CN115749689B - Intelligent decision regulation method for gas extraction pipe network - Google Patents
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- 238000000605 extraction Methods 0.000 title claims abstract description 136
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- 230000001105 regulatory effect Effects 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000010276 construction Methods 0.000 claims description 10
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- 238000012163 sequencing technique Methods 0.000 claims description 10
- 230000001276 controlling effect Effects 0.000 claims description 8
- 238000013178 mathematical model Methods 0.000 claims description 8
- 238000005086 pumping Methods 0.000 claims description 6
- 241000084490 Esenbeckia delta Species 0.000 claims description 3
- 238000009954 braiding Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 125000004122 cyclic group Chemical group 0.000 claims description 2
- 230000006835 compression Effects 0.000 description 2
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- 238000012544 monitoring process Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000003245 coal Substances 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
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Abstract
The invention provides an intelligent decision regulation system and method for a gas extraction pipe network, which relate to the technical field of gas extraction and aim at solving the problems that in the gas extraction process, the negative extraction pressure is uncontrollable and the extraction system is blindly regulated.
Description
Technical Field
The invention relates to the technical field of gas extraction, in particular to an intelligent decision-making regulation method for a gas extraction pipe network.
Background
The current state of gas extraction: the negative pressure distribution is unreasonable, and the real-time dynamic optimization and adjustment cannot be performed according to the change of the extraction object, so that the negative pressure is high, the concentration is low, the gas purity is low, and the adjustment and the control depend on manual experience; the local resistance of pipe network extraction is large and can not be timely judged, so that the pipeline safety and the gas extraction concentration are improved in order to solve the problems of gas extraction, and negative pressure is required to be accurately controlled.
Meanwhile, the current extraction system is difficult to meet the requirement of monitoring, distinguishing and controlling different drilling holes, negative pressure of each pipeline is difficult to reasonably and accurately distribute, so that the problems can reduce extraction efficiency of a pipe network, limit gas recycling, the fundamental way of solving the problems is to reasonably control the gas extraction system, accurate extraction is realized, namely, extraction working condition parameters are intelligently controlled in real time through data monitoring, model construction and intelligent control of the extraction system, gas extraction efficiency is improved, and extraction safety is guaranteed.
Disclosure of Invention
The invention aims at solving the problem that the gas extraction regulation and control in the traditional gas extraction method is too dependent on manpower and experience, so as to improve the safety and efficiency of the gas extraction system.
The technical scheme adopted for realizing the invention is as follows: firstly, analyzing resistance coefficients of all pipe sections of a pipe network and characteristic parameters of an extraction gas source end through data acquisition, constructing a pipe network mixed gas flow model, and constructing a gas extraction pipe network graph theory model according to the connection relation of all nodes to obtain a gas extraction pipe network system flow calculation model;
and secondly, the intelligent decision and regulation of gas extraction take the maximum pure quantity of the gas in the pipe network as a target, the flow and the concentration of an inlet of the pipe network as characteristic constraint conditions, the opening of a valve is taken as a decision variable, an intelligent decision and regulation model of the gas extraction pipe network is established, and the gas extraction of the extraction pipe network is optimized by depending on the intelligent decision and regulation model of the gas extraction pipe network.
The intelligent decision and regulation model of the gas extraction pipe network comprises pipe network mathematical model construction, model solving, model optimizing and regulation, and the regulation model can also realize accurate control of negative pressures of different branches and dynamic solving of gas flow and concentration of pipe network nodes.
