a gis based unsteady network model and system applications

Research ArticleA GIS Based Unsteady Network Model and SystemApplications for Intelligent Mine Ventilation

Hui Liu Shanjun Mao Mei Li and Pingyang Lyu

Institute of Remote Sensing and Geographic Information System Peking University Beijing 100871 China

Correspondence should be addressed to Shanjun Mao sjmao_pkueducn

Received 26 June 2020 Revised 14 September 2020 Accepted 12 October 2020 Published 28 October 2020

Academic Editor Chi-Hua Chen

Copyright copy 2020Hui Liu et alis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

With the development of state-of-the-art technology such as the artificial intelligence and the Internet ofings the constructionof ldquointelligent minerdquo is being vigorously promoted where the intelligent mine ventilation is one of the primary concerns thatprovides the efficient guarantee for safety production in the underground coal mine system is study aims to integrate thegeographical information system (GIS) and the unsteady ventilation network model together to provide location based in-formation and online real-time support for the decision-making system Firstly a GIS based unsteady network model is proposedand its algorithm steps are brought out Secondly a prototype web system named 3DVentCloud is designed and developed basedon the front and end technique which effectively integrates the proposed algorithms irdly the model is validated and thesystem is applied to a real coal mine for ventilation solution which demonstrates that the model is reasonable and practical eonline simulation system is efficient in providing real-time support e study is potential and is expected to guide the real-timecoal mine safety production

1 Introduction

e ldquointelligent minerdquo is being constructed and developedvigorously in recent years which is expected to providereliable fast smart and accurate decision support for safetyoperation and production in underground coal mine Mineventilation system is one of the most important and basicsystems to ensure safety production An efficient ventilationsystem is potential to guarantee a health working envi-ronment for coal miners and ensure sustainable develop-ment of the underground coal mine system e concept ofthe intelligent mine ventilation is being proposed whilethere are still a lot of scientific issues to be solved [1 2]

e current research on ventilation network model andventilation software is mainly focused on the traditionalsteady calculation model However mine ventilation systemessentially belongs to a dynamic complex system withgeospatial and temporal characteristics For example al-though the ventilation state is steady during normal pro-duction of coal mine various internal disturbances andground air pressure would have certain influence on airflow

and pressure inside the laneway thus it causes airflowpulsation [3] Besides due to gas outburst mine fire andother major accidents the ventilation state will change in-stantaneously and cause transient disorder of airflow insidethe laneway erefore the ventilation states during normalproduction or disaster should all be regarded as unsteadyflow thus it requires to establish a unified ventilationsimulation model and that is well adapted with the conceptof the intelligent mine ventilation [4]

Besides the current data acquisition and processingprocedures for mine ventilation lack an online integratedsystem to better adapt to intelligent mine ventilation systemNowadays there are a lot of ventilation software systems thatimplemented informatization for mine ventilation for ex-ample Polish Academy of Sciences developed VENT-GRAPH and Mine Fire Simulation software [5ndash7] Chasmcompany in Australia developed Ventsim software which iswidely used around the world [8] e US and UK alsodeveloped some ventilation software systems such asVentPC 2003 Ventilation Design VR-MNE and Datamine[9] ere are also some successful domestic ventilation

HindawiDiscrete Dynamics in Nature and SocietyVolume 2020 Article ID 1041927 8 pageshttpsdoiorg10115520201041927

systems such as MFire MVSS and VentAnaly developed byChinese institutes and universities [10 11] With the deepunderstanding for mine ventilation system and the greatdemand for practical production mine ventilation belongsto a typical geographical environment which has spatialtemporal and attribute data as well as the spatial topologicalrelationship [12] Fortunately the geographic informationsystem (GIS) is regarded as one of the most effective ways tomanage the geospatial data [13] erefore based on tra-ditional GIS technology a great number of ventilationsoftware systems have been developed For instanceLongruanGIS LKGIS and VRMine GIS platform and somesecondary developed systems based on AutoCAD or ArcGISare developed and have been widely used [14ndash17]

However it is difficult to deal with the spatial temporaland attribute data as well as the topological relationshipusing AutoCAD In addition the ventilation software sys-tems based on GIS are mostly focusing on the combinationof traditional GIS or static GIS with steady ventilationsimulation model us a unified unsteady ventilationmodel integrated with GIS technology is needed to provideonline and fast decision support to better satisfy the re-quirement of intelligent mine ventilation

erefore this study aims to provide a fast onlineventilation simulation solution to promote the constructionof intelligent mine ventilation by integrating the unsteadyventilation network model and GIS as well as the systemdevelopment Firstly the basic ventilation theory is intro-duced and an algorithm of GIS based unsteady networkmodel is proposed Secondly based on the data structure ofthe ventilation model as well as the laneway network aprototype simulation web system called 3D VentCloud isdesigned and developed irdly two experiments areimplemented to validate the ventilation model and apply theweb system respectively

