Online Coal Analyzers in the US Utility Industry



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The world leader in serving science Online Coal Analyzers in the US Utility Industry Richard Woodward Thermo Fisher Scientific ---------- EPRI Webcast 7 October 2008

Presentation Outline PGNAA principle of operation Background Common utility applications PGNAA Analyzers Two different models Choosing between them Getting the most value from the analyzer Software packages 2

Prompt Gamma Neutron Activation Analysis (PGNAA) Thermal Neutron Nucleus Excited Nucleus Stable Nucleus Gamma Ray γ 3

Principle of Operation: Thermo Scientific CQM Shielding Slider Plates Neutron Moderator / Reflector Neutron Shield Cf-252 Neutron Source Fission Gamma Ray Shield NaI Crystal Photo-multiplier Tube 4 4

Advantages of (PGNAA) Prompt Gamma Neutron Activation Analysis Lowest cost of neutrons Lowest radiation while in operation Most uniform sensitivity to entire coal cross-section, leading to superior accuracy When coal source is known, has best algorithm for calorific value determination Best reliability no chance of failure of ionization source Most mature online analysis technology Thermo Fisher alone has sold more than 600 analyzers in the past 25 years Large installed base permits service staff location close to customer 5

Background on elemental coal analyzers Online elemental coal analyzers have been in use for 25 years EPRI was an early sponsor Analyzers measure sulfur ash moisture caloric value all major ash constituents Analyzer use in power plant applications growing 6

Utilities are becoming a more significant part of the analyzer user population % of Thermo Scientific Coal Analyzers Sales going to Utilities Percentage 60% 50% 40% 30% 20% 43% 43% 52% 10% 0% 0 1998-2000 2001-2003 2004-2006 2007-present 7

Most utility analyzers are belt analyzers and are found on bunker feed Mix of Therm o Scientific Coal Analyzers sold to Utilities since 2000 At the mine--cqm, 14% At the mine--eca, 8% Bunker Feed--ECA, 49% Receipt monitoring--cqm, 8% Full-flow 62% Sample stream 38% ----------------------------- Bunker feed 65% Receipt monitoring 13% At the mine 22% Receipt monitoring--eca, 5% Bunker Feed--CQM, 16% 8

Bunker Feed Application Accounts for 65% of all Thermo Scientific utility installations since 2000 Process goals include Boiler optimization Emissions compliance Ensuring proper sorting between scrubbed and unscrubbed units When there are parallel bunker feed conveyors the solution can be Two full-flow analyzers One sample-stream analyzer, fed by two primary samplers 9

Receipt monitoring application Constellation Energy s C. P. Crane plant in Maryland Installed full-flow analyzer Monitors all incoming rail shipments as conveyed to yard stockpile Operator Console located in coal yard office Luoyang Longyu power plant in China Use an auger sampler to sample each incoming truck Sample increment sent through sample stream analyzer to determine if truck is on spec Plant saved $375K in six months 10

Two Types of PGNAA Elemental Analyzers Sample stream Typically flow rates of 2-10 tph Primary save stream or secondary rejects Most accurate analyzer in industry Constant analysis geometry Full flow Most effective on belt sizes between 30 and 60 inches Accuracy best when flow variations are minimal Thermo Scientific (formerly Gamma-Metrics) CQM Thermo Scientific ECA 11

Choosing between sample stream analyzer (CQM) and full flow analyzer (ECA) Any one of the following conditions could tip the scales in favor of a sample stream analyzer Stringent accuracy requirements Highly variable belt loading Top sizes greater than 4 inches Conveyor belt conditions Steel corded belts Belt sizes of 72 inches and greater Installation conditions Two parallel belt conveyors, which might be able to share in a multiplexed manner a single sample stream analyzer Existing sampling system with which a sample stream analyzer can be easily integrated 12

