Machine learning and artificial vision for 0% waste textile production
With the aim of reducing defective textiles to close to 0%, this proposal intends to develop a new system based on artificial intelligence, machine learning, and computer vision that, when installed into circular knitting machines, can detect defects in complex fabrics in real-time during production. The economic and environmental benefits of this proposal are evident given that the textile industry is one of the largest in the world, as well as one of the most polluting.
Integrated Resource Management Platform for Water Distribution System
An integrated monitoring system will be developed to prevent water losses and indirect energy losses in urban water distribution systems and to optimise the energy consumption of the distribution system. The water leakage prevention system will offer hardware and software solutions to be developed for the detection of technical losses in the water distribution network and an end-to-end monitoring system. Resource2Tap will develop a product with high commercialisation potential that will prevent technical losses with an IoT-based endpoint monitoring system and conventional neural network-based data analysis software.
Saw Machine that Can Make Smart and Sustainable Production with Prediction Algorithms
Beka-Mak Makina Sanayi ve Ticaret A.Ş. (Turkey)
Sawing machines, used to bring raw material to the desired dimensions in industrial production companies, are of great importance since they are at the beginning of the production line and have a great effect on production efficiency. In this framework, the aim is to manufacture sawing machines with smart and sustainable production techniques which automatically optimise the cutting parameters (cutting speed, surface quality, etc.) with the data to be collected from the field and provide error and lifecycle estimation for machine equipment.
Monitoring Greenhouse Gases with Long-Range Unmanned Aerial Vehicles and Novel Spectroscopic Sensors
Romaeris Corporation (Canada)
The project will use novel, long-range, large payload Unmanned Aerial Vehicles (UAVs) to carry innovative spectroscopic sensors to monitor multiple greenhouse gases (GHGs) over large geographic areas, locate emissions sources, take action and vastly improve our understanding of GHG emissions. A data portal will be created to make such GHG information available to governments and industry worldwide and the data will be made compatible with other sources of information, such as satellites, so that comprehensive and accurate GHG reporting is possible at last.