δh j,fmin The lower limit of the adjustable fan pressure of the jth branch (installed branch);

[0160] Δh j,fmax The upper limit of the adjustable fan pressure for the jth branch (installed branch).

[0161] (5) Fan operating air volume constraints

[0162] The fan operating air volume constraint should meet

[0163] q j,fmax ≥q j ≥q j,fmin ,j∈F (24)

[0164] Among them, q j is the air volume (installed air volume) of the j-th branch (installed branch);

[0165] q j,min The lower limit of the fan air volume allowed for the j-th branch (installed branch) satisfies q j,fmin ≥0;

[0166] q j,max The upper limit of the fan air volume allowed for the j-th branch (installed branch) satisfies q j,fmax ≥q j,min ≥0.

[0167] (6) Fan operating efficiency constraints

[0168] The fan operating efficiency constraint should satisfy

[0169] η j ≥C j ,j∈F (25)

[0170] Among them, η j represents the fan operating efficiency of the jth branch (installed branch);

[0171] C j is a constant, which represents the minimum fan operating efficiency required by the j-th branch (installed branch).

[0172] (7) Fan operating parameter constraints

[0173] The upper and lower limit constraints of fan parameters require that the fan parameters of the selected fan meet the conditions of the limited range.

[0174] p j,fmax ≥p j ≥p j,fmin (26)

[0175] Among them, p j,f Represents the fan parameters of the j-th branch (installed branch) (fan diameter, speed, blade installation angle, and number of fans in series and parallel, etc.);

[0176] p j,fmin The upper limit of the allowable values ​​of the corresponding fan parameters (fan diameter, speed, blade installation angle, and number of fans in series and parallel, etc.) of the jth branch (installed branch);

[0177] p j,fmax It is the lower limit of the allowable values ​​of the corresponding fan parameters (fan diameter, number of revolutions, blade installation angle, and number of fans in series and parallel, etc.) of the jth branch (installed branch).

[0178] The fan parameters of the installed branch are determined by the fan characteristic curves of the alternative fans in the fan bank.

[0179] In the embodiment of the present application, considering that the installed air volume has been determined, the set optimization model can be:

[0180]

[0181]

[0182] Among them, the decision variable of the optimization model is the regulated wind pressure value Δh of all installed branches j , and auxiliary decision variables h f 、 and Since the known air volume can be regarded as a constant, the objective function and the constraints are both linear functions, and the optimization model can be understood as a linear programming model.

[0183] In some embodiments, obtaining a wind turbine selection scheme based on the virtual installed air volume and the virtual installed air pressure of each wind turbine includes:

[0184] Determining a roadway wind resistance characteristic curve of an installed branch based on the virtual installed air volume and the virtual installed wind pressure of each fan;

[0185] According to the wind resistance characteristic curve of the roadway of the installed branch, the air volume-wind pressure characteristic curves of the fans in the fan library are queried one by one to determine the actual operating conditions of the fans;

[0186] Selecting a candidate wind turbine at the installation location based on the actual operating conditions;

[0187] Rank candidate fans based on fan performance indicators;

[0188] Based on the sorting results, the air distribution effect of the multi-stage station ventilation system is verified to obtain a fan selection scheme that meets the selection requirements. The fan selection scheme includes at least one of the following: fan model, installation angle, number of fans and series-parallel arrangement.

[0189] It can be understood that the embodiment of the present application can query the wind volume-wind pressure characteristic curve of the fan from the fan library based on the tunnel wind resistance characteristic curve of the installed branch, and then determine the actual operating conditions of the fan, that is, the actual operating wind volume of the fan and the actual operating wind pressure of the fan, and then determine the alternative fans, and then build the best fan selection scheme based on the alternative fans, which improves the intelligence level of fan selection, and is conducive to optimizing the wind pressure balance of the entire multi-stage station ventilation system, the minimum power of the ventilation fan and the best matching of the comprehensive control requirements of the fan wind pressure, thereby effectively improving the operating reliability of the system.

[0190] In some embodiments, selecting a candidate wind turbine at the installation location based on the actual operating conditions includes:

[0191] Based on the actual operating conditions, a fan whose operating efficiency is greater than or equal to a first threshold and whose operating wind pressure is less than or equal to a second threshold is selected as a candidate fan.

