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The aim is to enhance existing product/process design systems with features that will enable engineers to collaboratively design energy efficient and ecologically optimal discrete manufacturing processes, and generate appropriate extended monitoring and decision making services to support manufacturing installations to ensure optimal ecological impact over the process life cycle.Industrial companies have invested resources to make their products as energy efficient as possible. There is a lack of ICT systems/tools to support product/process design for energy efficiency of installations.A critical issue in energy use optimisation of manufacturing processes is availability of knowledge on actual energy consumption patterns within such processes. Ambient Intelligent (AmI) systems integrated in manufacturing installations may support energy efficiency of processes through extended services and provide such knowledge to optimise process design from energy efficiency point of view.A generic methodology and SOA-based context-sensitive services which can be easily added/integrated in the existing design systems will be developed: Energy Dependency Selector – pre-design stage planning of which equipment to use for meeting both specs and energy efficiency over the process life cycle for Eco-Design by applying TRIZ methodology, Energy Monitoring Setup – design of AmI systems and extended services to ensure energy efficiency of the installed process, Energy Analyser – multi-factor and multivariable optimisation of energy efficiency of process, Energy Simulator – what-if life cycle scenarios with stochastic inputs simulated to quantify the effects of different configurations.3 existing product/process design systems will be enhanced with these generic SW services and such integrated solutions will be tested for collaborative design using data from real manufacturing environments. The target is to reduce energy consumption by at least 15% in the newly designed processes.

More information under: http://www.demi-online.eu/

DEMI has received funding from the European Union’s RP 7 research and innovation programme under grant agreement no. 247831

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