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   CSIRO’s  Coal  Logis/cs  Findings  

GRAHAM    O‘BRIEN  

62%  of  our  people  hold  university  degrees    2000  doctorates      500  masters  

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Top  1%  of  global  ins4tu4ons  in  14  of  22  research  fields        

Industry  focus:  1600  Australian    companies,  350+  Mul4-­‐na4onals  

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2  sites   Sydney    5  sites  Canberra    7  sites  

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CSIRO:  Who  we  are  

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Brisbane  6  sites    

Bribie    Island  

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FOOD,  HEALTH    &  LIFE  SCIENCE    

 

ENVIRONMENT                          

MANUFACTURING,  MATERIALS  &    MINERALS  

                 

ENERGY                        

INFORMATION  &    COMMUNICATIONS  

                   

What  we  do:  our  domain  focus  

12 Research  Divisions  11 Na4onal  Research  Flagships  + Na4onal  Research Facilities

and Collections + Transforma4onal    

Capability  Pla]orms

Australian  Coal  Industry  Overview  

World’s second largest exporter – about 28% of world coal market

2010-2011 Coal production

raw black coal: 454Mt saleable black coal: 345Mt

Exports •  283Mt ($A44 billion)

Black and brown coal account for 75% of Australia’s electric power

Australia’s major resources and energy exports, 2010-11

CSIRO  Coal  Mining  Research    to  maximise  the  benefits  from  Australia’s  coal  resources  in  an  environmentally  and  socially  responsible  manner.  

Research Stream Scope

Mining engineering Development of new mining systems, design and operational control techniques

Mining automation Development of automation of mining equipment, sensing and communication systems

Mine environmental technologies

Development of new mining environment mitigation methods and remedial techniques

Coal processing and Logistics

Development of new coal cleaning methods and rail and port engagements.

Largest coal mining research program in Australia

More than 70 CSIRO researchers

4 key research areas

Established a solid track record of success and delivered significant benefits

Mining ROM/CPPP Loadout Transport Port Ship    Issues  that  have  been  iden4fied  are: • S"cky  coal:  Issues  with  transfer  hoppers,  conveyors,  rail  wagon  loading  and  unloading,  and  shiploading  and  unloading. • Cost  and  efficiency,    scheduling,  op4mizing  rail  transport  and  port  throughput  • Environmental.  Noise,  dust,  water,  spontaneous  combus4on.  

Efficient    material  transfer  is    cri4cal  at  all  parts  of  the  coal  chain.  Our  coal  logis4cs    ini4a4ve  specifically  focuses  on  issues  from  rail  loadout  through  to  shiploading.        

Trains  are  loaded  for  transport  to  the  coal  ports.  Coals  which  give  problems  during    unloading  are  known  colloquially  as  s/cky  coals.    

Non    s4cky  coals  unload  well  from  boPom  dump  rail  wagons.  

S4cky  coals  give  hang  up    which  impedes  unloading  

Loading method and loadout design contributed to coal hangup during unloading.

Loaded with a front end loader Loaded with an impact loader

The  RG  Tanna  Coal  Terminal  (Gladstone)  determined  that  many  mines  provided  s4cky  coal  products.    In  2005  GPA  es4mated  that  s4cky  coal  cost  approximately  2%  of  port  throughput  (approximately  800,000  tonnes/  year).    Other  QLD  coal  ports  had  similar  issues  with  s4cky  coal.        

Factors Outside of GPA Control

Wagon Design (134tph)

Waiting Dozers

Relocate Tripper

Sticky Coal

Barcode Resolution (wrong barcode, no barcode, damaged barcode)

Split Trains

QR Delays

Incorrect Mine Advice

Train Speed

GPA Can Influence by

GPA can Control:- Correct unloading procedure, skill levels, automation, design, allocation of responsib. System performance - delays of 60%

Electronic barcode

Reporting weekly to QRMonitoring, partnering

Scheduling

Scheduling

Monitor/reportReporting

Ploughing

Electronic data transfer

14% (218tph)

10%

6% 8%

1%

1%

Average of 660tph per split connote

5 Percent of trains jack hammered by mine

Jackhammer Trains by Mine 2003/2004 Start Train/End Train

HJ Roll.1%

Callide0%

SBW6%

Gregory4%

Yarrabee10%

Moura2%

Kestral5%

Oakey Crk1%

Ensham10%

Cook3%

BlackWater11%

Curragh24%

Jellinbah23%

What  makes  some  coal  s/cky?  