[0192] For example, a fan having an operating efficiency greater than or equal to 60% and an operating wind pressure less than or equal to 90% of the maximum wind pressure may be selected as the alternative fan.

[0193] In some embodiments, ranking the candidate wind turbines based on wind turbine performance indicators includes:

[0194] The candidate fans are ranked based on at least one of the fan operation efficiency, the fan operating air volume, and the fan operating air pressure.

[0195] In some embodiments, verifying the air distribution effect of the multi-stage station ventilation system based on the sorting results to obtain a fan selection scheme that meets the selection requirements includes:

[0196] Based on the sorting results, a fan selection scheme to be confirmed is generated, and the air volume distribution calculation is performed on the fan selection scheme to be confirmed to determine whether the calculation result meets the installed air volume value of each installed branch. If so, the fan selection scheme to be confirmed is used as the fan selection scheme.

[0197] It can be understood that based on the sorting results, the fan type, blade angle, operating air volume, operating wind pressure, efficiency, motor power and fan series and parallel stage number and other information of the alternative fans can be listed, and combined with the actual situation of the mine, a fan selection scheme to be confirmed is generated, and the air volume distribution calculation is performed on the fan selection scheme to be confirmed to verify whether the air volume distribution value corresponding to the calculation result meets the installed air volume value of each installed branch. If so, the fan selection scheme to be confirmed is used as the fan selection scheme.

[0198] For example, if the air volume distribution value corresponding to the calculation result does not meet the installed air volume value of each installed branch, the aforementioned steps 101 to 104 may be re-executed until a fan selection scheme that meets the requirements is obtained.

[0199] In some embodiments, the method further comprises:

[0200] Output the fan selection plan.

[0201] It is understandable that the electronic device can output the wind turbine selection plan based on the display interface, or send the wind turbine selection plan to a display terminal for display.

[0202] The following is an illustrative description of the fan optimization selection method for the multi-stage station ventilation system according to an embodiment of the present application in conjunction with an application example.

[0203] like Figure 2 As shown, the fan optimization selection method of this application embodiment includes:

[0204] Step 201, setting the fan installed air volume and the number of series and parallel stages.

[0205] For example, parameters such as installed air volume, number of series or parallel operation stages of fans, etc. can be set at the fan installation location (installed air volume branch) according to the on-demand air distribution requirements.

[0206] Step 202: Calculate the air volume distribution of the hybrid air distribution network.

[0207] For example, a hybrid air distribution network can be used to calculate air volume distribution and determine the unbalanced air pressure value of the installed air volume branch. For example, a fixed air volume solution algorithm can be used to perform a ventilation network solution on the installed air volume value of each installed branch to determine the initial installed air volume distribution value and the initial installed branch regulated air pressure value.

[0208] Step 203: Install the wind resistance characteristic curve of the branch tunnel.

[0209] Exemplarily, based on the initial installed air volume distribution value, the initial installed branch adjustment air pressure value and the set optimization model, the virtual installed air volume and virtual installed air pressure of the installed branch can be calculated, and the tunnel wind resistance characteristic curve at the installed branch can be determined.

[0210] Step 204 , based on the laneway wind resistance characteristic curve at the installed branch, query the wind volume-wind pressure characteristic curves of the fans in the fan database one by one to determine the operating point (ie, operating condition) of the fan.

[0211] Step 205 , determining whether the fan is running stably, if so, executing step 206 , if not, returning to step 204 and continuing to execute the traversal query operation.

[0212] For example, if the operating point of the fan satisfies the conditions that the operating efficiency of the fan is not less than 60% and the operating wind pressure of the fan does not exceed 90% of the maximum wind pressure, the fan is determined to be operating stably; otherwise, the fan is determined to be unable to operate stably.

[0213] Step 206: Add the wind turbine to the candidate list.

[0214] It is understandable that after adding a wind turbine to the candidate list, it is necessary to continue returning to step 204 until the wind turbine database is traversed and queried.

[0215] Step 207: Select a better fan to verify the air distribution effect.