2 GIS-Based Unsteady Network Model

Mine ventilation is a daily work to ensure safety productionof coal mine which requires accurate simulation to learnabout the onsite ventilation situation Rather than the tra-ditional steady ventilation simulation model this sectionmainly introduces the unsteady network model based onGIS technology as well as the model integration with theonline web system

21 Mathematical Models Firstly an ideal geometric lane-way model is established to build the basic equation for theunsteady ventilation network model As can be seen inFigure 1 the laneway length is L and cross section area is Awithout considering the compressible properties of airflow

Without any external force the airflow inside thelaneway belongs to the steady state If the wind pressure atthe ends of the laneway suddenly changes there will be anequivalent external force F to the ends of the air columnAccording to the Newtonrsquos second law suppose that theexternal force gives the acceleration a and the air columnhas the corresponding equations [18]

a dv

dt1A

dQ

dt

F

mΔPA

ALρΔPLρ

ΔP (p1 minus p2) +12ρv

21 minus

12ρv

221113874 1113875 + ρgΔh + hr minus RQ

2

(1)

dQ

dt

A

Lρ(p1 minus p2) +

12ρv

21 minus

12ρv

221113874 1113875 + ρgΔh + hr minus RQ

21113876 1113877

(2)

where R is the ventilating resistance p1 and p2 are staticpressures v1 and v2 are the velocities at the laneway endsand hr is the external power Assuming that a laneway issingle and horizontal both ends are connected with theatmosphere When a fan placed at the end of the lanewaysuddenly starts the pressure difference in equation (2) is 0then we obtain

dQ

dt

A

Lρhr minus RQ

21113960 1113961 (3)

As for the airflow inside the laneway network the air isassumed to follow three basic laws of wind flow which arethe law of ventilation resistance the law of pressure balancein loops and the law of wind balance at nodes ese threelaws represent the restriction and equilibrium relationshipsfor three basic ventilation parameters that is air volume andventilating resistance for laneway branch and node pressurein the ventilation network model [19] A simple ventilationnetwork graph is shown in Figure 2

e three basic laws of wind flow can be described as

hi Ri middot Qi

11138681113868111386811138681113868111386811138681113868 middot Qi (i 1 2 N)

1113944

N

i1εkiQi 0 k 1 2 3 (b minus 1)

(4)

1113944

N

i1Cjiβi

dQ

dτ 1113944

N

i1Cji ΔPi minus RiQi Qi

111386811138681113868111386811138681113868111386811138681113872 1113873 j 1 2 3 M

(5)

whereN is the number of the laneway branches andM is thenumber of network nodes εki and Cij are the elements frombasic correlation matrix and independent loop matrix of thenetwork graph and the value is 0 1 or minus1ΔPi hFj(Q)∓hNj(Q) stands for the ventilation powerincluding fan power hFj(Q) and natural power hNj(Q)Equation (5) can also be simplified to the matrix form ofloop equations

Cross section

L X

ZY

A P2 h2P1 h1

Figure 1 An ideal geometric laneway model

2 Discrete Dynamics in Nature and Society

dQ(τ)

dτ C

TCβC

T1113872 1113873

minus 1D (6)

where C is the loop matrix and the elements in D areDj ΔPj minus 1113936

i

CjiRiQi(τ)|Qi(τ)| j 1 2 b Based onthis the Runge-Kutta iterative algorithm can be applied tosimulate the air volume changing with time for each lanewaybranch [20 21] e following is our specific solution basedon the classical fourth-order Runge-Kutta method

Q τk+1( 1113857 Q τk( 1113857 +h

6K1 + 2K2 + 2K3 + K4( 1113857 (7)

K1 f Q τk( 1113857( 1113857 (8)

K2 fQ τk( 1113857 + hK1

2 (9)

K3 fQ τk( 1113857 + hK2

2 (10)

K4 fQ τk( 1113857 + hK3

2 (11)

