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AmI-MoSES ICT-FP7-224250 Am bient- I ntelligent Interactive Mo nitoring S ystem for E nergy use Optimisation in Manuf PowerPoint Presentation
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AmI-MoSES ICT-FP7-224250 Am bient- I ntelligent Interactive Mo nitoring S ystem for E nergy use Optimisation in Manufacturing S MEs. Rationale.

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Presentation Transcript
slide3

AmI-MoSES

ICT-FP7-224250

Ambient-Intelligent Interactive Monitoring System for

Energy use Optimisation in Manufacturing SMEs

rationale
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

basic assumptions
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
basic assumptions1
Basic Assumptions

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

tasks
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

solution targets
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:

solution targets1
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
solution targets2
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
approach
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
system concept
System Concept

Shop floor

Decision Level

AmI-MoSES

additional ami process parameters values
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
slide15

CO2

CO

Application Example

Heat recovery

ON/OFF

Hot air inject Tha

~~~

IN

Gas Burner

Preheating Furnace

slide16

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

slide17

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