[0216] Here, the list of fans to be selected can be sorted in different ways, and information such as the fan type, blade angle, operating air volume, operating air pressure, efficiency, motor power and fan series and parallel levels of the alternative fans can be listed; a fan selection scheme to be confirmed is generated based on the sorting results, and the wind separation effect of the fan selection scheme to be confirmed is verified.

[0217] Step 208 , determining whether the fan selection is reasonable, if not, returning to step 201 , if yes, executing step 209 .

[0218] For example, based on the fan selection scheme to be confirmed, the actual fan information can be used to replace the installed air volume, and the air volume distribution calculation of the ventilation network can be re-performed to verify the actual air volume distribution effect of the selected fan, that is, to determine whether the result of the distribution calculation meets the set fan installed air volume.

[0219] If the selected fan is not suitable, return to step 201 to reset parameters such as the installed branch installed air volume, the number of fan series operation or parallel operation levels, etc., redetermine the fan operating point based on the fan joint operation air volume-air pressure characteristic curve, and jump to step 205 to redetermine the alternative fan.

[0220] Step 209: outputting the optimal wind turbine selection scheme.

[0221] Figure 3A simplified mine ventilation network diagram is shown to verify the reliability of the branch selection method for a multi-stage station ventilation system. The numbers in the diagram indicate the ventilation network branches. The ventilation network uses a three-stage station ventilation method (pressure-extraction-pressure) and contains 14 branches, 13 nodes, and five installation locations.

[0222] When optimizing fan selection based on mine conditions and selecting fans from a designated fan library, the fan selection mathematical model should generally consider the alternative fan constraints (installed branch wind pressure adjustment range constraints, fan operating condition constraints) obtained from the alternative fan library information when constructing the fan selection mathematical model. For example, since the installed air volume corresponding to the installed branch has been determined, a reasonable (optional) range of installed air pressure corresponding to the installed air volume can be preliminarily determined by traversing the alternative fans in the fan library.

[0223] In order to use the mathematical model of fan optimization for multi-stage station Figure 3 To optimize fan selection for the ventilation network shown, the air volume distribution calculation should first be performed using a fixed air volume solution algorithm related to the installed air volume logic. Table 1 shows the air volume distribution results for each station in the ventilation network, and Table 2 shows the air pressure distribution results for each station in the ventilation network. Table 3 shows the fan optimization results for the multi-station ventilation network, including information such as the preferred fan model and fan installation angle for each installation point.

[0224] Table 1

[0225]

[0226] Table 2

[0227]

[0228] Table 3

[0229]

[0230]

[0231] It can be understood that after determining the results of air volume distribution and air pressure distribution at each level of the ventilation network, the fan optimization method with independent installation branch logic can be directly used to determine the alternative fans for each installation point to assist ventilation system designers in decision-making.

[0232] In order to implement the method of the embodiment of the present application, the embodiment of the present application also provides a multi-stage station ventilation system fan optimization selection device, which is set in an electronic device, such as Figure 4As shown, the device includes: a first determination module 401, a second determination module 402, a third determination module 403 and a fan selection module 404. Among them, the first determination module 401 is used to determine the installed air volume value of each installed branch according to the installed air volume range of the installed branch of the multi-stage station ventilation system and the set air volume interval; the second determination module 402 is used to use a fixed air volume solution algorithm to perform ventilation network solution on the installed air volume value of each installed branch, and determine the initial installed air volume distribution value and the initial installed branch adjustment air pressure value; the third determination module 403 is used to determine the virtual installed air volume and virtual installed air pressure of each fan based on the initial installed air volume distribution value, the initial installed branch adjustment air pressure value and the set optimization model; fan selection Module 404 is used to obtain a fan selection scheme based on the virtual installed air volume and the virtual installed wind pressure of each fan; the set optimization model is a multi-objective optimization function, and the multi-objective optimization function includes the following optimization objectives: minimum ventilation fan power target, negative pressure balance target of the same-level station and best matching fan wind pressure target, and the decision variables of the multi-objective optimization function include: optimal fan wind pressure range deviation of the installed branch, upper limit deviation of the optimal fan wind pressure range of the installed branch, lower limit deviation of the optimal fan wind pressure range of the installed branch, positive deviation of the installed wind pressure and negative deviation of the installed fan.

