COAL RESERVES AND RESOURCES:

Transcription

1 COAL RESERVES AND RESOURCES: Practical Yield and Coal Quality Aspects Andrew Swanson and Wes MacKinnon QCC Resources

2 Introduction QCC has been involved in supporting exploration and mine planning for almost 30 years Seen a range of approaches and outcomes In the time available today would like to share our experiences in three key areas Moisture and density Practical yields and process strategies Maximising the information from coal quality data Essential that a resource be adequately characterised and a sound geological model created requires proper bore core coverage Page 2

3 Defining Reserves from Resources Modelled from Thickness In situ Density (RD + M IS ) Washability Coal Properties Product Requirements M IS M ROM In Situ Coal (volume) In Situ Tonnes (at M IS ) ROM Tonnage Practical Yield Algorithm M ROM Mining Parameters (Loss & Dilution) M Washed Product Tonnes Page 3

4 Moisture and Density Page 4

5 Moisture Moisture is a critical coal quality parameter. It impacts directly on: the calculation of reserves, ROM tonnages for scheduling washed coal tonnages for yield delivered tonnages for price adjustment. Currently, there is no standard industry approach to predicting a moisture profile for a new resource. The recent trend has seen the need to evaluate lower rank resources in new and remote areas, requiring a more robust approach to moisture prediction. Page 5

6 Moisture Association with Coal Note that moisture properties vary with ash! Page 6

7 Moisture Profiles Each coal type has a moisture profile which shows the relative moisture levels for Mad, Mis, Mrom and Mwc Page 7

8 Calculation of Coal Reserve Tonnages In-situ tonnage is equal to the volume of the resource times the relative density Preston and Sanders (1993) the RD to be used must be the relative density that corresponds to the condition of the coal in the ground, RDis.. not RDad. RDis = RDad (100 - Mad) / (100 + (RDad (Mis - Mad)) - Mis) Need to predict and make use of Mis Page 8

9 Summary Moisture and Density Density translates volumes into tonnages In situ density a function of coal density and in situ moisture Need to correct tonnages for ROM and washed moistures Page 9

10 Practical Yield Page 10

11 Coal Preparation Simulations Feed DMC I DMC II Product 1 Product 2 Reject Tailings TBS Spirals Convert detailed sizing and washability data into reliable estimates of plant yield and product ash. Need for pre-treated LD cores LIMN package. Page 11

12 Practical Yield Algorithms Make use of plentiful slim core data modelled washability and quality data generated and used with a procedure to give optimum practical yield A reliable procedure must account for washability changes; dilution & coal loss; seam mix; product specifications coal preparation other quality parameters (TS, SE, etc) Page 12

13 Yield (%) Practical Yield Algorithms From simulations of detailed washability Modelled Slim Core Washability Expanded Slim Core Washability Practical Yield-Ash Product Ash or RD target Slim Core 10.0 Simulated Ash (%) Product Yield & Ash Page 13

14 Practical Yield Algorithms The algorithm to be applied will entail: check compatibility between raw ash and F/S for each seam in each time period expansion of F/S data for each seam o coal washability model derive tables of coal only practical yield-ash for each seam o o practical yield-ash relationship developed via LD simulations calculate dilution effect allocation of seams to product types determine washing set-points raw/washed product calculations By applying to a ROM schedule annual variations are captured and alternatives investigated Page 14

15 Cut-point for Mine B Tonnes (ar) Scenario Development Allows optimal development path to be established 7,000,000 6,000,000 5,000,000 4,000,000 3,000, ,000,000 1,000, Year Option 1 - Bypass Seams 2A/2B & Wash Balance Option 2 - No Underground, Bypass Seams 2A/2B & Wash Balance Option 3 - No Underground, Wash All Mine B Coal From Year Year Option 1 - Bypass Seams 2A/2B & Wash Balance Option 2 - No Underground, Bypass Seams 2A/2B & Wash Balance Option 3 - No Underground, Wash All Mine B coal From Year 4 Page 15

16 Hydrogen dmmf %. Ro,max Coal Quality Aspects Seyler's Chart (augmented) Prime Hard Coking Coal 40 Moisture Lines Semi-inertinite CARBONACEOUS 30 SEMI- ANTHRACITE 1 SUB-HYDROUS 20 ANTHRACITE 36 Vol. Matter 10 dmmf % 34 Zero oxygen line BITUMINOUS Oxygen dmmf % PER-HYDROUS Carbon dmmf % LIGNITOUS CSN Lines 20 Gross CV dmmf, MJ/kg Liptinite Ro,max Line Seyler s Chart Page 16

17 Coal Quality Data Must cover all aspects of coal characterisation to allow consideration of marketing and utilisation Must consider regional variation After validation, data can be sorted and correlated to unearth a wealth of additional information on the coal resource. For precise correlations, data sets must be discrete, not mixed. Correlations show the bounds of possibilities for coal properties, and possible issues. In a sense, it is more an Identikit picture of the whole deposit. Page 17

18 Relative Density d Interpreting and RD Plot Iron bearing minerals present n = Minerals range Density of pure dry coal = Ash d,% Page 18

19 Moisture ad,% Interpreting a Moisture Plot n = Ash ad,% Page 19

20 Volatile Matter d, % Interpreting a Volatile Matter Plot Ash d, % n = 1101 Page 20

21 Specific Energy d,mj/kg Interpreting a CV Plot n = Ash d,% Page 21

22 Coal Grain Analysis (CGA) Coal Grain Analysis (CGA) automated petrographics analysis of each individual particle (grain) Relative Density can be calculated for every grain The ability to predict coal washability at topsize, from small samples (10 to 15 grams), will be of huge benefit to reserve definition, coal preparation design and operation. CGA appears to offer a more absolute, effective and economical manner in which to characterise and determine coal reserves. CPP FEED WASHABILITY PREDICTION FROM SMALL TOPSIZE SAMPLES, ACARP Project C18041, July 2016, Atkinson et al Page 22

23 Conclusion One cannot have a properly defined resource without A good understanding of coal moisture and density characteristics that allow accurate computation of in situ tonnages A proper estimate of product yields that vary in a cord with the mine plan and the nature of the coal deposit A sound knowledge of coal quality to confirm the likely utilisation of a coal and its potential standing in the market Page 23