22 Data Models for Ventilation Network Based on GIStheories and methods the data model for unsteady venti-lation network model is designed Firstly the ventilationnetwork graph is described as G (V E) whereV v1 v2 vm1113864 1113865 is a set of nodes in network graph andE e1 e2 en1113864 1113865 is a set of laneway branches in networkgraph Consider that airflow on every laneway branch has awind direction thus the ventilation network graph is adirected graph en we obtained the adjacency matrixincidence matrix cut set matrix loop matrix and pathmatrix of the network graph which stores the topologicalinformation of the ventilation network graph Besides basedon the perspective of GIS the ventilation network graph andmodel belong to typical geospatial objects including spatialdata attribute data temporal data and the spatial rela-tionship where the spatial relationship mainly includes thetopological relation of the ventilation network graph whichconnected the laneway branches and nodes and is stored aspoint-line (node-branch) indexed structure based on GISdata organization as can be seen in Table 1

e specific data structure for laneway branch and nodesis established and presented in Table 2

23 Algorithm Design e whole algorithm is designed forthe GIS based unsteady ventilation network model Table 3shows the specific procedure of the whole structure ismodel and data organization structure provides more detailsof geospatial and attribute data for ventilation network byintegrating geographical information

3 System Design and Development

A prototype system called 3D VentCloud is designed in thisstudy which is developed by combing the front-end and theback-end technologies e front-end is developed by usingHtml JavaScript and CSSe back-endmainly includes themodel and algorithm which is developed by C++ and Py-thon Besides Tornado is adopted as the server to transmitdata between the front-end and back-end and thus the twoends are connectede systemmainly includes three layersAs presented in Figure 3 they are respectively technologylayer service layer and application layer

Each layer and its functions are specifically described asfollows

(1) Technology layer is layer contains the data sourceand the GIS based unsteady ventilation networkmodel which is the key technology for the systemdevelopment e data source includes the originalobtained centerline data of the laneway networkwhich is processed and stored in GIS database asGeoJson format including the geospatial attributeand temporal data of the laneway branch and nodese GIS based unsteady network model includes thegeneration of the topological relationship of theventilation network graph and the unsteady venti-lation simulation model where the topological re-lation and the dynamic simulation results are linkedby the point-line indexed structure ese modelsjointly constructed the core model library and thekey technology for the ventilation simulation system

(2) Service layer In this layer the Tornado architectureis applied as the service and WebSocket interface isutilized to link the front-end of JavaScript with the

1

1

2

2

3 3

4

45

6

Figure 2 Ventilation network graph

Table 1 Point-line indexed structure for topological relationLaneway branch ID(line)

Start node ID(point)

End node ID(point)

Discrete Dynamics in Nature and Society 3

back-end of Python and C++ program to realize thedata transmission ree js are adopted to imple-ment the 3D visualization function

(3) Application layer is layer provides the unsteadyventilation simulation function 3D visualization andspatiotemporal attribute query function of thelaneway network and simulation results e systemcan read the original data of laneway centreline tostore it and display the 3D lanewaymodel and outputthe stl file of the laneway geometric model Whenthe simulation function is implemented each of thelaneway branches can also be rendered with differentcolors based on different air volume and the

geospatial coordinates and attribute data of thelaneway can be queried and displayed to show moregeographical laneway details

4 Results and Discussion

41 GIS-Based Unsteady Ventilation Network ModelValidation In order to verify the GIS based nonsteadynetwork model before field application we acquired the datafrom reference [3] e simplified mine ventilation networkdiagram and its minimum spanning tree are shown inFigure 4 e network diagram has 11 laneway branchesincluding one virtual branch and 8 nodes Table 4 presents

Table 2 Data structure for laneway branch and node

Data type Laneway branch attribute (line) Data type Node attribute (point)Int ID Int IDString name String nameString Kind Double Coordinate (x y z)Double Length Double PressureDouble Cross section area Double HumidityDouble Perimeter Double DensityDouble Wind drag Double TemperatureDouble Ventilating resistance vectorltintgt Adjacent laneway IDVector ltdoublegt Air volume (time series)

Table 3 e algorithm for GIS Based Unsteady Network Model

Algorithm 1 Algorithm steps for GIS based unsteady network modelStep 1 Establish the ventilation network graph initialize the geospatial and attribute data for laneways and nodesStep 2 Generate the topological relation for the ventilation network graph and stored as point-line indexed structure and basic incidencematrixStep 3 Sort the laneway branches according to wind drag and generate the minimum spanning tree based on Dijkstra shortest pathalgorithmStep 4 Search the cotree branches generate the independent loop and loop matrixStep 5 Main iteration based on Runge-Kutta algorithm see equations (7)ndash(11)Step 6 Save the results as time series air volume for each laneway branch based on point-line indexed structure