[0233] In some embodiments, the wind turbine selection module 404 is specifically configured to:

[0234] Determining a roadway wind resistance characteristic curve of an installed branch based on the virtual installed air volume and the virtual installed wind pressure of each fan;

[0235] According to the wind resistance characteristic curve of the roadway of the installed branch, the air volume-wind pressure characteristic curves of the fans in the fan library are queried one by one to determine the actual operating conditions of the fans;

[0236] Selecting a candidate wind turbine at the installation location based on the actual operating conditions;

[0237] Rank candidate fans based on fan performance indicators;

[0238] Based on the sorting results, the air distribution effect of the multi-stage station ventilation system is verified to obtain a fan selection scheme that meets the selection requirements. The fan selection scheme includes at least one of the following: fan model, installation angle, number of fans and series-parallel arrangement.

[0239] In some embodiments, the set optimization model is as follows:

[0240]

[0241]

[0242] Among them, Z is the optimization target, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, F is the set of all installed branches f, q f is the fan air volume of the installed branch f, h f is the fan pressure of the installed branch f, is the positive deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station, is the negative deviation variable between the installed wind pressure at the installation point of the installed branch f and the installed wind pressure at the reference installation point f′ in each level of the station, is the upper limit deviation of the optimal fan pressure range for installed branch f, h f is the lower limit deviation of the optimal fan pressure range of the installed branch f, N is the number of branches of the ventilation network, M is the number of independent circuits of the ventilation network, b ij Indicates the relationship between the installed branch and the independent circuit, h j is the algebraic sum of the wind pressures of the j-th installed branch, Δh j is the wind pressure adjustment value of the j-th installed branch, Δh j,fmin is the lower limit of the adjustable fan pressure of the j-th installed branch, Δh j,fmax is the upper limit of the adjustable fan pressure of the j-th installed branch, h f,max is the upper limit of the optimal fan pressure for the installed branch f, h f,min is the lower limit of the optimal fan pressure for the installed branch f, q f′ is the fan air volume at the reference installation point f′, h f′ is the wind pressure of the fan at the reference installation point f′.

[0243] In some embodiments, the fan selection module 404 selects a candidate fan at the installation location based on the actual operating conditions, including:

[0244] Based on the actual operating conditions, a fan whose operating efficiency is greater than or equal to a first threshold and whose operating wind pressure is less than or equal to a second threshold is selected as a candidate fan.

[0245] In some embodiments, the wind turbine selection module 404 ranks candidate wind turbines based on wind turbine performance indicators, including:

[0246] The candidate fans are ranked based on at least one of the fan operation efficiency, the fan operating air volume, and the fan operating air pressure.

[0247] In some embodiments, the fan selection module 404 verifies the air distribution effect of the multi-stage station ventilation system based on the sorting results and obtains a fan selection scheme that meets the selection requirements, including:

[0248] Based on the sorting results, a fan selection scheme to be confirmed is generated, and the air volume distribution calculation is performed on the fan selection scheme to be confirmed to determine whether the calculation result meets the installed air volume value of each installed branch. If so, the fan selection scheme to be confirmed is used as the fan selection scheme.

[0249] In some embodiments, the fan optimization selection device for the ventilation system further includes: an output module 405, which is used to output the fan selection solution.

[0250] In actual application, the first determination module 401, the second determination module 402, the third determination module 403, the wind turbine selection module 404 and the output module 405 can be implemented by a processor in an electronic device. Of course, the processor needs to run the computer program in the memory to implement its functions.

[0251] It should be noted that the fan optimization and selection device for the multi-stage station ventilation system provided in the above embodiment only uses the division of the above program modules as an example to illustrate when optimizing the fan selection for the ventilation system. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the fan optimization and selection device for the ventilation system provided in the above embodiment and the fan optimization and selection method embodiment for the ventilation system belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0252] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present application, the embodiment of the present application also provides an electronic device for optimizing the selection of fans in a multi-stage station ventilation system. Figure 5 Only the exemplary structure of the device is shown, not all structures, and can be implemented as needed. Figure 5 Partial or complete structure shown.

