AmI-MoSES ICT-FP7-224250 Am bient- I ntelligent Interactive Mo nitoring S ystem for E nergy use Optimisation in Manuf

3. AmI-MoSES ICT-FP7-224250 Ambient-Intelligent Interactive Monitoring System for Energy use Optimisation in Manufacturing SMEs

4. Rationale • Increasing Cost and Environmental constraints causing increasing pressure on manufacturing companies to decrease energy consumption • Innovative approach needed • sophisticated • intelligent • real time to maximise energy savings in manufacturing companies instead of almost exhausted standard approaches

5. Basic Assumptions • Energy Efficiency begins with measuring energy consumption and continually revisiting the measurements for identifying current consumption patterns to select improvement actions to decrease energy consumption • i.e.to optimise energy consumption

6. Basic Assumptions Besides measuring energy consumption, it is important to monitor processes and activities leading to (higher) energy consumption

7. Tasks Identify ways to effectively monitor various manufacturing processes and correlate these measurements with measured energy consumption data to find out what could be improved in processes to reduce energy consumption

8. Solution Targets AmI-MoSES system addresses two of the most critical problems in EE management in industrial production technologies - especially in modern highly flexible and human centred discrete manufacturing:

9. Solution Targets • How to efficiently and promptly - on-line - acquire/provide information/knowledge needed for optimisation of EE • How to effectively use such knowledge to support decisions regarding Life-Cycle oriented EE Management and EE services

10. Solution Targets • Revolutionise currently very time and cost intensive ways of acquiring and using such knowledge, for both users and vendors of machines/equipment • Address particularly flexible discrete manufacturing industry where interaction between the processes/production equipment and human operators, causing EE variations is more difficult to monitor compared to e.g. process industry

11. Approach • The AmI-MoSES project applies a novel approach: • Combination of Ambient Intelligence -AmI- and Knowledge Management -KM- technologies for monitoring processes and support/suggest solutions • AmI involves new use of sensors to monitor process performance and behaviour aspects, to collect cost and time effectively a radically higher amount of information/knowledge, even those up to now practically impossible to acquire • This information can be correlated with EE to identify problems and propose solutions

12. System Concept Shop floor Decision Level AmI-MoSES

13. Additional AmI / Process Parameters Values To define the context of the EE monitoring and to support problems diagnosing • Surface temperature in several points → Indicating heat loss from the furnace surface → Cause diagnose: Insulation characteristics deterioration • Combustion efficiency → Optimisation of the combustion air temperature → Chimney gas dosing • Gas consumption in the context of product type - heated mass

14. Application ExampleChain Manufacturing – Preheating Furnace

15. CO2 CO Application Example Heat recovery ON/OFF Hot air inject Tha ~~~ IN Gas Burner Preheating Furnace

16. CO2 CO Chimney gas temperature Tcg GAS flow meter Standard volume m3 Inside Furnace Temperatures Heat recovery ON/OFF Hot air inject Tha ~~~ Tzone1 IN Tzone2 Tzone3 Gas Burner Combustion Efficiency C – Existing Measurements L –Additional Measurements

17. CO2 CO Environment temperature Te Chimney gas temperature Tcg GAS flow meter Standard volume m3 Inside Furnace Temperatures Heat recovery Roof temperature Tr ON/OFF Hot air inject Tha ~~~ IR Camera Tzone1 IN Tzone2 Tzone3 Gas Burner Back door temperature Tbd Bar out temperature Tbo Combustion Efficiency Front door temperature Tfd Side wall(s) temperature(s) Tsw Lateral window temperature Tlw C – Existing Measurements L –Additional Measurements