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Example 2: Online ‘Determination’ of the actual fired coal blending; Modelling coal-ID

Example 2: Online ‘Determination’ of the actual fired coal blending; Modelling coal-ID. During a test program 10 different coal blendings (not types) ‘coal-ID’s’ were fired:. - Ash content approx. 14 - 21 % - Water content approx. 9 - 12 % - Sulphur content approx. 0,85 - 1 %

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Example 2: Online ‘Determination’ of the actual fired coal blending; Modelling coal-ID

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  1. Example 2:Online ‘Determination’ of the actual fired coal blending; Modelling coal-ID During a test program 10 different coal blendings (not types) ‘coal-ID’s’ were fired: - Ash content approx. 14 - 21 % - Water content approx. 9 - 12 % - Sulphur content approx. 0,85 - 1 % - Carbon content approx. 58 - 64 % - Volatiles ca. 33 - 39 % - calorifc value ca. 23,5 - 25,5 MJ/kg ‚Coal-ID‘

  2. Example 2:Modelling of coal-ID The used coal blending -‘coal-ID’- can be exactly predicted with the image processing and the crude gas O2 and CO! ‚coal-ID‘ Image Processing & Prediction ‚coal-ID‘ Laboratory

  3. air dampers at every burner • total air flow • F primary air • F secondary air • flue gas recirculation • S feeder Boiler- model • Plant Heat Rate • Boiler End Temperature • NOx concentration • CO concentration • O2 concentration • Carbon in ash Advantage of the image processing: Spatial and temporal changes of • intensity and temperature, • streaming, • mixing, etc. With correlations from: • coal quality • mill wear • combustion air • slagging etc. Boiler model with flame characteristics Manipulated Variables Optimisation targets e.g. Add. Process Variables

  4. PiT Navigator: customer benefits Increase of efficiency by optimized fuel / air ratio and equalized combustion control: • reduced fuel consumption • reduced emissions (CO and NOx) • reduced slagging & fouling Increased flexibility regarding combustion of different coal qualities and alternative combustion Smoothened plant operation / condition-based maintenance • increased lifetime of the plant • longer service intervals, reduced service costs • increase of plant availability Finally, the PiT Navigator System results in a considerable reduction of energy production costs

  5. PiT Navigator in 4 coal fired boilers 4 boilers are operated with the PiT Navigator System and reach as follows: Enlaged efficiency from: • reduced fuel costs • reduced excess air • reduced CIA (carbon in ash) RAG Saarenergie, Power Plant Fenne Völklingen, Germany (1 Unit) 230 MW Vattenfall, HEW Tiefstack Power Plant KEPCO, Power Plant Seocheon Hamburg, Germany (1 Unit) 252 MW Seocheon, South Korea (2 Units) 215 MW

  6. PiT Navigatorat MKV Fenne plant, Germany Fenne Power Plant, Völklingen, ‚MKV‘ boiler Operated by Saarenergie, STEAG group 230 MW unit, local region hard coal, peak-load Fenne Power Plant, is a two wall firing boiler with 8 burners (2 levels). PiT Navigator controls the combustion air distribution per burner and the amount of sewage sludge feed since May 2005. Targets: - Lambda: n 1,28 -> 1,20 - reduced exit gas temperature (heating loss) • Performance Contracting between Powitec & Saarenergie! • Benefit sharing over the next seven years! • Powitec operates the PiT Navigator system (installation, maintenance…) PiT Multisensor air coal

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