Applicationlayer

Servicelayer

Technologylayer

Mineventilationsimulation

Technologicalrelation

generation

Unsteadyventilation

network model

Lanewaycenteline

data

Laneway spatialtemporal andattribute data

Data source

Model andalgorithm

3Dvisualization

Systemimplementation

threejs Tornado

JavaScript HtmlEnvironment

Figure 3 e architecture of 3D VentCloud system


the basic ventilation parameters for the network e casestudy investigated wind flow and pressure disturbancecaused by the mine car movement in the transportationlaneway which belongs to the unsteady flow phenomenon

In this case study the mine car is moving windwardinside the transportation laneway (with the branchesnumbers 2 and 3) which would lead to the increase ofdynamic wind drag and the corresponding decrease of airvolume in laneway branches 2 and 3 Based on the nonsteadynetwork model we simulated the variation regularity overtime of air volume for each laneway as can be seen inFigure 5 e positive value represents the reduced air andthe negative value stands for the increased value of airquantity It is obvious that the transportation laneway withbranches numbers 2 and 3 has the maximum air reducedquantity while branch number 5 has the maximum airincreased quantity which is easy to understand becausemost of the airflow from the air intake laneway flows into thelaneway (with branch number 5) that is connected withtransportation laneway in node 2

Besides we also investigated the particular laneways andnodes to observe the changing regularity over time of airvolume and pressure after mine car stops moving towindward As presented in Figures 6 and 7 the lanewaybranches 2 and 5 nodes 3 and 5 are taken as examples toshow the nonsteady characteristics of air volume andpressure change It shows that with the mine car moving towindward air volume reduction in laneways 2 and 5presents a significantly rising trend and falling trend

respectively and then keeps a steady state After the mine carstops moving to windward air volume reduction in lane-ways 2 and 5 appears with an opposite tendency and finallyreaches the initial steady state e wind pressure variationhas the similar regularity e result is in accord with thepublished research in [3] and is consistent with the variationlaw of airflow and wind pressure in ventilation networkmodel

42 A Case Study Application for a Real Mine VentilationNetwork is study is also applied to Xinqiao coal mine(located in Henan province China) to extend the practicalityof the model and investigate the real mine ventilation tofurther promote the development of intelligent ventilationerefore the real mine ventilation network data fromXinqiao coal mine is obtained which contains 290 lanewaysand 222 nodes e three-dimensional geometric model ofXinqiao laneway network is read and visualized by 3DVentCloud system as presented in Figure 8

e specific spatial data and attribute data include the IDand name of the laneway the ID and coordinates of the startnode and end node the area and perimeter of lanewaysection laneway length laneway type wind drag ventilatingresistance and friction resistance Here some of the basicventilation parameters are shown in Table 5

e topological structure for ventilation network graphof Xinqiao coal mine is established based on GIS to simulatethe ventilation state At first the shortest path algorithm is

11

2

3

34

5

6

6

7

7

8

910

8

5

11

4

2

(a)

11

2

23

35

6

710

8

5

11

4

4

(b)

Figure 4 Mine ventilation network diagram (a) and its minimum spanning tree (b)

Table 4 Basic ventilation parameters for the network

Laneway branch number Start node number End node number Wind drag (kgmiddotmminus4) Ventilating resistance (Pa)1 1 2 0020 1052 2 3 0001 03 3 4 0010 04 3 5 0050 05 2 6 0069 06 4 5 0010 07 4 6 0020 408 6 7 00045 09 5 7 0010 010 7 8 002 15011 8 1 0 0


applied to sort the laneway ventilating resistance based onwhich 70 residual tree branches and the correspondingminimum spanning tree of the network graph are generatedBy adding each of the residual tree branches to the minimumspanning tree 70 independent circuits are created

respectively en the GIS based nonsteady network modelis applied to conduct the ventilation simulation We ob-tained the air volume of each laneway branch changing withtime When the ventilation of Xinqiao coal mine reaches thesteady state the result is almost the same with the state based

Time (s)0

ndash2

0

2

4

6

10 20 30 40 50 60 70 80 90

Air

volu

me r

educ

tion

(m3 s

)

Laneway 1

Laneway 4

Laneway 2Laneway 3

Laneway 5Laneway 6Laneway 7


Figure 5 Air volume reduction for each laneway with mine car moving to windward

Time (s)0 20 40 60 80 100 120 140 160 180

ndash2

0

2

4

6

Air volume reduction for laneway 2

Air

volu

me r

educ

tion

(m3 s

)

(a)

