Category Archives: research

REAMIT

Improving Resource Efficiency of Agribusiness supply chains by Minimising waste using Big Data and Internet of Things sensors

Project details

  • Programme: INTERREG North-West Europe
  • Duration of the Project: 42 months
  • Total budget: € 4.88 m
  • EU funding: € 2.93 m
  • Lead Partner: University of Bedfordshire (UK)
  • Partners: 
    • Images & RĂ©seaux (France)
    • National University of Ireland
    • University College Dublin (Ireland)
    • University of Nantes (France)
    • Levstone Ltd (UK)
    • Nottingham Trent University (UK)
    • Whysor (Netherlands)
    • Institute of Technology in Tralee (Ireland)
    • SenX (France)
    • Ulster University (UK)
    • Dunbia (Northern Ireland)

Aims and Objectives

The REAMIT project proposes to adapt and apply existing innovative technology to food supply chains in NWE to reduce food waste and hence improve resource efficiency. The EU has committed to halving food waste by 2030 by focusing on all stages in the supply chain. 35% of food waste in EU-28 has occurred in supply chains in 2012. Though technologies exist to reduce food waste, they have not been applied to food supply chains.

Specific objectives:

REAMIT will focus on fruits, vegetables, meat and fish as these are wasted in large quantities. The supply chain includes farms, packaging sites, food processors, distribution, logistics, wholesalers and retailers. The project will be carried out in Ireland, Germany, France, UK and the Netherlands due to the amount of interconnected food supply chains and huge food waste in these countries. The project will adapt existing Internet of Things and Big Data technologies to best fit the needs of the food supply chain management system in NWE. Through testing and adaptation, these technologies will be enabled to continuously monitor and record food quality and signal potential food quality issues. Through analytics, owners of ‘food to be at risk of becoming waste’ will be provided with decision support options to minimise food waste including redistribution to nearby customers. REAMIT project will save 1.8Mt of food waste or €3B per year in NWE, avoid 5.5Mt/yr of CO2 emissions, test and operationalise 8 solutions, and, support 20 enterprises. The technologies will be self-sustaining at the end of the project. They will be made available to the public via REAMIT website and social media.

The long-term effects of REAMIT will be optimising (re)use of food and natural resources in NWE economies and a consortium capable of jointly addressing the challenges in food sector.

The first newsletter is now available.

The AIM 2016 Conference in Canada

Niall O’ Mahony attended the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) in Banff, Alberta, Canada. The AIM 2016 conference brings together an international community of experts to discuss the state-of-the-art, new research results, perspectives of future developments, and innovative applications relevant to mechatronics, robotics, control, automation, and related areas. The Content presented was Smart Sensors for Process Analytical Technology. The paper presented reviewed the advantages of smart sensors and their role in recent trends in intelligent manufacturing such as the Industrial Internet of Things (IIOT) and Industry 4.0. The paper also presented the selection process for a number of smart sensors for a number of industrial applications dealt with in collaborative work with the ProPAT project. These applications included the Mass flowrate, Moisture content and Temperature of powders in milling and pharmaceutical industries and the Moisture content, Viscosity and Colour of polymer resin in a polymerisation process. This paper has been published in IEEEXplore Conference Proceedings for 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

Intelligent Profiling of Blood Donors in Ireland

Joanna Kossakowska one of the researcher at IMaR is currently researching methods for improving efficiency of blood collection in Ireland. The main goal of the study is to examine currently available techniques for intelligent profiling of blood donors. This involves examination and identification of most suitable data mining techniques and machine learning algorithms which will be applied on individuals’ historical data in order to predict their future blood donation patterns. This research can be run thanks to the support and collaboration from Irish Blood Transfusion Services. However, further help from volunteers is needed, as a special survey has been developed to provide data for the research. If you would like to know more about the project and participate in the study by filling out the survey please visit: http://blood-donors-research.imar.ie/