The intelligent decision is realized by automatically triggering a regulation and control model through the data intelligently perceived by the sensor when the target value does not meet the manual setting requirement, so that the regulation and control of a pipe network are realized;
the pipe network mathematical model construction can be mainly structurally divided into 3 parts: the gas source end, the pipe network and the accessory device are connected with the gas outlet end of the extraction pump;
the pipe network mathematical model construction comprises a node flow balance equation set, a mixed gas mass flow equation, a mixed gas volume concentration equation, a gas extraction pipe network pipeline pressure drop equation, a gas state equation, a gas extraction pump working characteristic equation, a pipe network gas source end characteristic equation and a gas source end pure gas flow characteristic equation;
the model solving comprises the following steps:
(1) drawing a gas extraction pipe network diagram, marking main basic data on the diagram, numbering each node and pipe section, and braiding extraction pump nodes at the end in the numbering process;
(2) determining the airflow direction and the pressure drop direction, determining an incidence matrix, and establishing a node flow balance equation;
(3) assigning a value M to the node flow of the extraction inlet end according to the extraction data ai (i＝1,…,n)、M gi (i=1, …, n), the assignment suggests using smaller values to avoid the risk of excessive calculation values;
(4) determining the flow of each pipe section and the flow of the extraction pump by using the correlation matrix and the node flow balance law, and calculating the negative pressure P of the extraction pump node at a fixed rotating speed by combining a characteristic curve of the gas extraction pump Drawing machine ；
(5) Performing equivalent treatment on the valve by using a length equivalent method, obtaining the pressure drop of each pipe section by using a pipe section pressure drop equation, solving the negative pressure of each node, and determining the sequential stress gas flow direction;
(6) based on the negative pressure of the pumping pump node and the pressure drop of each pipe section, the negative pressure of each node is obtained, and the new inlet end node flow M 'is obtained by utilizing the boundary condition of the negative pressure flow of the pumping inlet end node' ai (i＝1,…,n)、M' gi (i=1, …, n), correction of inlet port flow adjustment;
(7) checking calculation accuracy, if the calculation accuracy does not meet the requirement, recalculating the pipe section resistance by using the calculated pipe section flow, and then carrying out adjustment, so as to carry out iterative loop calculation until the accuracy requirement is met;
wherein: m is M ai -assigning a mass air flow to the inode;
M gi -i node assigning a pure gas mass flow;
M’ ai -solving the resulting mass air flow;
M’ gi -resolving the resulting pure gas mass flow.
The model optimizing is a loop iteration optimizing method, and the global optimizing is realized mainly through local exhaustion and global loop: specifically, the initial valve parameters are given first, and under the condition that other valve parameters are not moved, the parameters of each valve are sequentially adjusted, so that the objective function after each adjustment is optimal, after all valves are completely adjusted, the initial valve parameters are taken as the initial valve parameters, and then the valve parameters are sequentially adjusted again until convergence is achieved and the accurate conditions are met.
The model regulation and control comprises the following steps:
(1) drawing a drainage network diagram, installing valves on each side, wherein the number of the valves is equal to the number S of the sides, sequencing the valves according to the sequence from the near to the far of a gas drainage trunk route, and judging the judgment basis to be that the fewer the number of nodes which are needed to be passed by the gas at the valve to reach the nodes of an outlet section, the closer the nodes are, and the number s=1, 2 and … S of the number of the valves according to the valve rank;
(2) the continuous opening degree discretization treatment of the valve is divided into N gears with the gear numbers of n=1, 2, … N and k s Represents the opening degree of an s valve, k sn The s valve is opened at the n opening; set K s ＝{k s S=1, 2, … S }, the number of elements is S, and the opening conditions of all valves are recorded;
(3) for all K s Valve opening k s Assigning values, and solving an objective function f (c, M) of the pipe network at the moment, and taking reference for subsequent sequencing;
(4) keeping the opening of other valves unchanged, selecting different opening of No. 1 valves, solving N objective function values by using a gas extraction pipe network system flow solution model, sequencing, and judging the optimal opening k of the valves 1n Assigning the valve opening k to the valve 1 to obtain a new valve opening k of the valve 1 1 And solving an objective function f (c, M) of the pipe network at the moment;
(5) the opening degree of the rest valves is sequentially changed in the same way to obtain the optimal valve opening degree K of all the valves s And find the objective function at this time as f 1 (c,M)；
(6) Comparing f (c, M) with f 1 (c, M) determining the error delta, when the accuracy requirement is satisfied, i.e. delta is less than or equal to delta 0 Output valve opening K s Taking the opening of the valve as the basis of the actual hardware regulation valve, otherwise, letting f 1 The value of (c, M) is given to f (c, M), returning to step 4;
(7) selecting proper gas extraction pump rotation speed r and valve opening K corresponding to the gas extraction pump rotation speed r according to extraction requirements and actual working conditions s As a result.
The intelligent decision and regulation model of the gas extraction pipe network comprises an objective function and constraint conditions:
the gas extraction pure quantity and the extraction volume fraction are selected as objective functions, the gas volume fraction at the inlet of the pipe network and the safety condition are characteristic constraint conditions, and the valve opening is used as a decision variable.
