CN-115203861-B - Fan frequency conversion optimization method, device and equipment for multi-stage machine station ventilation system

CN115203861BCN 115203861 BCN115203861 BCN 115203861BCN-115203861-B

Abstract

The application discloses a fan frequency conversion optimization method, device and equipment of a multi-stage machine station ventilation system, wherein the method comprises the steps of obtaining a ventilation network model of the multi-stage machine station ventilation system; the method comprises the steps of carrying out air distribution calculation on a ventilation network model based on a ventilation network calculation method, adjusting the ventilation network model based on an air distribution calculation result until the simulation working condition of fans in the ventilation network model is matched with the actual working condition, generating at least one fan frequency conversion regulation scheme to be selected based on underground ventilation air volume requirements and a set optimization model corresponding to a multi-stage machine station ventilation system, and determining the optimal fan frequency conversion regulation scheme based on the at least one fan frequency conversion regulation scheme to be selected and the underground ventilation air volume requirements, wherein the set optimization model is a multi-objective mixed integer linear programming model. Therefore, intelligent variable frequency regulation and control of the multi-stage machine station ventilation system can be realized, and in addition, the solving performance of a fan variable frequency regulation and control scheme can be greatly improved.

Inventors

ZHONG DEYUN
WANG LIGUAN
JIA MINGTAO
BI LIN
HU JIANHUA

Assignees

中南大学

Dates

Publication Date: 20260505
Application Date: 20220815

Claims (8)