Time (s)0 20 40 60 80 100 120 140 160 180

ndash3

ndash2

ndash1

0

1

2

3


Air

volu

me r

educ

tion

(m3 s

)

(b)

Figure 6 Air volume reduction for laneways 2 (a) and 5 (b) with mine car stops moving to windward

Pressure reduction for node 3

Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(a)


Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(b)

Figure 7 Wind pressure reduction for nodes 3 (a) and 5 (b) with mine car stops moving to windward


on the traditional steady ventilation network model isstudy takes a few seconds at the web end to get the simu-lation result it consumes 472 s in our latest experimentwhich is expected to provide fast or real-time decisionsupport for afterwards site applications

e 3D geometric model of Xinqiao laneway network isrendered based on the simulation results and visualized on3D VentCloud as can be seen in Figure 9 e web systemsupports user interaction such as the geospatial queryfunction For example the spatial and attribute features willbe shown on web page by clicking on each laneway branch

5 Conclusions

is paper investigated the unsteady mine ventilationsimulation model based on GIS technology and developed aprototype web system to implement the online ventilationsimulation which can better adapt and promote the con-struction of intelligent mine ventilation

Specifically by integrating the GIS technology and un-steady ventilation simulation model this study provides aunified GIS based ventilation network model which can

effectively simulate the dynamic changes of the unsteadyventilation state as well as the steady ventilation state Be-sides a prototype web system is developed based on theproposed algorithm which can provide fast simulation re-sult and location based information e result is demon-strated to be effective in simulating the unified ventilationresult and the simulation system is expected to provide theonline and real-time decision making support for coal minesafety production

However the ventilation system belongs to a complexand dynamic 3D system Mine accidents are occasionallyhappened inside the laneway network e future workshould comprehensively consider the inner structure and 3Dcharacteristics of the laneway network as well as the airflowand the quantitative accuracy of the model is also worthstudy in the future

Data Availability

e data used in this study are confidential e readers canaccess the data by sending e-mail to the correspondingauthor

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is work was financially supported by the National KeyResearch and Development Program of China (Grant no2016YFC0803108) and the National Natural ScienceFoundation of China (Grant no 51774281)

References

[1] C Ren and S Cao ldquoImplementation and visualization ofartificial intelligent ventilation control system using fastprediction models and limited monitoring datardquo SustainableCities and Society vol 52 pp 101ndash860 2020

[2] C-H Chen F Song F-J Hwang and L Wu ldquoA probabilitydensity function generator based on neural networksrdquoPhysica A Statistical Mechanics and Its Applications vol 541Article ID 123344 2020

[3] SWang B Liu and S Liu ldquoComputer simulation of unsteadyairflow processes in mine venilation networksrdquo Journal ofLiaoning Technical University (Natural Science) vol 19 no 5pp 449ndash453 2000

[4] G Wang H Wang H Ren et al ldquo2025 Scenarios and de-velopment path of intelligent coal minerdquo Journal of ChinaCoal Society vol 43 no 2 pp 295ndash305 2018

[5] W Dziurzynski A Krach and T Pałka ldquoA reliable method ofcompleting and compensating the results of measurements offlow parameters in a network of headingsrdquo Archives of MiningSciences vol 60 no 1 pp 3ndash24 2015

[6] W Dziurzynski and E Krause ldquoInfluence of the field ofaerodynamic potentials and surroundings of goaf on methanehazard in longwall N-12 in seam 3291 3291-2 in ldquokrupinskirdquocoal minerdquo Archives of Mining Sciences vol 57 no 4pp 819ndash920 2012

Table 5 Some of the ventilation attribute data for Xinqiao coalmine laneway

LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584

Figure 9 e 3D visualization of mine ventilation simulationresult

Figure 8 e 3D laneway network model of Xinqiao coal mine


[7] C-H Chen ldquoA cell probe-based method for vehicle speedestimationrdquo IEICE Transactions on Fundamentals of Elec-tronics Communications and Computer Sciences vol E103A-A no 1 pp 265ndash267 2020

[8] S Maleki ldquoApplication of VENTSIM 3D and mathematicalprogramming to optimize underground mine ventilationnetwork a case studyrdquo Journal of Mining amp Environmentvol 9 2018

[9] D Zielinski B Macdonald and R Kopper ldquoComparativestudy of input devices for a VR mine simulationrdquo in Pro-ceedings of the IEEE Virtual Reality (VR) Minneapolis MNUSA March 2014