The decision variable K satisfies:
K smax ≥K sn ≥K smin ，n＝(1,2,3,...,n),s＝(1,…,s)
K sn indicating that the valve on the S pipe section is opened at the n-number opening degree, K smax And K smin The upper limit and the lower limit of a reasonable range of the valve resistance coefficient are respectively set;
for the quality of the gas pipe network extraction effect, the pure flow of the gas is used as a judgment index, and the objective function is as follows:
f(c,M)＝c×M
c is the gas concentration and M is the flow.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following drawings are used in the description of the embodiments or the prior art as follows:
FIG. 1 is a schematic flow chart of an intelligent decision control system for a gas extraction pipeline network according to an embodiment of the present invention
FIG. 2 is a diagram of a gas extraction pipeline network
FIG. 3 is a flow chart for solving a gas pipe network
FIG. 4 gas extraction pipe network regulation and control flow chart
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
The gas extraction intelligent regulation and control model provided by the application has a regulation and control flow shown in a figure 1 and is applied to a gas extraction system shown in a figure 2.
The intelligent decision regulation and control system and method for the gas extraction pipe network have the following regulation and control ideas:
the intelligent decision and regulation model of the gas extraction pipe network comprises pipe network mathematical model construction, model solving, model optimizing and regulation, and the regulation model can also realize accurate control of negative pressures of different branches and solving of gas flow and concentration of pipe network nodes.
The intelligent decision is data intelligently perceived by a sensor, and when the target value does not meet the manual setting requirement, a regulation model is automatically triggered, so that the regulation of a pipe network is realized;
firstly, constructing a gas extraction pipe network model.
The gas extraction pipe network formed by the gas extraction pipeline and the extraction pump is an organic whole, the working characteristics of the pipeline and the pump are closely connected and mutually influence, and the construction of the pipe network mathematical model mainly comprises 3 parts: the device comprises an air source end, a pipe network and an accessory device, and a gas outlet end of a pumping pump.
The gas flow of the underground coal seam gas extraction pipe network is directional flow, the connecting structure is a simple tree branch single-outlet pipeline structure, the gas flow is similar to one-dimensional unidirectional flow, and the pipe network is shown in figure 2 by taking a certain mine extraction pipe network as an example.
The pipe network air source end, the extraction pump port, the pipeline junction is a node, the extraction pipeline is an edge, and the relation between the node number m and the edge number s is as follows: s=m-1.
According to the theory of graph theory, the interrelationship among the nodes can be described by using a matrix, and the flow of all pipe sections of the tree pipe network is represented through the flow of the inlet nodes by utilizing the wind flow balance theorem.
For a tree-like mine gas pipe network with the node number of m (m=14) (the end node number of n (n=8), the number of the inlet nodes of n-1 (n-1=7), the number of the outlet nodes of 1) and the side of m-1 (m-1=13), the parameters to be solved are (n-1) x 2 ((n-1) x 2=14) inlet node flows (pure gas flow m) g Air flow of blow-by gas m a ) And m (m=14) node pressures p.
By utilizing graph theory and gas flow equation, m-1 (m-1=13) independent pipeline pressure drop equations, 1 gas extraction pump working characteristic equation and (n-1) 2 ((n-1) 2=14) independent pipe network gas source end characteristic equations are established. The number of parameters to be solved is equal to the number of independent equations, namely m+2n-2 (m+2n-2=28), a square course solution can be solved, and finally, the relation between the extraction negative pressure and the extraction concentration and the pure quantity is solved, so that reasonable regulation and control of the negative pressure are realized, and the equation set is as follows:
the working characteristic equation of the gas extraction pump, the pipe section pressure drop equation set and the pipe network gas source end extraction characteristic equation set are as follows:
wherein the mass flow formula of the mixed gas is as follows:
for the i orifice, and in the s pipeline, the mixed gas mass flow is the sum of the air mass flow and the pure gas mass flow.