Coal properties? (moisture, fines, ash content)

Wagon design? (slope sheet angle, door width, wall material)

Loading forces? (loading method, drop height)

Travel forces? ( vibration, travel time, adverse weather)

Field and laboratory work was conducted to identify and quantify these factors. This work was supported by the coal producers, Qld Rail and the coal ports.

Quantifying coal handleability- laboratory testing

Consolidating pressure (t/m2)

0 2 4 6 8 10 12

Dis

char

ge (k

g)

0

10

20

30

40

50

A B C D E F.2 (PP) G1 H1 (thermal) H2 (PCI)

Free flowing coalsSticky coals

Lab scale test which uses approx 90 kgs of coal replicates the arches formed in full scale wagons. It enables us to benchmark coals and determine size and moisture effects.

We  developed  a  laboratory  scale  and  a  pilot  scale  test  for  assessing  coals.  

Centre sill

Coal arch

Acoustic Measuring devices

Video camera

Arch profile in a rail wagon. Arch profile in CSIRO lab scale test unit.

The  lab  test  unit  shows  same  arch  profile  as  observed  in  rail  wagons  

Issues associated with the unloading of coal wagons are quite complex. To develop solutions to complex problems require buy in from all parties. To a large extent this was achieved for this research. Sticky coal is currently less of an issue than it was previously. Changes made at one position in the chain can impact on other links in the chain. New coals with different properties will come into the system in the next few years. Unknown at this stage is whether they will be sticky.

Project  findings  

Remnant  Coal  Detec/on  

Undesirable  residual  coal  in  the  wagons  of  emp4ed  coal  trains  •  Reduced  capacity  of  coal  wagons  –reduced  produc4vity  

•  Causes  cross-­‐contamina4on  of  coal  with  other  mines:  poten4ally  reject  wagons  

•  Dries  out  eventually,  causing  coal  dust  pollu4on  from  the  trains  

•  Coal  can  jam  in  the  wagon  doors,  leading  to  spills  and  derailments  

•  Large  coal  “hangups”  can  occur:  the  load  fails  to  dump,  hanging  in  a  bridge  that  breaks  later,  causing  spillage  and  poten4al  derailment  

1.  RCD  is  a  system  that  monitors  coal  train  wagons,  looking  for  coal  hangups  (currently  installed  on  two  dump  sta4ons  at  GPC—R  G  Tanna).  

2.  GPC  have  also  given  considera4on  to  a  proposed  future  automa"c  cleaning  system,  with  remote  operator  supervision,  will  engage  the  jackhammers  to  clear  the  detected  hangup.  

Remnant  Coal  Detec/on  System  

Remnant  Coal  Detec/on    Enabling Technology for Detection & Measurement

•  Several eye-safe commercial options

•  Single-point laser scans using a rotating mirror to develop a 2D line

•  Movement of target can create a 3D profile

Remnant  Coal  Detec/on  

Remnant  Coal  Detec/on  

Port  efficiency:  As  the  port  is  area  constrained  they  need  to  maximize  the  use  of  their  footprint.  Also  shown  are  dust  monitoring  sites.  

We  are  currently  undertaking  a  Laser  Stockpile  Mapping  Trial  at  the  R.G.  Tanna  Coal  Terminal  to  assist  them  to  op/mise  the  use  of  their  footprint,  and  hence  improve  annual  port  capacity.  

 

The low reflectivity of coal is a major limitation on the use of conventional laser range measurement systems for mapping coal stockpiles.

Modeling  is  an  important  tool  for  op4mizing  material  transfer  opera4ons.    

