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Heterogeneous systems - hardware software co-design

Industrial AI Systems

Information Design

Model-Based Engineering of Embedded Systems

Neuroengineering

Production Development

Real-Time Systems Design

Robotics

Sustainable lifestyle and health from a public health perspective

Artificial Intelligence och Intelligent Systems

Biomedical Engineering

Care, Recovery and Health

Digital and Circular Industrial Services

New Replacement Policy Considering Environment Sustainability

The project aims to develop an ICT-enabled, data-driven, decision-support system that implements a practical economic replacement time model based on high-quality, real cost data and environmental parameters.

Start

2023-10-01

Planned completion

2026-09-30

Main financing

Vinnova ,

Collaboration partners

Predge AB , Trafikverket , Damill AB ,

Research group

Embedded Systems

Project manager at MDU

No partial template found

NRPCES will develop an ICT-enabled, data-driven decision-support tool to enable the industry to consider sustainability factors to optimize lifetime of their production machineries. In addition, it aims to develop a real time trend monitoring of carbon footprint of a production plant, considering the whole supply chain and its associated cost-CO2e conversion. This will facilitate the transition from linear to circular economy, and a more effective consumption of the resources, to help SMEs and large enterprises adopting new regulatory requirements to reduce carbon-footprint.

The overall NRPCES project objectives are:

  1. Development of ICT-enabled, data-driven, decision-support system that implements a practical economic replacement time model based on high-quality, real cost data and environmental parameters.Live monitoring of carbon foot-print emission of production machineries to assess sustainability KPIs and fulfilling regulatory requirements.
  2. Support the long-term transformation from linear to circular economy and more sustainable use of resources.

Project objective

Development of ICT-enabled, data-driven, decision-support system that implements a practical economic replacement time model based on high-quality, real cost data and environmental parameters.