1. A fan frequency conversion optimization method for a multi-stage machine station ventilation system is characterized by comprising the following steps: acquiring a ventilation network model of a multi-stage machine station ventilation system; Performing air distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition; Generating at least one fan variable frequency regulation scheme to be selected based on underground ventilation air volume requirements and a set optimization model corresponding to the multi-stage machine station ventilation system; Determining an optimal fan variable frequency regulation scheme based on the at least one fan variable frequency regulation scheme to be selected and the underground ventilation air volume requirement; The fan frequency conversion regulation and control scheme comprises target operation rotating speeds of all frequency conversion fans, wherein the set optimization model is a multi-target mixed integer linear programming model and comprises optimization targets of a minimum ventilation fan power target, an optimal on-demand ventilation demand target, an optimal working condition fan air volume target and an optimal working condition fan air pressure target, and decision variables of the set optimization model are 0-1 integer decision variables, and the set optimization model comprises a first variable representing the corresponding relation between the air volume of an on-demand air distribution branch and a plurality of air volume values of the branch, a second variable representing the corresponding relation between the rotating speed ratio before and after fan branch adjustment and a plurality of rotating speed ratios of the branch and a third variable representing the product of the first variable and the second variable; the set optimization model is as follows: ; ; Wherein Z is an optimization target, As a first weight coefficient, a first set of weights, As a result of the second weight coefficient, As a result of the third weight coefficient, As a result of the third weight coefficient, For all fans to branch Is a set of (a) and (b), The air quantity of the fan of the j-th branch is, The wind pressure of the fan of the j-th branch, Representing the set of all on-demand wind branches, The upper limit deviation of the on-demand wind-dividing range of the j-th on-demand wind-dividing branch, The j-th on-demand wind-dividing branch is divided by the lower limit deviation amount of the wind-dividing range on demand, Is the upper limit deviation of the air quantity range of the optimal working condition of the j branch, Is the lower limit deviation of the air quantity range of the optimal working condition of the j-th branch, Is the upper limit deviation of the wind pressure range of the optimal working condition of the jth branch, Is the lower limit deviation of the wind pressure range of the optimal working condition of the jth branch, For the number of branches of the ventilation network, For the number of nodes of the ventilation network, For the relationship of the node to the branch, Is the first The strips divide the air quantity of the air branches according to the requirement, For the number of independent loops of the ventilation network, Is the first Algebraic sum of wind pressure of the branches, In order to relate the branches to the circuit, Is the first The lower limit of the wind speed is allowed by the strip branch, Is the first The cross-sectional area of the roadway of the branch, Is the first The upper limit of the wind speed is allowed by the branch, To adjust the rotation speed The wind pressure of the fans of the branch, To adjust the rotation speed Fan characteristic curve fitting coefficients of the branches, Represent the first The speed ratio of the branch after the fan adjusts the speed and before the fan adjusts the speed, To adjust the rotation speed The air quantity of the fans of the branch, Is the first The wind lower limit of the fan of the branch, Is the first The wind upper limit of the fan of the branch, Is the first The actual operating rotational speed of the branches, Is the first The lower limit of the rotating speed of the adjustable fan of the branch, Is the first The upper limit of the rotating speed of the adjustable fan of the branch, Is the first The lower limit of the allowable fan air quantity of the branch, Is the first The upper limit of the allowable fan air quantity of the branch, Is the first The fan operation efficiency of the branch is improved, Is the first The minimum fan operating efficiency required by the branches, Is the first The lower limit of the allowable air quantity of the air-splitting branches according to the requirement, Is the first The upper limit of the allowable air quantity of the air-splitting branches is divided according to the requirement.
2. The method according to claim 1, wherein the calculating the air volume distribution of the ventilation network model based on the ventilation network calculation method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated working condition of the fan in the ventilation network model matches the actual working condition, includes: Carrying out air distribution calculation on the ventilation network model by adopting a ventilation network calculation method based on loop air quantity to obtain an air distribution calculation result; Adjusting the tunnel wind resistance parameter based on a resistance measurement mode until the comparison error between the wind quantity distribution calculation result and the actually measured tunnel wind quantity is within a set threshold value; obtaining a simulation working condition of a fan in the ventilation network model based on the air distribution calculation result; Judging whether the simulation working condition of the fan is matched with the actual working condition, if not, adjusting the model parameters of the ventilation network model until the simulation working condition of the fan in the ventilation network model is matched with the actual working condition.
3. The method of claim 1, wherein generating at least one alternative fan variable frequency control scheme based on the downhole ventilation air volume demand and a set optimization model corresponding to the multi-stage station ventilation system comprises: setting each weight coefficient and decision variable of the set optimization model according to the underground ventilation air volume demand; and solving at least one fan variable frequency regulation scheme to be selected by utilizing the set optimization model based on the set weight coefficient and the decision variable.
4. The method of claim 1, wherein the determining an optimal fan frequency modulation scheme based on the at least one candidate fan frequency modulation scheme and the downhole ventilation air volume demand comprises: Adopting a ventilation network resolving method based on loop air quantity to perform air quantity distribution calculation on at least one fan frequency conversion regulation scheme to be selected, and obtaining an air quantity distribution calculation result corresponding to each fan frequency conversion regulation scheme; And comparing the air distribution calculation result corresponding to each fan frequency conversion regulation scheme with the distribution air quantity of the on-demand air distribution branch determined based on the underground ventilation air quantity demand, and determining the optimal fan frequency conversion regulation scheme.
5. The method according to claim 1, wherein the method further comprises: and regulating and controlling the multi-stage machine station ventilation system based on the optimal fan variable frequency regulation and control scheme.
6. A fan frequency conversion optimizing device of a multi-stage machine station ventilation system is characterized by comprising: The ventilation network model acquisition module is used for acquiring a ventilation network model of the multi-stage machine station ventilation system; The ventilation network model optimization module is used for carrying out air distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air distribution calculation result until the simulation working condition of the fan in the ventilation network model is matched with the actual working condition; the regulation and control scheme generation module is used for generating at least one fan variable frequency regulation and control scheme to be selected based on underground ventilation air volume requirements and a set optimization model corresponding to the multi-stage machine station ventilation system; the regulation and control scheme selection module is used for determining an optimal fan frequency conversion regulation and control scheme based on the at least one fan frequency conversion regulation and control scheme to be selected and the underground ventilation air volume demand; The fan frequency conversion regulation and control scheme comprises target operation rotating speeds of all frequency conversion fans, wherein the set optimization model is a multi-target mixed integer linear programming model and comprises optimization targets of a minimum ventilation fan power target, an optimal on-demand ventilation demand target, an optimal working condition fan air volume target and an optimal working condition fan air pressure target, and decision variables of the set optimization model are 0-1 integer decision variables, and the set optimization model comprises a first variable representing the corresponding relation between the air volume of an on-demand air distribution branch and a plurality of air volume values of the branch, a second variable representing the corresponding relation between the rotating speed ratio before and after fan branch adjustment and a plurality of rotating speed ratios of the branch and a third variable representing the product of the first variable and the second variable; the set optimization model is as follows: ; ; Wherein Z is an optimization target, As a first weight coefficient, a first set of weights, As a result of the second weight coefficient, As a result of the third weight coefficient, As a result of the third weight coefficient, For all fans to branch Is a set of (a) and (b), The air quantity of the fan of the j-th branch is, The wind pressure of the fan of the j-th branch, Representing the set of all on-demand wind branches, The upper limit deviation of the on-demand wind-dividing range of the j-th on-demand wind-dividing branch, The j-th on-demand wind-dividing branch is divided by the lower limit deviation amount of the wind-dividing range on demand, Is the upper limit deviation of the air quantity range of the optimal working condition of the j branch, Is the lower limit deviation of the air quantity range of the optimal working condition of the j-th branch, Is the upper limit deviation of the wind pressure range of the optimal working condition of the jth branch, Is the lower limit deviation of the wind pressure range of the optimal working condition of the jth branch, For the number of branches of the ventilation network, For the number of nodes of the ventilation network, For the relationship of the node to the branch, Is the first The strips divide the air quantity of the air branches according to the requirement, For the number of independent loops of the ventilation network, Is the first Algebraic sum of wind pressure of the branches, In order to relate the branches to the circuit, Is the first The lower limit of the wind speed is allowed by the strip branch, Is the first The cross-sectional area of the roadway of the branch, Is the first The upper limit of the wind speed is allowed by the branch, To adjust the rotation speed The wind pressure of the fans of the branch, To adjust the rotation speed Fan characteristic curve fitting coefficients of the branches, Represent the first The speed ratio of the branch after the fan adjusts the speed and before the fan adjusts the speed, To adjust the rotation speed The air quantity of the fans of the branch, Is the first The wind lower limit of the fan of the branch, Is the first The wind upper limit of the fan of the branch, Is the first The actual operating rotational speed of the branches, Is the first The lower limit of the rotating speed of the adjustable fan of the branch, Is the first The upper limit of the rotating speed of the adjustable fan of the branch, Is the first The lower limit of the allowable fan air quantity of the branch, Is the first The upper limit of the allowable fan air quantity of the branch, Is the first The fan operation efficiency of the branch is improved, Is the first The minimum fan operating efficiency required by the branches, Is the first The lower limit of the allowable air quantity of the air-splitting branches according to the requirement, Is the first The upper limit of the allowable air quantity of the air-splitting branches is divided according to the requirement.
7. An electronic device comprising a processor and a memory for storing a computer program capable of running on the processor, wherein, The processor being adapted to perform the steps of the method of any of claims 1 to 5 when the computer program is run.
8. A storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any of claims 1 to 5.