Computa4onal  Modelling  Applica4ons  in  Coal  Transport  and  dust  genera4on.  

This  work  was  done  by  Paul  Cleary’s  research  group  in  Melbourne.    

Paul.Cleary@csiro.au    

   

Presenta4on  4tle    |    Presenter  name  

Coal  Discharge  from  Rail  Wagons  

Coal  transport  from  mine  to  terminal  is  vital  to  Na4onal  Coal  Industry.    

The  unloading  4me  for  a  single  wagon  constrains  the  rate  at  which  coal  can  be  delivered.  

 •  Discharge  4me  depends  on  coal  flowability  which  depends  on:    –     par4cle  size  distribu4on  –     par4cle  shape  distribu4on  –     material  proper4es  –     cohesive  forces  between  wet  coal  

Improvements  in  rail  wagon  design  require  deeper  understanding  of  the  interac4on  between  granular  coal  mass  and  the  internal  geometry  of  the  wagon.  

Coal  Discharge  from  Rail  Wagons  

Bradken  Rail  Wagon  Design  

 Dimensions  of  a  single  wagon  –   Height  =  3.7  m  

–   Length  =  14.6  m  

–   Width  =  3.0  m  

 5  compartments,  8  doors  which  are  scheduled  to  open  in  pairs  

 Coal  unloads  while  the  train  is  in  mo4on.  –  Adjacent  pairs  of  doors  are  triggered  to  open  mechanically  in  a  coal  terminal  by  a  fixed  mechanism  beside  the  rail  line.    

–  The  4me  interval  between  each  set  of  doors  opening  is  determined  by  the  speed  of  the  train  through  the  terminal.  

 

Coal  Flow  in  Rail  Wagons  

DEM  Coal  par5cles    Real  coal  mass  with  a  top  size  of  50  mm  was  modelled  using  cohesive,  oversized  80  mm  DEM  par4cles    

485,000  par4cles    

DEM  superquadrics  –  Shape  distribu"on:  3.0  –  6.0    –  Aspect  ra"os  from  0.5  –  1.0  

A  cohesion  Bond  number  of  0.5  was  used  (based  on  comparison  between  a  50  mm  DEM  simula4on  and  pilot  hopper  experiments)  

Pairs  of  wagon  doors  were  scheduled  to  open  at  6  s  intervals  

 

Coal  Discharge  from  a  Rail  Wagon  

•  Sloping end walls provide strong resistance to flow. End compartments slower to fully discharge than inner compartments

•  Considerable flow between compartments around and through the baffles. Preferential central flow through each door with coal mass retarded at walls.

•  In the inner compartments, active regions of rapid coal flow are observed to extend from discharging door up to the coal surface

Dust dispersal modelling on a conveyor chute using a coupled discrete element and CFD method, CFD 2011, Trondheim, June 2011

   

Model  –  Physically  realis4c  dust  model  –  Based  on  experimentally  derived  expression  

Computa4onal  method  –  Combined  DEM/CFD  method  

Dust  Modelling  

Par4culates  comes  from  many  different  sources:  •  Natural  sources  

–  sea  spray  –  animal  and  plant  maPer  –  wind  blown  stone  dust  –  wildfires  –  etc.  

•  Human  sources  –  Mining  ac4vi4es  –  plas4c  and  paint  –  fuel  combus4on  –  rust  –  etc.  

Introduc/on  to  Air  Par/culates  (Dust)  

200µm

10µm

Mackay  Ports  •   Part  of    a  large  study  being  conducted  •  Assisted  a  coal  producer  address  a  community  complaint      

Newcastle  •  Assessment  of  rou4ne  samples  •  Assessment  of  compliance  samples  Gladstone  •  Assisted  with  Port  community  complaints  

Brisbane  •  Assisted  with  community  concerns  

Current  studies  

http://en.wikipedia.org/wiki/File:Australian_Energie_ressources_and_major_export_ports_map.svg