Choosing between sample stream analyzer (CQM) and full flow analyzer (ECA) Any one of the following conditions could tip the scales in favor of a sample stream analyzer Stringent accuracy requirements Highly variable belt loading Top sizes greater than 4 inches Conveyor belt conditions Steel corded belts Belt sizes of 72 inches and greater Installation conditions Two parallel belt conveyors, which might be able to share in a multiplexed manner a single sample stream analyzer Existing sampling system with which a sample stream analyzer can be easily integrated If none of these are issues, a full flow analyzer should be sufficient 13

Full Stream Analyzer Accuracy Compared with Sample Stream Analyzer Accuracy Full flow analyzer ~ 15-50% Sample stream analyzer Left-to-right sensitivity variance Varying belt loading Vertical sensitivity variance 14

Accuracy comparison: sample stream vs. full flow Arch Mountain Laurel CQM: Lab vs. Analyzer Ash Comparison August 2007 12 10 RMSD = 0.46% Standard Error of the Estimate = 0.35% Laboratory Ash (Wt. %) 8 6 4 2 12 Arch Mountain Laurel ECA: Lab vs. Analyzer Comparison August 2007 RMSD = 0.51% Standard Error of the Estimate = 0.44% 10 0 0 2 4 6 8 8 10 12 Analyzer Ash (Wt. %) When flow is consistent and belt is fully loaded, the accuracy difference is slight; in this example 20% worse Standard Error of the Estimate Laboratory 6 4 2 0 0 2 4 6 8 10 12 Analyzer 15

The critical importance of analyzer and reference system accuracy Probability 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 RMSD = 0.03 Setpoint = 0.74 RMSD = 0.04 Setpoint = 0.72 Upper limit = 0.8 Seemingly modest differences in accuracy can lead to huge profit differences Assume 5 mtpy clean coal burned $10/ton cost differential 0.6% sulfur 1.8% sulfur 0 8 8.5 9 9.5 10 10.5 11 11.5 12 % sulfur % Ash Annual savings associated with 0.03% RMSD rather than 0.04% = $833,000 16

The analyzer value pyramid Proper selection of set point based upon actual performance Regular lab vs. analyzer calibrations to ensure optimal calibration Do a thorough calibration during commissioning Install it in the best location, with a sampling system located on the same process stream Choose the right analyzer 17

The analyzer value pyramid Proper selection of set point based upon actual performance Regular lab vs. analyzer calibrations to ensure optimal calibration Do a thorough calibration during commissioning Choose the right location to install Choose the right analyzer 18

The analyzer value pyramid Proper selection of set point based upon actual performance Regular lab vs. analyzer calibrations to ensure optimal calibration Calibrate thoroughly during commissioning Choose the right location to install Choose the right analyzer 19

The analyzer value pyramid Proper selection of set point based upon actual performance Check calibration regularly Calibrate thoroughly during commissioning Choose the right location to install Choose the right analyzer 20

The analyzer value pyramid Use Applications Software for added value Check calibration regularly Calibrate thoroughly during commissioning Choose the right location to install Choose the right analyzer 21

Utility Applications Software COBOS Automated Blending from Thermo Scientific Coal yard Quality and Silo Tracking from the Engineering Consulting Group Boiler optimization and profit maximization from Black & Veatch 22

COBOS from Thermo Fisher Up to six sources Up to three control parameters Cost minimization algorithm subject to achieving min s & max s on control variables Batch or continuous, with adjustable recovery rate from deviations Feeder constraints Adjusts for varying delays from feeders Auto adjustment in analyzer assumptions (e.g., MAF Btu) based upon feed proportions 23

ECG s AccuTrack Objectives Fuel Tracking Objectives: Track incoming coal tons and fuel spec from Mine to Yard to Bunker to Burner Forecast Blending to meet Operational and Economic Objectives Track Chemistry to Avoid Boiler Upsets while Maintaining Consistent Emissions Provide Operators with Early Warning Advisory Information 24

AccuTrack Operator Advisory Screen When? What? 25

Black & Veatch: Predicting Performance Based on Coal Quality Holistic view Takes market conditions into account Overall goal is profit maximization Model is dynamic, learning from actual effects of different coal qualities 26

Questions and Comments 27