Description

Fan frequency conversion optimization method, device and equipment for multi-stage machine station ventilation system Technical Field The application relates to the field of ventilation control, in particular to a fan frequency conversion optimization method, device and equipment for a multi-stage machine station ventilation system. Background The purpose of mine ventilation is to supply enough fresh air to a mining operation area, timely discharge dirty air underground to the ground surface, improve the mine ventilation environment, strengthen the safety production standard and create a good and comfortable operation environment for underground workers. Compared with a large main fan ventilation system, the multi-stage station ventilation system (multi-fanstation ventilation system) is more controllable and is commonly applied to metal mines. The multi-stage machine station ventilation system is an engineering facility system for conveying ground fresh air to an operation mining area by a multi-stage air inlet machine station and a multi-stage machine station and discharging dirty air out of a mine, wherein the ventilation system is more adjustable and controllable in a multi-fan serial-parallel connection and multi-stage machine station cascading mode, the efficiency of the ventilation system is improved, and the ventilation energy consumption is reduced. However, with the development of intelligent control technology, a multi-stage machine station ventilation system is required to realize intelligent variable frequency regulation and control. Disclosure of Invention In view of the above, the embodiment of the application provides a fan frequency conversion optimization method, device and equipment for a multi-stage machine station ventilation system, which aim to realize intelligent frequency conversion regulation and control of the multi-stage machine station ventilation system and meet unattended control requirements. The technical scheme of the embodiment of the application is realized as follows: the embodiment of the application provides a fan frequency conversion optimization method for a multi-stage machine station ventilation system, which comprises the following steps: acquiring a ventilation network model of a multi-stage machine station ventilation system; Performing air distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition; Generating at least one fan variable frequency regulation scheme to be selected based on underground ventilation air volume requirements and a set optimization model corresponding to the multi-stage machine station ventilation system; Determining an optimal fan variable frequency regulation scheme based on the at least one fan variable frequency regulation scheme to be selected and the underground ventilation air volume requirement; The method comprises the steps of setting an optimization model to be a multi-target mixed integer linear programming model, wherein the optimization model comprises an optimization target of a minimum ventilation fan power, an optimal on-demand ventilation demand target, an optimal working condition fan air volume target and an optimal working condition fan air pressure target, decision variables of the optimization model are 0-1 integer decision variables, the decision variables comprise a first variable representing the corresponding relation between the air volume of an on-demand air distribution branch and a plurality of air volume values of the branch, a second variable representing the corresponding relation between the speed ratio before and after fan branch adjustment and a plurality of speed ratios of the branch, and a third variable representing the product of the first variable and the second variable, and the fan variable-frequency regulation scheme comprises target operation speeds of variable-frequency fans. In some embodiments, the calculating method based on the ventilation network performs air distribution calculation on the ventilation network model, and adjusts the ventilation network model based on the air distribution calculation result until the simulated working condition of the fan in the ventilation network model matches the actual working condition, including: Carrying out air distribution calculation on the ventilation network model by adopting a ventilation network calculation method based on loop air quantity to obtain an air distribution calculation result; Adjusting the tunnel wind resistance parameter based on a resistance measurement mode until the comparison error between the wind quantity distribution calculation result and the actually measured tunnel wind quantity is within a set threshold value; obtaining a simulation working condition of