Air  Par/culates  –  Size  Does  Mafer    

Gets  trapped  by  the  body’s  

natural  defense  mechanisms  

Does  not  enter  body  

Small  enough  to  enter  the  airways  

Gets  trapped  in  the  lungs  

Small  enough  to  enter  the  blood  

vessels  

Gets  deposited  around  the  body  in  various  organs  

Nuisance  Dust  SePles  quickly  and  can  easily  be  

seen  

Respirable  Dust  Stays  suspended  for  a  long  4me  and  

is  too  small  to  see  

Smaller  than  2.5  microns  Small  than  10  microns  

Typically  of  industrial  origin  

Total  airborne  

par/culates  

Bigger  than  10  microns  

Typically  of  natural  origin  

“Dust”   PM10   PM2.5  

Category  

Technical  Name  

Size  

Origin  

Health  Implica/ons  

•  14  bit  colour  images  are  collected  using  an  air  lens  

•  Images  are  mosaiced  together  to  provide  detail  on  mul4ple  complete  par4cles.    

•  Depending  on  magnifica4on  used  1  pixel  in  the  images  represents  0.32  or  0.12  microns.  

•  This  provides  informa4on  on  each  individual  par4cle  in  the  sample.  

 

 

 

CSIRO’s  Coal  Petrography  Laboratory  uses  op4cal  reflected  light  micro.scopy    for  the  analysis  of  dust  samples  

50µm

Unidentified Mineral

Coal

Soot

Paint

50µm

Unidentified Mineral

Coal

Soot

Spore

2 images collected as part of a study of dust from the Mackay region

0

2

4

6

8

10

12

14

0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 1.50 1.60 1.70 1.80 1.90 2.00 2.10

%

Random Vitrinite Reflectance (%)

Reflectance histogram of the coal particles indicate that the coal dust came from multiple sources

Analysis  of  a  Dust  Sample  from  Mackay  Image  Collec/on  

500µm

50µm

100 Images mosaiced together covering an area of 4.5x3.5 mm

Analysis  of  a  Dust  Sample  Original  Image  

Analysis  of  a  Dust  Sample  Characterised  Image  

Coal Non-coal

Par/cle  informa/on    

Image Characterised image Area (pixels)

Area (um)^2

Particle width (um)

Particle length (um) Grain Class

227 23.2 4 10 non-coal 1055 108.0 11 16 non-coal 475 48.6 7 10 coal 621 63.6 7 13 non-coal 24113 2469.2 49 85 non-coal 297 30.4 6 10 non-coal 1871 191.6 14 21 non-coal 9015 923.1 32 48 coal 3024 309.7 20 31 non-coal 348 35.6 6 13 non-coal 284 29.1 6 10 non-coal 1090 111.6 12 13 non-coal 1085 111.1 11 25 non-coal 235 24.1 5 8 non-coal 1026 105.1 11 24 non-coal 1613 165.2 12 23 non-coal 360 36.9 6 9 non-coal 3762 385.2 24 36 non-coal 6046 619.1 24 55 non-coal 1361 139.4 13 19 non-coal

Results  of  Analysis  by  Size  (volume  %)    SIZE   AMOUNT  COAL     AMOUNT  NON-­‐COAL     TOTAL   NUMBER    OF  GRAINS  

PM2.5*  (>2.5um)  

0*   0*   0*   10*  

PM10  (2.5><-­‐10um)  

1   18   19   8155  

Nuisance  Dust  (>10um)  

3   78   81   4667  

Total   4   96   100   12832  *Limited  data  available  on  -­‐2.5  micron  par4cles  as  images  were  collected  with  20x  lens  

•  Coal  only  4%  of  the  sample  •  Only  ¼  of  the  coal  present  <10micron  •  More  than  80%  of  the  sample  is  nuisance  dust  (greater  than  10  micron)  •  Limited  data  on  the  >2.5micron  at  200x  magnifica4on  •  Results  validated  through  manual  point  coun4ng    

 