[10] Q Zhag Y Yao and J Zhao ldquoStatus of mine ventilationtechnology in China and prospects for intelligent develop-mentrdquo Coal Science and Technology vol 48 no 2 pp 97ndash1032020

[11] C-H Chen F-J Hwang and H-Y Kung ldquoTravel timeprediction system based on data clustering for waste collec-tion vehiclesrdquo IEICE Transactions on Information and Sys-tems vol E102DD no 7 pp 1374ndash1383 2019

[12] J Zhang and W Gao ldquoMine ventilation information systembased on 3D GISrdquo Journal of Liaoning Technical University(Natural Science) vol 31 no 5 pp 634ndash637 2012

[13] H Liu S MaoM Li and SWang ldquoA tightly coupled GIS andspatiotemporal modeling for methane emission simulation inthe underground coal mine systemrdquo Applied Sciences vol 9no 9 p 1931 2019

[14] B-R Li M Inoue and S-B Shen ldquoMine ventilation networkoptimization based on airflow asymptotic calculationmethodrdquo Journal of Mining Science vol 54 no 1 pp 99ndash1102018

[15] P Wang B Lu and L I U Xu ldquoTwo and three dimensionalmine ventilation simulation system based on GIS andAutoCADrdquo Safety in Coal Mines vol 47 no 12 pp 90ndash922016

[16] J Suh S-M Kim H Yi and Y Choi ldquoAn overview of GIS-based modeling and assessment of mining-induced hazardssoil water and forestrdquo International Journal of Environ-mental Research and Public Health vol 14 no 12 p 14632017

[17] S Choi M O Karslıoglu and N Demirel ldquoDevelopment of aGIS-based monitoring and management system for under-ground coal mining safetyrdquo International Journal of CoalGeology vol 80 no 2 pp 105ndash112 2009

[18] W Nyaaba S Frimpong and K A El-Nagdy ldquoOptimisationof mine ventilation networks using the Lagrangian algorithmfor equality constraintsrdquo International Journal of MiningReclamation and Environment vol 29 no 3 pp 201ndash2122015

[19] X Wang X Liu Y Sun et al ldquoConstruction schedule sim-ulation of a diversion tunnel based on the optimized venti-lation timerdquo Journal of Hazardous Materials vol 165 no 1ndash3pp 933ndash943 2009

[20] Y Liang J Zhang T Ren Z Wang and S Song ldquoApplicationof ventilation simulation to spontaneous combustion controlin underground coal mine a case study from Bulianta col-lieryrdquo International Journal of Mining Science and Technologyvol 28 no 2 pp 231ndash242 2018

[21] S G Wang J R Wang and L Hong ldquoMathematical modelfor solving ventilation networks during conventional venti-lation and emergency conditionsrdquo Journal of LiaoningTechnical University vol 22 no 4 pp 436ndash438 2003


systems such as MFire MVSS and VentAnaly developed byChinese institutes and universities [10 11] With the deepunderstanding for mine ventilation system and the greatdemand for practical production mine ventilation belongsto a typical geographical environment which has spatialtemporal and attribute data as well as the spatial topologicalrelationship [12] Fortunately the geographic informationsystem (GIS) is regarded as one of the most effective ways tomanage the geospatial data [13] erefore based on tra-ditional GIS technology a great number of ventilationsoftware systems have been developed For instanceLongruanGIS LKGIS and VRMine GIS platform and somesecondary developed systems based on AutoCAD or ArcGISare developed and have been widely used [14ndash17]

However it is difficult to deal with the spatial temporaland attribute data as well as the topological relationshipusing AutoCAD In addition the ventilation software sys-tems based on GIS are mostly focusing on the combinationof traditional GIS or static GIS with steady ventilationsimulation model us a unified unsteady ventilationmodel integrated with GIS technology is needed to provideonline and fast decision support to better satisfy the re-quirement of intelligent mine ventilation

erefore this study aims to provide a fast onlineventilation simulation solution to promote the constructionof intelligent mine ventilation by integrating the unsteadyventilation network model and GIS as well as the systemdevelopment Firstly the basic ventilation theory is intro-duced and an algorithm of GIS based unsteady networkmodel is proposed Secondly based on the data structure ofthe ventilation model as well as the laneway network aprototype simulation web system called 3D VentCloud isdesigned and developed irdly two experiments areimplemented to validate the ventilation model and apply theweb system respectively

2 GIS-Based Unsteady Network Model

Mine ventilation is a daily work to ensure safety productionof coal mine which requires accurate simulation to learnabout the onsite ventilation situation Rather than the tra-ditional steady ventilation simulation model this sectionmainly introduces the unsteady network model based onGIS technology as well as the model integration with theonline web system