[0253] like Figure 5 As shown, the electronic device 500 provided in the embodiment of the present application includes: at least one processor 501, a memory 502, a user interface 503 and at least one network interface 504. The various components in the electronic device 500 are coupled together through a bus system 505. It can be understood that the bus system 505 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 505 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 5 Various buses are labeled as bus system 505.

[0254] The user interface 503 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0255] The memory 502 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer program used to operate on the electronic device.

[0256] The fan optimization selection method for a ventilation system disclosed in the embodiments of the present application can be applied to or implemented by processor 501. Processor 501 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the fan optimization selection method for a ventilation system can be completed by hardware integrated logic circuits or software instructions in processor 501. The aforementioned processor 501 can be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 501 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly implemented as execution by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium located in memory 502. Processor 501 reads the information in memory 502 and, in conjunction with its hardware, completes the steps of the fan optimization selection method for a ventilation system provided in the embodiments of the present application.

[0257] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0258] It is understood that the memory 502 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a magnetic disk or a magnetic tape. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0259] In an exemplary embodiment, the present application also provides a storage medium, namely, a computer storage medium, which may be a computer-readable storage medium, for example, including a memory 502 storing a computer program. The computer program may be executed by a processor 501 of an electronic device to complete the steps of the method described in the embodiment of the present application. The computer-readable storage medium may be a memory such as a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0260] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0261] In addition, the technical solutions described in the embodiments of the present application can be arbitrarily combined without conflict.

[0262] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing fan selection in a multi-stage station ventilation system, characterized in that: include: Determine the installed air volume value of each installed branch according to the installed air volume range and the set air volume interval of the installed branch of the multi-stage station ventilation system; Use the fixed air volume solution algorithm to perform ventilation network solution on the installed air volume value of each installed branch, and determine the initial installed air volume distribution value and the initial installed branch adjustment air pressure value; Determining a virtual installed air volume and a virtual installed air pressure of each wind turbine based on the initial installed air volume distribution value, the initial installed branch adjustment air pressure value, and a set optimization model; Obtaining a fan selection scheme based on the virtual installed air volume and the virtual installed air pressure of each fan; Wherein, the set optimization model is a multi-objective optimization function, which includes the following optimization objectives: minimum ventilation fan power objective, negative pressure balance objective of the same-level station and optimal matching fan wind pressure objective, and the decision variables of the multi-objective optimization function include: optimal fan wind pressure range deviation of the installed branch, upper limit deviation of the optimal fan wind pressure range of the installed branch, lower limit deviation of the optimal fan wind pressure range of the installed branch, positive deviation of the installed wind pressure and negative deviation of the installed fan; The optimization model of the above setting is as follows: ； ； Among them, Z is the optimization target, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, For all installed branches A collection of For installation branch The fan air volume, For installation branch The fan pressure, Install branches for stations at all levels The installed wind pressure at the installation point and the benchmark installation point Positive deviation variable of installed wind pressure, Install branches for stations at all levels The installed wind pressure at the installation point and the benchmark installation point Negative deviation variable of installed wind pressure, For installation branch The upper limit deviation of the optimal fan pressure range, For installation branch The lower limit deviation of the optimal fan pressure range, is the number of branches of the ventilation network, is the number of independent circuits in the ventilation network, Indicates the relationship between the installed branch and the independent circuit, For the The algebraic sum of the wind pressure of the strip machine branches, For the The wind pressure adjustment value of the strip machine branch, For the The lower limit of the adjustable fan pressure of the strip machine branch, For the The upper limit of the adjustable fan pressure of the strip-mounted machine branch, For installation branch The upper limit of the optimal fan pressure, For installation branch The optimal lower limit of fan pressure is Base installation point The fan air volume, Base installation point The fan pressure.