M i ＝M ai +M gi ,i＝(1,…,7)
M s ＝M as +M gs ,s＝(1,…,7)
Wherein: m is M i -i node mixed gas mass flow, kg/s;
M ai -i node mass air flow, kg/s;
M gi -i node pure gas mass flow, kg/s;
M s -s pipe section mixed gas mass flow, kg/s;
M as -s pipe section leak air mass flow, kg/s;
M gs -s pipe section pure gas mass flow, kg/s;
wherein the formula of the volume concentration of the mixed gas is as follows:
for the mixed gas volume concentration in the s pipe section (or i node), the calculation formula is as follows:
wherein: c s -s pipe section mixed gas volume concentration,%;
M as -s pipe section leak air mass flow, kg/s;
M gs -s pipe section pure gas mass flow, kg/s;
wherein the gas state equation is:
p i V i ＝ZR i0 T 0 ，i＝(1,…,7)
wherein: z-gas compression factor; z=1
T 0 -gas temperature, K; t= 293.16K
P i -extracting absolute pressure Pa from the i node;
V i inode mixed gas relative volume, m 3 /mol；
ρ i Density of mixed gas at node i, kg/m 3 ；
R i0 -extracting gas constant, kJ/(kg×k);
R i0 the method can be obtained by the following formula:
R i0 ＝(1-c i )R air +cR CH4
wherein: c i -i node gas mixture volume concentration,%;
R air ＝0.287KJ/(Kg*k)
the working characteristic equation fitting of the gas extraction pump is as follows:
in the fourth step of the resolving step, determining the pressure of the gas extraction pump:
wherein: p (P) atm Atmospheric pressure, pa, P atm ＝101325pa；
P Drawing machine Absolute pressure of gas extraction pump inlet, pa, P Drawing machine ＝P 6 ；
M i -i node mixed gas mass flow, kg/s;
P atm -P drawing machine ＝75000
By utilizing a pressure drop equation of the pipeline of the gas extraction pipeline network, the pressure of the node of the outlet section can be obtained according to the pressure of the node of the inlet section, and for a pipeline section s with the inlet end being j nodes and the outlet end being k nodes, the pressure drop equation of the pipeline of the gas extraction pipeline network is as follows:
when the pipe is a horizontal pipe,
wherein: p (P) j -the absolute pressure, pa, of the extraction of the j node at the inlet end;
P k -the absolute pressure, pa, of the extraction of the k node at the outlet end;
L s -corrected pipe length of s pipe section, m;
L s ＝L sr +L sv
L sr -the actual pipe length of the s pipe section, m;
L sv the equivalent length of the valve of the s pipe section, m;
z—gas compression coefficient, z=1;
T 0 -gas temperature, K, t= 293.16K;
M s -the flow of the mixed gas in the s pipe section, kg/s;
R 0 -extracting gas constant in s pipe section, kJ/(kg. K) taking R 0 ＝0.29862KJ/(Kg*k)；
D s -the inner diameter of the pipe of the s pipe section, m;
sin s θ -the pipe slope of the s pipe segment;
λ s -the resistance coefficient of the s pipe section along the way;
Δ s ＝0.0001；
when the fluid flows in a completely turbulent state, the lambda coefficient is only related to the relative smoothness of the pipe wall, and the larger the relative smoothness of the pipe wall is, the smaller the lambda value is, and the formula is as follows:
and (3) a pipe network air source end characteristic equation:
wherein: m is M ai -i node blow-by air mass flow, kg/s;
M gi -i node pure gas mass flow, kg/s;
a gi 、b gi 、c gi 、a ai 、b ai 、c ai -a constant.
The flow balance equation construction is performed next:
in order to facilitate theoretical analysis of gas flow characteristics in a gas extraction pipe network, a graph theory is used for abstract description of the gas extraction pipe network of a mine, a gas extraction system is changed into a gas extraction network consisting of nodes, edges and attributes thereof, wherein a gas extraction pump is used as a vertex of a graph, pipelines and other pipeline accessories are used as edges of the graph, the intersection point of two or more pipelines is used as a node of the graph, and the gas flow direction is calibrated as the direction of the corresponding edge. The branch pipe is directly connected with the drill hole of the drill site, and the branch pipe is more in number, so that the branch pipe and the connecting drill hole thereof in the drill site are treated equivalently and serve as gas extraction nodes.
According to the theory of graph theory, the structural relation between the mine gas pipe network nodes and the edges can be expressed by using an incidence matrix of the directed graph: let a (G) = (a) ij ) m×(m-1) Is a mine gas pipe network association matrix, a ij The definition is as follows:
the gas extraction network is represented by g= (I, S), where I is a set of finite points (m), S is a set of finite edges (m-1), s= | a b … m|, i= |1 2 … 14|.