21 Mathematical Models Firstly an ideal geometric lane-way model is established to build the basic equation for theunsteady ventilation network model As can be seen inFigure 1 the laneway length is L and cross section area is Awithout considering the compressible properties of airflow

Without any external force the airflow inside thelaneway belongs to the steady state If the wind pressure atthe ends of the laneway suddenly changes there will be anequivalent external force F to the ends of the air columnAccording to the Newtonrsquos second law suppose that theexternal force gives the acceleration a and the air columnhas the corresponding equations [18]

a dv

dt1A

dQ

dt

F

mΔPA

ALρΔPLρ

ΔP (p1 minus p2) +12ρv

21 minus

12ρv

221113874 1113875 + ρgΔh + hr minus RQ

2

(1)

dQ

dt

A

Lρ(p1 minus p2) +

12ρv

21 minus

12ρv

221113874 1113875 + ρgΔh + hr minus RQ

21113876 1113877

(2)

where R is the ventilating resistance p1 and p2 are staticpressures v1 and v2 are the velocities at the laneway endsand hr is the external power Assuming that a laneway issingle and horizontal both ends are connected with theatmosphere When a fan placed at the end of the lanewaysuddenly starts the pressure difference in equation (2) is 0then we obtain

dQ

dt

A

Lρhr minus RQ

21113960 1113961 (3)

As for the airflow inside the laneway network the air isassumed to follow three basic laws of wind flow which arethe law of ventilation resistance the law of pressure balancein loops and the law of wind balance at nodes ese threelaws represent the restriction and equilibrium relationshipsfor three basic ventilation parameters that is air volume andventilating resistance for laneway branch and node pressurein the ventilation network model [19] A simple ventilationnetwork graph is shown in Figure 2

e three basic laws of wind flow can be described as

hi Ri middot Qi

11138681113868111386811138681113868111386811138681113868 middot Qi (i 1 2 N)

1113944

N

i1εkiQi 0 k 1 2 3 (b minus 1)

(4)

1113944

N

i1Cjiβi

dQ

dτ 1113944

N

i1Cji ΔPi minus RiQi Qi

111386811138681113868111386811138681113868111386811138681113872 1113873 j 1 2 3 M

(5)

whereN is the number of the laneway branches andM is thenumber of network nodes εki and Cij are the elements frombasic correlation matrix and independent loop matrix of thenetwork graph and the value is 0 1 or minus1ΔPi hFj(Q)∓hNj(Q) stands for the ventilation powerincluding fan power hFj(Q) and natural power hNj(Q)Equation (5) can also be simplified to the matrix form ofloop equations

Cross section

L X

ZY

A P2 h2P1 h1

Figure 1 An ideal geometric laneway model


dQ(τ)

dτ C

TCβC

T1113872 1113873

minus 1D (6)


i


Q τk+1( 1113857 Q τk( 1113857 +h

6K1 + 2K2 + 2K3 + K4( 1113857 (7)

K1 f Q τk( 1113857( 1113857 (8)

K2 fQ τk( 1113857 + hK1

2 (9)

K3 fQ τk( 1113857 + hK2

2 (10)

K4 fQ τk( 1113857 + hK3

2 (11)









1

1

2

2

3 3

4

45

6




End node ID(point)











Applicationlayer

Servicelayer

Technologylayer



generation

Unsteadyventilation

network model

Lanewaycenteline

data


Data source

Model andalgorithm

3Dvisualization


threejs Tornado











11

2

3

34

5

6

6

7

7

8

910

8

5

11

4

2

(a)

11

2

23

35

6

710

8

5

11

4

4

(b)







Time (s)0

ndash2

0

2

4

6

10 20 30 40 50 60 70 80 90

Air

volu

me r

educ

tion

(m3 s

)

Laneway 1

Laneway 4

Laneway 2Laneway 3




Time (s)0 20 40 60 80 100 120 140 160 180

ndash2

0

2

4

6


Air

volu

me r

educ

tion

(m3 s

)

(a)

Time (s)0 20 40 60 80 100 120 140 160 180

ndash3

ndash2

ndash1

0

1

2

3


Air

volu

me r

educ

tion

(m3 s

)

(b)



Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(a)


Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(b)





5 Conclusions





Data Availability




Acknowledgments


References








LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584




















dQ(τ)

dτ C

TCβC

T1113872 1113873

minus 1D (6)


i


Q τk+1( 1113857 Q τk( 1113857 +h

6K1 + 2K2 + 2K3 + K4( 1113857 (7)

