1. COMPARISON OF ASH DEPOSITION PROPENSITY PREDICTED BY INDICES WITH ACTUAL ASH DEPOSITION BEHAVIOUR
Anthony Williams
Robert Creelman
Terry Dixon
A&B Mylec
Clearwater Clean Coal
Technology Conference
June 5 to 9

2. BACKGROUND
IMPACT used to model the utilisation performance and value-in-use of coals in power plants.
Evaluate single coals in power plants
Blending (binary and ternary)
Clean coal evaluations
Modelling multiple units (e.g. India)
Customised versions used by power plants in Australia
Coal comparison tool - Balance between accuracy and complexity of model.
Google Map Data 2016

3. BACKGROUND - MODEL Ranking adjusted for furnace temperature
Current ash deposition model based on traditional indices (5)
Weighting given to indices with an overall ranking determined
0 to 5 low propensity
5 to 10 moderate propensity
10 + severe
Ranking adjusted for furnace temperature
Ranking determines the effect of the ash deposition on Operating and Maintenance cost
Lost Revenue
Load Reduction

4. OVERALL PROJECT OBJECTIVE
Improve the estimation of a coal’s propensity to cause ash deposition problems.
Improve the estimation of the cost of ash deposition on a power plant’s operating and maintenance budget.
Improve the estimation of the coal price discount for ash deposition propensity.
CURRENT OBJECTIVE
Evaluate current indices with a world coal quality database
Compare indices with experience with several Australian coals

5. ASH DEPOSITION INDICES
Indices have been used for over 50 years
Created based on particular set of coals, predominantly in the northern hemisphere
Authors unaware of indices created using a large international coal dataset
Formulae use a variety of ash chemistry concentrations and ash fusion temperatures
Indices have grading systems to indicate problematic values e.g. if index > 10 then the coal will cause slagging problems.
Silica Ratio
Base/Acid Ratio
Iron + Calcium
CV1426°C
Iron Index
Iron/Calcium Ratio
T250
Slagging Factor
Multi-Viscosity
Slagging Temperature

6. Coals – Domestic and Export
Coal 1 – 5300 kcal/kg
Coal 2 – 4800 kcal/kg
Coal 3 – 4400 kcal/kg
Coal 4 – 3500 kcal/kg

7. ASH VISCOSITY RELATED INDICES
Indonesia/USA
Severe
27 % - Severe 4 1 2 3
ASH VISCOSITY RELATED INDICES
14 % - Low
Indonesia/USA
Severe
21 % - Low 3 2 1 4
34 % - Severe

8. ASH FUSION TEMPERATURE RELATED INDICES
Indonesia/USA
Severe
55 % - Low 2 3 4 1
ASH FUSION TEMPERATURE RELATED INDICES
13 % - Low
Indonesia/USA
Moderate 3 2
60 % - Low 4 1
1 % - Severe

9. ASH CHEMISTRY RELATED INDICES
Indonesia/USA
Severe
22 % - Severe 1 4 2 3
ASH CHEMISTRY RELATED INDICES
19 % - Low
0 % - Severe
USA
Moderate 1 4 2 3
97 % - Low

10. Indonesia/USA
Severe
32 % - Severe 3 1 2 4
32 % - Low
Indonesia/USA Severe
16 % - Low 2 3 4 1
30 % - Severe

11. Index Summary

12. Coal 1 Coal Details Deposition Propensity Nothing to worry about!!
5300kcal/kg AR
13.5% Ash
Rank: High Volatile C Bituminous
Domestic and Export Coal
Deposition Propensity
Indices: Moderate
ABM Model: Moderate
Nothing to worry about!!

13. Coal 1 Continued Deposition Characteristics Failure of Indices
Significant fouling of superheater tubes resulting in shutdown.
80-90 tonnes of material was removed costing > $0.5million.
Plant design and operation related factors were ruled out as contributors
Coal mineralogy changed and caused slagging. Iron cordierite reduced. Siderite increased.
Failure of Indices
Failed to predict severity of slagging resulting in expense repairs and downtime
Mineralogy Considerations
Initiation Layer – Fe rich layer, reacts with tubes.
Sinter Layer - Bulk of deposits
Molten layer – molten layer on external surface

14. Coal 2 Coal Details Deposition Propensity 4800kcal/kg AR 25% Ash
Rank: High Volatile C Bituminous
Mine Mouth Power Plant
Deposition Propensity
Indices: Low to Moderate
ABM Model: Low to Moderate
She’ll be right Mate!!

15. Coal 2 Continued Deposition Characteristics Failure of Indices
Significant fouling of superheater tubes and shedding resulting in shutdown.
Deposit difficult to remove due to deposit mass falling and agglomerating on a flat surface.
Plant design and operation related factors contributed to the problem.
Failure of Indices
Failed to predict severe AD event
But plant and operation related factors not considered with indices
Mineralogy Considerations
Initiation Layer – Fe rich layer was weak.
Sinter Layer – Bulk of deposit.

16. Coal 3 Coal Details Deposition Propensity 4400 kcal/kg AR 31% Ash
Rank: High Volatile B Bituminous
Mine Mouth Power Plant
Evaluation of new coal supply
Deposition Propensity
Indices: Low to Moderate
ABM Model: Low
Nothing to worry about!!

17. Coal 3 Continued Deposition Characteristics Failure of Indices
Significant slagging on walls resulting in a sudden drop in ash output followed by a collapse of deposits on walls.
Major ash handling problem. Could not remove bottom ash.
Coal additive resulted in continuous self shedding of slag.
Failure of Indices
Failed to predict slagging characteristics of coal.
Plant and operational factors ruled out.
Mineralogy Considerations
Molten Layer – Iron/Potassium combination significantly produced molten material.

18. Coal 4 Coal Details Deposition Propensity 3500kcal/kg AR 20% Ash
Rank: Sub Bituminous C
Mine Mouth Power Plant
Deposition Propensity
Indices: Moderate
ABM Model: Moderate
No way will this coal cause major problem!!

19. Coal 4 Continued Deposition Characteristics Failure of Indices
Significant fouling of furnace and superheater tubes resulting in shutdown.
Deposit difficult to remove resulting in significant cost through loss of revenue plus repairs.
Plant design and operation related factors are not significant
Failure of Indices
Failed to predict severe ash deposition because the mine considered iron content was the problem. Sodium was the problem.
A specific sodium index was developed which proved to be very accurate.
Mineralogy Considerations
Presence of sodium reduced the melt temperature of slag. Significant sodium was organically linked.

20. CONCLUSIONS
A range of ash deposition propensities was predicted using ash deposition indices
Ash deposition indices currently cannot estimate ash deposition propensity well
Accuracy for ash deposition prediction without considering plant factors can be questionable.
There are occasions where the plant factors are either secondary or not relevant
There is the potential to improve the indices by incorporating coal mineralogy into index calculations
Develop relationships between standard coal properties and coal mineralogy
Ash deposition can occur via different mechanisms and any developed model should reflect that
A model for ash deposition should include multiple mechanisms