Application of On-line Coal Analyzer to Plant Performance Steve Benson Minnesota Energy Ingenuity Conference November 6-7, 2008 Great River Energy & Holiday Inn & Suites

Presentation Overview  Background on Microbeam Technologies Inc.  Fuel quality challenges for existing and future power systems  Microbeam advanced analysis database and predictive methods  Application of On-line analyzer Technology Standards Accuracy and precision Optimizing plant performance – Integration with MTI Indices for predicting plant performance  Conclusions and directions 2

3 Background of Microbeam Technologies, Inc.  Mission is to provide advanced analysis and interpretations of the impacts of fuel properties on combustion and gasification system performance.  Began performing analysis of samples using advanced electron microscopy methods in 1992  Client base includes electric utilities, state and federal government, coal companies, consultants, universities, law firms, research organizations, and others  Conducted >1000 projects, >5000 samples analyzed  Recipient of two Phase I Small Business Innovative Research (SBIR) grants from Department of Energy and National Science Foundation (NSF)  Completed and currently commercializing Phase II NSF SBIR technology for recovery of valuable materials from coal gasification systems

Annual Cost of Ash Deposition and Coal Quality in US Ash Deposition – $943 million Coal Quality – $267.3 million Total estimated cost $1.2 billion 4 (Harding and O’Connor, 2007)

Fuel Property Impacts on Performance  Moisture - efficiency, gas flow  Sulfur - emissions control  Nitrogen - emission control  Ash forming materials Slagging and fouling Abrasion and erosion Particulate emission  Heating value – firing rate 5

MTI Extensive Database  Over 5000 fuel and deposit analysis database Worldwide coal – brown, lignite, subbituminous, bituminous, anthracite Biomass – wood, switch grass, rice hulls, sunflower hulls, corn, lawn waste, manure, municipal solid waste, sewage sludge, and other Oil – Fuel oil (various grades) and petroleum cokes 6

MTI Extensive Database  Ash deposits and fly ash – combustion systems Pulverized fired boilers – wear (abrasion and erosion), wall slag, burner and overfire air eyebrows, pendent deposits, fouling deposits (secondary, reheat, primary, economizer heat transfer surfaces), SCR, air heaters, electrostatic precipitators, fabric filtration, SO2 scrubber materials (sludge, deposits). Cyclone fired boilers – wear (abrasion and erosion), cyclone slag, cyclone refractory, wall slag, fouling deposit (secondary, reheat, primary, economizer heat transfer surfaces), SCR, air heaters, electrostatic precipitators, and scrubber materials. Fluidized bed combustion systems (bubbling and circulating) – wear (abrasion and erosion), bed materials, bed agglomerates, in-bed heat transfer deposits, wall deposits, cyclone deposits, j-leg agglomerates, fouling deposits (convective pass). 7

Burners Slagging Convective pass fouling High temperature Low temperature Wear –Abrasion – fuel handling equipment –Erosion – heat transfer surfaces Slag Flow Behavior –T 250, T 80, T cv –Base/Acid, ash quantity Wall Slagging Index –Accumulation, strength, thermal conductivity –Soot blower Removability (Peak Impact Pressure (PIP)) Convective Pass Fouling –Silicate (High-Temperature) Index Accumulation, strength, thermal conductivity PIP –Sulfate (Low-Temperature) Index: Accumulation, strength, thermal conductivity PIP Particulate Control –Ash particle size – fine particulate index –Ash resistivity and cohesivity Indices for PC and Cyclone-Fired Systems 8

On-line analyzers - Prompt Gamma Neutron Activation Analysis  Califonium-252 neutron source  Neutrons emitted from the Cf-252 source are directed at the coal material.  Neutrons are captured by the nuclei of elements in the coal. The nucleus of the element becomes excited, and a gamma ray is released (prompt).  Energy of the gamma ray is characteristic of each element.  Gamma rays are detected by a sodium iodide detector. 9

On-Line Analyzer - ETI  Moisture meter  Ash meter – dual gamma  Elemental analyzer – prompt gamma neutron activation analysis 10

On-line Analyzer Output (ETI)  Ash content  Heating values  Sulfur  Carbon  Major inorganic constituents (ash-forming components) 11

Challenges  Standards  Data acquisition time – belt loading and speed  Precision and accuracy  Use of data 12

Standards – Impact of Averaging Times 13

Error as a function of acquisition time for base/acid ratio 14

Analyzer Location – Unit 1 MRY 15

Analyzer Location – Unit 2 MRY 16

Example of Application  Assessed fuel variability – in mine and as-fired Developed criteria for managing coal quality  Simplified slag flow, slagging, fouling, and ESP performance criteria were developed Software developed for FSEA to predict performance of coal as delivered 17

Example  Transition in coal quality  Calculated using MTI-developed software to predict: slag flow water wall slagging convective pass fouling deposit strength development 18

Change in Fuel Quality - CaO 19

Change in Fuel Quality – SiO 2 20

Change in Fuel Quality – Base/Acid Ratio 21

Change in Fuel Quality – Fe 2 O 3 22

Predictions based on On-Line Analyzer output  Cyclone performance – T 250, Cyclone index  Deposit strength  Wall slagging  Convective pass fouling 23

Slag T

Cyclone Performance Index 25

Deposit Strength -- Calculated at 2250°F 26

High Temperature Fouling – Growth Rate 27

Low Temperature Fouling – Growth Rate 28

Future Applications  Thermal Conductivity – based on deposit composition, growth rate, and strength development  Deposit removability – Peak Impact Pressure – based on deposit composition, growth rate, strength development, and thermal properties 29

Thermal Conductivity - Database (Calculated at 450°C) 30

Predicted Peak Impact Pressure (PIP) – Database 31

Summary and Conclusions  On-line coal analyzer technology is being installed at power plants and coal mines  Provides sufficient data for assessment of many operational problems  Utilized On-line analyzer to provide composition of coal delivered to plant to provide insight into slag flow challenges – significant cost savings  Data analysis and integration need more work 32

Future Directions  Advanced predictions Neural network applications Advanced indices  Integrate into operations Operating conditions Cleaning cycles Blending 33

34 Contact Information Microbeam Technologies, Inc James Ray Drive, Ste. 191 Grand Forks, ND Tel.: (701) Fax: (701)