CARTIF PROJECTS
CAPRI
Cognitive Automation Platform for European Process Industry Digital Transformation
Description
CAPRI project (“Cognitive Automation Platform for European Process Industry digital transformation“) is a EU financed project represented by a strong consortium of 12 partners. Overall CAPRI objective is to develop, test and experiment an innovative cognitive automation platform (CAP) to achieve the digital transformation of the European process industry enabled by cognitive solutions that give operation flexibility to existing process industries,better performance using different key indicators (KPI) of their products quality control and intermediate production flows.
CARTIF will be the coordinator due to its extensive experience in application of digital technologies in the industry acquired in different EU projects coordinated (currently coordinating 6 UE projects), participating extensively as a researcher. This project will imply to CARTIF the expansion in their knowledge on modelling and control of asphalt plants and the development of new research lines and the encouragement of the different manufacturing sectors to implement CAPRI outcomes.
Objectives
CAPRI project focuses on the digital transformation of three relevant process industry sectors, like minerals (asphalt mixes production), chemical (pharmaceutical industry) and steel (billets and bars):
- Process Industry Digital Transformation: IoT, on-line evaluation and assessment of complex quality parameters, capture and integration of personal knowledge, interoperability.
- Improved performance and flexibility in process plants: optimal efficiency, holistic optimization, interactive control and self-learning, in real time.
- New generation of plants in the process industry: development of a methodology that defines the steps, the exploitation, the new resources as learning materials, the relationship with other projects.
Actions
- Creation of planning tools for the rediction of the production process, reducing downtime and re-planning of emergencies.
- Develop operating tools for optimal production, material traceability and deviation of defective production, prediction of maintenance operations.
- Development of control approaches, optimization of critical attributes of individual processes and creation of self-adapting processes.
- Integration of cognitive sensors that help the development of other cognitive solutions and the digital transformation of the process industry.
Expected Results
CAPRI main results in three pilot industries:
- Minerals (Asphalt): development of cognitive sensors to know the bitumen content in recycled asphalt (RAP), sensor to calculate the amount of filler present in the process, cognitive control of the dryer predictive maintenance of the baghouse filer. A cognitive asphalt production planning and control solution will also be developed that will make use of the developed cognitive solutions.
- Steel (Billets and bars): use of digital twins and automatic learning tools for the management of the cognitive production of steel, analysis of the product traceability, obtaining information on the solidification process, temperatures, prediction of the accumulation of steel flakes and the thickness of the pieces, identification of process anomalies.
- Chemistry (Tablets): monitoring or prediction of critical quality attributes and implementation of a feedback control strategy of the process in real time. Prediction of test results and of the uniformity of the dosage units, measurement of the distribution of the size of granules, failures of the system or derivations of the quality attributes, in addition, the residual humidity in different sections of the dryer will be predicted.
CAPRI has three common goals for the three industry sectors:
- Reduce resource consumption, carbon footprint.
- Increase energy efficiency.
- Increase product quality.
Partners
H2020
H2020-EU.2.1.5.3
No. 870062
Total Budget: 7,050,276.25 €
Contribution: 6,104,451.75 €
Duration: 01/04/2020 – 30/09/2023
Responsible
Cristina Vega
Division of Industrial and Digital Systems
Networking
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