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

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

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

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

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

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

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

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

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

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

Index Summary

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!!

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

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!!

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.

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!!

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.

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!!

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.

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