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Caldera inteligente: optimización de la combustión y reducción de emisiones

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Outline • Aim and scope of the Smart boiler project

• Process of the development phases – air and fuel

• Description of the type of sensors and the integration

• Introduction to LoadCycleTest

• Impact of fuel versus boiler

• Results of successful integration of components as basis for a smart boiler

– Including results showing reduction potential with the intelligent boiler

• Outlook, perspective and benefits from a smart boiler

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Aim and scope of the project • Nationally funded by the Danish EPA

• Cooperation between NBE production and Danish Technological Institute(DTI)

• Research and development project

• Scope – Development of a smart boiler by introducing a measurements techniques in the boiler for

improvement of the combustion, reduce the emissions and provide the user with a boiler that is optimized for use and not for testing.

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Idea of the smart boiler

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Idea of the smart boiler – Information to the end-user

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Description of the development phases 1. Towards actual control of air

– Essential to be able to adapt the amount of air to the amount of fuel added

2. Measurement of added fuel – Essential to know the actual mass of fuel in order to optimize combustion

3. Development and definition of a LoadCycleTest – Development of boilers for use in real life

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Type of sensors and integration in the boiler - Air

• Venturi flow meter

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Type of sensors and integration in the boiler - Fuel

• Acustic technique for measurement of mass

Low load Low load Nominal load Nominal load

0,00

2,00

4,00

6,00

8,00

10,00

12,00

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

kW

TID

Spring

Kenneth_85 Kongsgaden m mike

mtjell togmanden Gennemsnit

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LoadCycleTest - Why is it relevant?

0,00

2,00

4,00

6,00

8,00

10,00

12,00

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

kW

TIME

Summer

Kenneth_85 Kongsgaden m mike

mtjell togmanden Gennemsnit

0,00

2,00

4,00

6,00

8,00

10,00

12,00

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

kW

TID

Fall

Kenneth_85 Kongsgaden m mike

mtjell togmanden Gennemsnit

0,00

2,00

4,00

6,00

8,00

10,00

12,00

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00

kW

TIME

Winter

Kenneth_85 Kongsgaden m mike

mtjell togmanden Gennemsnit

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LoadCycleTest

• Initially 24 h with 4 seasons included

• Next step 24 h testing per season(72h) – Spring/Fall

– Summer

– Winter

• Stress test of system

• Realistic test of system performance – Efficiency

– Emissions

0%

20%

40%

60%

80%

100%

0 2 4 6 8 10 12 14 16 18 20 22 24

Po

wer

ou

tpu

t se

tpo

int

Hours

24h

24h

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Scientific results - Identification of correlation between fuel and emissions

As Ak Bs Bk Cs Ck Ds Dk Es Ek N [mg/kg] 400 400 780 780 480 480 990 990 1180 1180

Calorific value [MJ/kg] 19,18 19,18 18,73 18,73 19,12 19,12 18,37 18,37 19,11 19,11

Ash content [%] 0,32 0,32 0,49 0,49 0,37 0,37 0,3 0,3 0,32 0,32

Prefix s – Skamol in burner

Prefix k – Ceramic in burner

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Impact of fuel versus boiler

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Results of successful integration of components as basis for a smart boiler

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Results of successful integration of components as basis for intelligent Boiler

Orginal boiler Optimized boiler with air

mass measurment Reduction

CO at 10% CO2 [mg/m3] 1313 702 47 %

OGC at 10% CO2 [mg/m3] 53 21 60 %

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• Results of successful integration of components as basis for a smart boiler

• Extra auger installed in burner – More homogeneous amount of pellets to

the burner

– Next step ->mass of fuel as input for the system

• Further reduction potential – By combining air and fuel mass

– Optimization of controller by use of LCT

– Air staging and adaption of air mass in different load operations

• Special focus on NOx and dust reduction

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Outlook, perspective and benefits from a smart boiler

• Improved emissions during real life emissions

• Feedback to the user – Active customer service

– Trouble shooting on the entire system

– Feedback on user-behavior of heating systems

– Service contracting

• Future possibilities – New optimized algorithms can be added directly

– System can be extended to suggest improved overall heating system of the house

• Solar heat/power, heat storage, distribution to the house etc.

– Direct ordering of fuel based on data collected

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