LEVEL-UP | Protocols and Strategies for extending the useful Life of major capital investments and Large Industrial Equipment
01-10-2019
-30-09-2023
01-10-2019
-30-09-2023
01-01-2020
-31-12-2023
01-09-2022
-31-08-2026
01-10-2020
-30-09-2023
01-12-2019
-30-11-2022
01-01-2020
-31-12-2023
01-01-2019
-31-07-2022
01-10-2017
-30-09-2021
The cloud-based optimisation of lamination oven’s configuration will lead to the following significant impacts: saving in energy consumption will result in saving of 18,000 kWh a year (short-term) and it will reach 27,000 kWh a year (medium-term). These figures can be multiplied by three in the long term because EndeF is going to build two new ovens. EndeF will drop CO2 emissions by 4,500 kg CO2eq and by 6750 kg CO2eq a year (short- and medium-term).
Hydal, as end user, will benefit from optimized process of water quenching by saving energy and scrap material and by having shorter turnover time as well. Expected economic impact is estimated 100 K€ a year on energy savings alone. Expected economic impact is estimated on 500 000 Euros on in turnover increase in first year after the experiment. This value will rise to the 2 million of euro after 5 years from the experiment.
01-10-2017
-31-03-2021
Z-BRE4K will lead to the optimisation of the performance, avoiding waste due to malfunctioning machinery and increased energy consumption due to the presence of failures. the reduction of the electric costs is extimated by 10%.
The avoidance of defective production and overproduction will lead to a better efficiency in the use of materials.
Z-Bre4k will contribute to the optimisation of the manufacturing processesresulting in significantly less waste and scrap. Z-Bre4k will contribute to the reduction of defective production thanks to the optimisation of manufacturing through model-based control and improved accuracy. Moreover, it will allow to avoid overproduction that is to say manufacturing items for which there are no orders thanks to the collection of data that will control the production process producing only what is required and not overproduce.
01-11-2017
-28-02-2021
01-10-2017
-31-03-2021
Production equipment uses a number of process resources to operate such as water, air, lubricants, other. In time maintenance activities wnabled by the SERENA predictive maintenance platfrom and optimising the scheduling of maintenance operations can have a significant impact on the consumption of such resources when the equipment is not in proper working condition.
The prediction of maintenance needs of the production equipment thorugh the predictive analytics and scheduling of the SERENA project is expected to reduce the defective workpieces caused by manufacturing equipments not in proper working condition.
The predictive maintenance solutions of the SERENA project are expected to contribute to the sustainability of the production equipment ot proper functional condition thus reducing the energy consumption that is present when machine's are close to the end of their life or in a need of maintenance.
Indutrial equipment not in proper working condition consumes greater quantities of input and operational sources than normal. The SERENA data-driven condition evaluation and prediction of potential failures will enabled the sustainability of the production machines to proper operational condition, thus contributing to reduced process resources.
01-10-2016
-31-03-2021
01-10-2016
-30-09-2020
01-01-2015
-31-12-2017
01-10-2022
-30-09-2026
By accelerating and upscaling the structuring process, the OPTIMAL project will increase the process efficiency and yield, which will allow for “first time right” fabrication of the required structures, lower consumption of resources, waste reduction, lower CO2 emissions, increase of productivity, and cost reduction.
01-06-2022
-31-05-2025
01-12-2014
-30-11-2018
01-10-2016
-31-03-2021
01-10-2016
-30-09-2019
01-01-2015
-31-12-2018
09-01-2015
-31-08-2018
01-01-2017
-30-06-2020
01-01-2015
-31-12-2017
01-10-2016
-31-10-2019
01-01-2015
-31-12-2017
01-01-2015
-30-06-2018
01-10-2016
-31-12-2019
01-01-2015
-31-12-2017
01-09-2016
-31-08-2019