K1 f Q τk( 1113857( 1113857 (8)

K2 fQ τk( 1113857 + hK1

2 (9)

K3 fQ τk( 1113857 + hK2

2 (10)

K4 fQ τk( 1113857 + hK3

2 (11)









1

1

2

2

3 3

4

45

6




End node ID(point)











Applicationlayer

Servicelayer

Technologylayer



generation

Unsteadyventilation

network model

Lanewaycenteline

data


Data source

Model andalgorithm

3Dvisualization


threejs Tornado











11

2

3

34

5

6

6

7

7

8

910

8

5

11

4

2

(a)

11

2

23

35

6

710

8

5

11

4

4

(b)







Time (s)0

ndash2

0

2

4

6

10 20 30 40 50 60 70 80 90

Air

volu

me r

educ

tion

(m3 s

)

Laneway 1

Laneway 4

Laneway 2Laneway 3




Time (s)0 20 40 60 80 100 120 140 160 180

ndash2

0

2

4

6


Air

volu

me r

educ

tion

(m3 s

)

(a)

Time (s)0 20 40 60 80 100 120 140 160 180

ndash3

ndash2

ndash1

0

1

2

3


Air

volu

me r

educ

tion

(m3 s

)

(b)



Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(a)


Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(b)





5 Conclusions





Data Availability




Acknowledgments


References








LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584





























Applicationlayer

Servicelayer

Technologylayer



generation

Unsteadyventilation

network model

Lanewaycenteline

data


Data source

Model andalgorithm

3Dvisualization


threejs Tornado











11

2

3

34

5

6

6

7

7

8

910

8

5

11

4

2

(a)

11

2

23

35

6

710

8

5

11

4

4

(b)







Time (s)0

ndash2

0

2

4

6

10 20 30 40 50 60 70 80 90

Air

volu

me r

educ

tion

(m3 s

)

Laneway 1

Laneway 4

Laneway 2Laneway 3




Time (s)0 20 40 60 80 100 120 140 160 180

ndash2

0

2

4

6


Air

volu

me r

educ

tion

(m3 s

)

(a)

Time (s)0 20 40 60 80 100 120 140 160 180

ndash3

ndash2

ndash1

0

1

2

3


Air

volu

me r

educ

tion

(m3 s

)

(b)



Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(a)


Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(b)





5 Conclusions





Data Availability




Acknowledgments


References








LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584



























11

2

3

34

5

6

6

7

7

8

910

8

5

11

4

2

(a)

11

2

23

35

6

710

8

5

11

4

4

(b)







Time (s)0

ndash2

0

2

4

6

10 20 30 40 50 60 70 80 90

Air

volu

me r

educ

tion

(m3 s

)

Laneway 1

Laneway 4

Laneway 2Laneway 3




Time (s)0 20 40 60 80 100 120 140 160 180

ndash2

0

2

4

6


Air

volu

me r

educ

tion

(m3 s

)

(a)

Time (s)0 20 40 60 80 100 120 140 160 180

ndash3

ndash2

ndash1

0

1

2

3


Air

volu

me r

educ

tion

(m3 s

)

(b)



Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(a)


Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(b)





5 Conclusions





Data Availability




Acknowledgments


References








LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584






















Time (s)0

ndash2

0

2

4

6

10 20 30 40 50 60 70 80 90

Air

volu

me r

educ

tion

(m3 s

)

Laneway 1

Laneway 4

Laneway 2Laneway 3




Time (s)0 20 40 60 80 100 120 140 160 180

ndash2

0

2

4

6


Air

volu

me r

educ

tion

(m3 s

)

(a)

Time (s)0 20 40 60 80 100 120 140 160 180

ndash3

ndash2

ndash1

0

1

2

3


Air

volu

me r

educ

tion

(m3 s

)

(b)



Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(a)


Pres

sure

redu

ctio

n (P

a)

002468

1012

20 40 60 80 100 120 140 160 180Time (s)

(b)





5 Conclusions





Data Availability




Acknowledgments


References








LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584






















5 Conclusions





Data Availability




Acknowledgments


References








LanewayID

StartnodeID

EndnodeID

Winddrag

(Nmiddots2m8)

Sectionarea(m2)

Sectionperimeter

(m)

Lanewaylength(m)

1 1 2 122336 1814 1578 322 3 4 114535 1867 1546 40103 5 6 145384 1828 1523 3304 7 8 000016 2400 2037 6125 9 10 000043 1406 1589 584




















a gis based unsteady network model and system applications

Documents