Establishing an association matrix:
establishing an association matrix pipe section flow matrix:
B(G)＝|M a M b … M m |
the node traffic matrix is:
P(M)＝|k 1 M 1 k 2 M 2 … k 14 M 14 |
the node flow characteristic matrix is as follows:
K＝|k 1 k 2 … k 14 |
K＝|1 1 1 1 1 1 1 0 0 0 0 0 0 -1|
k m the definition is as follows:
according to B (G). Times.A T (G)＝P T (M) obtaining a flow balance equation, and the pipe section flow M s Can be represented by node traffic:
the pure gas and air follow the node flow balance equation, and the node flow balance equation comprises:
the extraction pipe network model solving flow is as follows, as shown in fig. 3:
(1) drawing a gas extraction pipe network diagram, marking main basic data on the diagram, numbering each node and pipe section, and braiding extraction pump nodes at the end in the numbering process;
(2) determining the airflow direction and the pressure drop direction, determining an incidence matrix, and establishing a node flow balance equation;
(3) assigning a value M to the node flow of the extraction inlet end according to the extraction data ai (i＝1,…,7)、M gi (i=1, …, 7). Assignment suggests using smaller values to avoid the risk of computing values too large. Can make M ai ＝0.1，M gi For a valve-closed network branch, its flow is 0, =0.01;
(4) determining the flow of each pipe section and the flow of the extraction pump by using the correlation matrix and the node flow balance law, and calculating the negative pressure P of the extraction pump node at a fixed rotating speed by combining a characteristic curve of the gas extraction pump Drawing machine ；
(5) And (3) performing equivalent treatment on the valve by using a length equivalent method, and obtaining the pressure drop of each pipe section by using a pipe section pressure drop equation to obtain the negative pressure of each node. Determining the sequence of the gas flow direction, wherein the sequence is as follows: e. c, d, b, a;
(6) based on the negative pressure of the pumping pump node and the pressure drop of each pipe section, the negative pressure of each node is obtained, and the new inlet end node flow M 'is obtained by utilizing the boundary condition of the negative pressure flow of the pumping inlet end node' ai (i＝1,…,7)、M' gi (i=1, …, 7), correction of inlet port flow adjustment;
(7) checking calculation accuracy, if the accuracy is not satisfied, recalculating the pipe section resistance by using the calculated pipe section flow, and then carrying out adjustment, so as to carry out iterative loop calculation until the accuracy requirement is satisfied (less than 0.002), wherein the iteration times are usually not more than 20 times.
Wherein: m is M ai -assigning a mass air flow to the inode;
M gi -i node assigning a pure gas mass flow;
M’ ai -solving the resulting mass air flow;
M’ gi -resolving the resulting pure gas mass flow.
The extraction pipe network model is optimized and regulated as follows:
firstly, determining a regulated objective function and constraint conditions, selecting a gas extraction pure quantity and extraction volume fraction as the objective function, and selecting a single-hole gas volume fraction, a efficiency ratio and the like as characteristic constraint conditions, wherein the opening of a valve is used as a decision variable.
The decision variable K satisfies:
K smax ≥K sn ≥K smin ，n＝(1,2,3,...,n),s＝(1,…,s)
K sn indicating that the valve located on the S-pipe section is open at n-degree. K (K) smax And K smin The upper limit and the lower limit of a reasonable range of the valve resistance coefficient are respectively set;
for the quality of the gas pipe network extraction effect, the pure flow of the gas is used as a judgment index, and the objective function is as follows:
f(c,M)＝c×M
c is the gas concentration and M is the flow.
The specific flow of model optimization and regulation is shown in fig. 4, and the detailed steps are as follows:
the model optimizing is a cyclic iterative optimizing method, global optimizing is realized mainly through local exhaustion and global circulation, specifically, initial valve parameters are given first, parameters of each valve are sequentially adjusted under the condition that other valve parameters are not moved, so that an objective function after each adjustment is optimal, after all valves are completely adjusted, the objective function is taken as the initial valve parameters, and then the valve parameters are sequentially adjusted again until convergence is achieved and accurate conditions are met.