Size  distribu/on  of  par/cles  

0  

10  

20  

30  

40  

50  

60  

<2.5   2.5<10   10<30   30<60   60<100   100<140  

Volume  %  

Size  (microns)  

Size  Distribu/on  

coal  

non-­‐coal  

Analysis  of  a  Dust  Sample  from  Brisbane  Image  Collec/on  

Over 100 mages collected of 3mm diameter circle containing sample

•  Informa4on  determined  on  in  excess  of  2000  par4cles  

•  Less  than  1%  of  par4cles  iden4fied  as  coal  

•  Reflectance  values  for  the  coal  par4cles  was  around  0.5%  –  This  is  consistent  with  literature  values  for  low  rank  thermal  coals  from  West  Moreton  Coal  Fields  which  are  railed  through  Brisbane  to  the  Port  of  Brisbane  

•  Majority  of  non-­‐coal  par4cles  appeared  to  be  soot  with  a  median  size  of  20µm    

 

Results  of  Analysis  –  Brisbane  Sample    

Analysis  of  a  Dust  Sample  Example  Par/culates  at  200x  magnifica/on  

50µm

Mineral

Coal

Soot

Quartz

50µm

Unidentified Mineral

Coal

Soot

Spore

Analysis  of  a  Dust  Sample  Example  Par/culates  at  500x  magnifica/on  

10µm

Coal

Mineral

Flyash

Plastic

Coal

Paint

10µm

Mineral

        Non-­‐Coal  Total   No  of  

grains  Size  class   Grain  class   Coal   Fly  ash   Soot   Bright  Minerals  

Dark  Minerals   Unidentified  

Nuisance   +30   13.9   0.1   0.3   0.0   1.0   0.0   15.3   58  Nuisance     -­‐30  +  10   45.0   0.3   0.0   0.0   3.8   0.0   49.1   1056  Inhalable   -­‐10  +  2.5   28.4   0.0   0.0   0.0   5.2   0.0   33.6   6449  Respirable   -­‐2.5   1.6   0.0   0.0   0.0   0.4   0.0   2.0   1883       Total   88.9   0.4   0.3   0.0   10.3   0.0   100.0   9,446  

Example image (one of about 2,000) collected during the analysis of a sample of dust from a sampling station at a coal port.

        Non-­‐Coal  Total   No  of  

grains  Size  class   Grain  class   Coal   Fly  ash   Soot   Bright  Minerals  

Dark  Minerals   Unidentified  

Nuisance   +30   0.0   0.0   0.0   6.7   29.9   1.9   38.5   61  Nuisance     -­‐30  +  10   0.1   0.2   0.1   20.1   28.4   1.3   50.1   535  Inhalable   -­‐10  +  2.5   0.1   0.0   0.0   3.4   7.3   0.0   10.9   1260  Respirable   -­‐2.5   0.0   0.0   0.0   0.1   0.5   0.0   0.5   333       Total   0.2   0.2   0.1   30.2   66.2   3.1   100.0   2,189  

Example image (one of about 2,000) collected during the analysis of a sample of dust from a sampling station a distance from the coal port.

 

 

To  improve  the  safely  and  efficiency  of  coal  transporta4on  from  mine  to  port  and  onto  markets  in  a  socially  responsible  manner  which  has  zero  environmental  impact,  requires  commitment  from  all  stakeholders.  

 

We  believe  that  applied  research  is  an  important  component  for  obtaining  incremental  improvement  for  exis4ng  opera4ons  and  step  change  improvement  for  greenfield  opera4ons.            

 

   

Conclusions  

           Graham  O'Brien      CSIRO  Energy  Flagship                                                                                      Stream  Leader-­‐  Enhanced  Coal  Cleaning  and    Logis/cs                    Queensland    Centre  of  Advanced  Technologies                                                                                                                                      1  Technology  Court  phone                                                                                            +61  733274457  Pullenvale  4069                                                                                                                                        mobile  0417612374  Brisbane  QLD  Australia                                                                                                                Graham.O’Brien@csiro.au                                                                                                                                                      

       

Thank you.

Questions?