2. The method according to claim 1, characterized in that The step of obtaining a fan selection scheme based on the virtual installed air volume and the virtual installed air pressure of each fan includes: Determining a roadway wind resistance characteristic curve of an installed branch based on the virtual installed air volume and the virtual installed wind pressure of each fan; According to the wind resistance characteristic curve of the roadway of the installed branch, the air volume-wind pressure characteristic curves of the fans in the fan library are queried one by one to determine the actual operating conditions of the fans; Selecting a candidate wind turbine at the installation location based on the actual operating conditions; Rank candidate fans based on fan performance indicators; Based on the sorting results, the air distribution effect of the multi-stage station ventilation system is verified to obtain a fan selection scheme that meets the selection requirements. The fan selection scheme includes at least one of the following: fan model, installation angle, number of fans and series-parallel arrangement.

3. The method according to claim 2, characterized in that The selecting a candidate wind turbine at the installation location based on the actual operating conditions includes: Based on the actual operating conditions, a fan whose operating efficiency is greater than or equal to a first threshold and whose operating wind pressure is less than or equal to a second threshold is selected as a candidate fan.

4. The method according to claim 2, characterized in that The sorting of candidate wind turbines based on wind turbine performance indicators includes: The candidate fans are ranked based on at least one of the fan operation efficiency, the fan operating air volume, and the fan operating air pressure.

5. The method according to claim 2, characterized in that The verifying of the air distribution effect of the multi-stage station ventilation system based on the sorting results to obtain a fan selection scheme that meets the selection requirements includes: Based on the sorting results, a fan selection scheme to be confirmed is generated, and the air volume distribution calculation is performed on the fan selection scheme to be confirmed to determine whether the calculation result meets the installed air volume value of each installed branch. If so, the fan selection scheme to be confirmed is used as the fan selection scheme.

6. The method according to claim 1, wherein The method further comprises: Output the fan selection scheme.

7. A device for optimizing fan selection in a multi-stage station ventilation system, characterized in that: include: A first determining module is configured to determine an installed air volume value of each installed branch according to an installed air volume range of the installed branches of the multi-stage station ventilation system and a set air volume interval; The second determination module is used to perform ventilation network solution on the installed air volume value of each installed branch using a fixed air volume solution algorithm to determine an initial installed air volume distribution value and an initial installed branch regulated air pressure value; a third determining module, configured to determine a virtual installed air volume and a virtual installed air pressure of each wind turbine based on the initial installed air volume allocation value, the initial installed branch adjustment air pressure value, and a set optimization model; a fan selection module, configured to obtain a fan selection scheme based on the virtual installed air volume and the virtual installed air pressure of each fan; Wherein, the set optimization model is a multi-objective optimization function, which includes the following optimization objectives: minimum ventilation fan power objective, negative pressure balance objective of the same-level station and optimal matching fan wind pressure objective, and the decision variables of the multi-objective optimization function include: optimal fan wind pressure range deviation of the installed branch, upper limit deviation of the optimal fan wind pressure range of the installed branch, lower limit deviation of the optimal fan wind pressure range of the installed branch, positive deviation of the installed wind pressure and negative deviation of the installed fan; The optimization model of the above setting is as follows: ； ； Among them, Z is the optimization target, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, For all installed branches A collection of For installation branch The fan air volume, For installation branch The fan pressure, Install branches for stations at all levels The installed wind pressure at the installation point and the benchmark installation point Positive deviation variable of installed wind pressure, Install branches for stations at all levels The installed wind pressure at the installation point and the benchmark installation point Negative deviation variable of installed wind pressure, For installation branch The upper limit deviation of the optimal fan pressure range, For installation branch The lower limit deviation of the optimal fan pressure range, is the number of branches of the ventilation network, is the number of independent circuits in the ventilation network, Indicates the relationship between the installed branch and the independent circuit, For the The algebraic sum of the wind pressure of the strip machine branches, For the The wind pressure adjustment value of the strip machine branch, For the The lower limit of the adjustable fan pressure of the strip machine branch, For the The upper limit of the adjustable fan pressure of the strip-mounted machine branch, For installation branch The upper limit of the optimal fan pressure, For installation branch The optimal lower limit of fan pressure is Base installation point The fan air volume, Base installation point The fan pressure.

8. An electronic device, characterized in that: include: A processor and a memory for storing a computer program capable of being executed on the processor, wherein The processor is configured to execute the steps of the method according to any one of claims 1 to 6 when running a computer program.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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