A model tuning as claimed in claim 3 comprising the steps of:
(1) drawing a drainage network diagram, installing valves on each side, sequencing the valves according to the sequence from the near to the far of the gas drainage trunk route, wherein the judgment basis is that the fewer the number of nodes which are needed to be passed by the gas at the valve to reach the nodes of the outlet section, the closer the nodes are, and determining the sequence of the valve positions as follows through sequencing: m, g, l, f, k, e, g, d, i, c, h, b, a; according to the valve order, the valve numbers are numbered, and the numbers s=1, 2 and … S;
(2) the continuous opening degree discretization treatment of the valve is divided into N gears with the gear numbers of n=1, 2, … N and k s Represents the opening degree of an s valve, k sn The s valve is opened at the n opening; set K s ＝{k s S=1, 2, … S }, the number of elements is S, and the opening conditions of all valves are recorded;
(3) for all K s Valve opening k s Assigning values, and solving an objective function f (c, M) of the pipe network at the moment, and taking reference for subsequent sequencing;
(4) keeping the opening of other valves unchanged, selecting different opening of No. 1 valves, solving N objective function values by using a gas pipe network calculation model, sequencing, and judging the optimal opening k of the valves 1n Assigning the valve opening k to the valve 1 to obtain a new valve opening k of the valve 1 1 And solving an objective function f (c, M) of the pipe network at the moment;
(5) the opening degree of the rest valves is sequentially changed in the same way to obtain the optimal valve opening degree K of all the valves s And find the objective function at this time as f 1 (c,M)；
(6) Comparing f (c, M) with f 1 (c, M) determining the error delta, when the accuracy requirement is satisfied, i.e. delta is less than or equal to delta 0 Output valve opening K s Taking the opening of the valve as the basis of the actual hardware regulation valve, otherwise, letting f 1 The value of (c, M) is given to f (c, M), returning to step 4;
(7) selecting proper gas extraction pump rotation speed r and valve opening K corresponding to the gas extraction pump rotation speed r according to extraction requirements and actual working conditions s As a result;
finally, the matching of the negative pressure and the flow is obtained, and the accurate control of the negative pressure of each branch is realized.
Claims (6)
1. The intelligent decision regulation and control method for the gas extraction pipe network is characterized by comprising the following steps of:
firstly, analyzing resistance coefficients of each pipe section of a pipe network and characteristic parameters of an extraction gas source end through data acquisition, constructing a pipe network mixed gas flow model, constructing a gas extraction pipe network graph theory model according to a tree pipe network connection relation, and finally obtaining a gas extraction pipe network system flow calculation model; secondly, the intelligent decision and regulation model of the gas extraction pipe network takes the maximum gas purity of the pipe network as a target, the flow and the concentration of an inlet of the pipe network as characteristic constraint conditions, the opening of a valve is taken as a decision variable, the intelligent decision and regulation model of the gas extraction pipe network is established, and the gas extraction of the gas extraction pipe network is optimized by depending on the intelligent decision and regulation model of the gas extraction pipe network.
2. The intelligent decision-making and controlling method for the gas extraction pipe network according to claim 1, wherein the intelligent decision-making and controlling model for the gas extraction pipe network comprises pipe network mathematical model construction, model solving, model optimizing and controlling, and the intelligent decision-making and controlling model for the gas extraction pipe network can also realize accurate control of negative pressures of different branches and dynamic solving of gas flow and concentration of pipe network nodes.
3. The intelligent decision-making and controlling method for the gas extraction pipe network according to claim 2, wherein the construction of the pipe network mathematical model mainly comprises 3 parts: the device comprises an air source end, a pipe network and an accessory device, and a gas outlet end of a pumping pump.
4. The intelligent decision-making and controlling method for the gas extraction pipe network according to claim 2, wherein the construction of the pipe network mathematical model comprises a node flow balance equation set, a mixed gas mass flow equation, a mixed gas volume concentration equation, a gas extraction pipe network pipeline pressure drop equation, a gas state equation, a gas extraction pump operation characteristic equation, a pipe network gas source end characteristic equation and a gas source end pure gas flow characteristic equation.
5. The intelligent decision control method of the gas extraction pipe network according to claim 2, wherein the model solving comprises the following steps:
(1) drawing a gas extraction pipe network diagram, marking main basic data on the diagram, numbering each node and pipe section, and braiding extraction pump nodes at the end in the numbering process;
(2) determining the airflow direction and the pressure drop direction, determining an incidence matrix, and establishing a node flow balance equation;
(3) assigning a value to the node flow of the extraction inlet according to the extraction data, wherein the assignment proposal uses a smaller value so as to avoid the risk of overlarge calculated value;
(4) determining the flow of each pipe section and the flow of the extraction pump by using the correlation matrix and the node flow balance law, and calculating the node negative pressure of the extraction pump at a fixed rotating speed by combining a characteristic curve of the gas extraction pump;
(5) performing equivalent treatment on the valve by using a length equivalent method, obtaining the pressure drop of each pipe section by using a pipe section pressure drop equation, solving the negative pressure of each node, and determining the sequential stress gas flow direction;
(6) based on the negative pressure of the extraction pump node and the pressure drop of each pipe section, the negative pressure of each node is obtained, the boundary condition of the negative pressure flow of the extraction inlet end node is utilized to obtain new inlet end node flow, and the adjustment of the inlet end flow is corrected;
(7) checking calculation accuracy, if the calculation accuracy does not meet the requirement, recalculating the pipe section resistance by using the calculated pipe section flow, and then carrying out adjustment, so that iterative loop calculation is carried out until the accuracy requirement is met.
6. The intelligent decision control method of the gas extraction pipe network according to claim 2, wherein the model optimizing and controlling process comprises the following steps:
the model optimizing is a cyclic iteration optimizing method, and the global optimizing is realized mainly through local exhaustion and global circulation, specifically: setting initial valve parameters, and sequentially adjusting parameters of each valve under the condition that other valve parameters are not moved, so that an objective function after each adjustment is optimal, and after all valves are completely adjusted, taking the objective function as the initial valve parameters, and sequentially adjusting the valve parameters again until convergence and accurate conditions are met;
wherein the model tuning comprises the steps of:
(1) drawing a drainage network diagram, installing valves on each side, wherein the number of the valves is equal to the number S of the sides, sequencing the valves according to the sequence from the near to the far of a gas drainage trunk route, and judging the judgment basis to be that the fewer the number of nodes which are needed to be passed by the gas at the valve to reach the nodes of an outlet section, the closer the nodes are, and the number s=1, 2 and … S of the number of the valves according to the valve rank;
(2) the continuous opening degree discretization treatment of the valve is divided into N gears with the gear numbers of n=1, 2, … N and k s Represents the opening degree of an s valve, k sn The s valve is opened at the n opening; set K s ＝{k s S=1, 2, … S }, the number of elements is S, and the opening conditions of all valves are recorded;
(3) for all K s Valve opening k s Assigning values, and solving an objective function f (c, M) of the pipe network at the moment, and taking reference for subsequent sequencing;
(4) keeping the opening of other valves unchanged, selecting different opening of No. 1 valves, solving N objective function values by using a gas extraction pipe network system flow solution model, sequencing, and judging the optimal opening k of the valves 1n Assigning the valve opening k to the valve 1 to obtain a new valve opening k of the valve 1 1 And solving an objective function f (c, M) of the pipe network at the moment;
(5) the opening degree of the rest valves is sequentially changed in the same way to obtain the optimal valve opening degree K of all the valves s And find the objective function at this time as f 1 (c,M)；
(6) Comparing f (c, M) with f 1 (c, M) determining the error delta, when the accuracy requirement is satisfied, i.e. delta is less than or equal to delta 0 Output valve opening K s Taking the opening of the valve as the basis of the actual hardware regulation valve, otherwise, letting f 1 The value of (c, M) is given to f (c, M), returning to step (4);
(7) selecting proper gas extraction pump rotation speed r and valve opening K corresponding to the gas extraction pump rotation speed r according to extraction requirements and actual working conditions s As a result;
the intelligent decision and regulation model of the gas extraction pipe network comprises a regulated objective function and a constraint condition;
selecting the gas extraction pure quantity and the extraction volume fraction as objective functions, wherein the gas volume fraction at the inlet of the pipe network and the safety condition are characteristic constraint conditions, and the opening of a valve is used as a decision variable;
the decision variable K satisfies:
K smax ≥K sn ≥K smin ，n＝(1,2,3,...,n),s＝(1,…,s)
K sn indicating that the valve on the S pipe section is opened at the n-number opening degree, K smax And K smin The upper limit and the lower limit of a reasonable range of the valve resistance coefficient are respectively set;
for the quality of the gas pipe network extraction effect, the pure flow of the gas is used as a judgment index, and the objective function is as follows:
f(c,M)＝c×M
c is the gas concentration and M is the flow.
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