self-X Artificial Intelligence for Asphalt Use Case

self-X Artificial Intelligence for Asphalt Use Case
Summary

EIFFAGE asphalt use case, based in the La Atalaya asphalt mix production plant, focuses on circularity of the value chain (from quarry to road) with respect of the quality control of feedstock (aggregates, bitumen, recycled asphalt, ...) the overall sustainable performance of the process (including asphalt paving) and the quality of final product (asphalt mix). Application of toolset to Asphalt: AI application for integrating value chain information from quality laboratories, production and paving logistics and improve overall sustainable performance of the process by including human feedback for asphalt mix design adaptation, maintenance actions and quality monitoring.

The objective is to create self-X AI applications for the whole value chain to gather and infer information from all steps of Asphalt manufacturing with data from: (1) Production, (2) Laboratory, (3) Paving and logistics (asphalt transport to the job site) and (4) Raw materials (aggregates, bitumen, Reclaimed Asphalt Pavement or RAP and additives). To improve the efficiency of the whole value chain, AI apps will be used in predictive diagnosis of equipment efficiency to infer energy savings in production plant and maintenance optimization.

More information & hyperlinks
Web resources: https://s-x-aipi-project.eu/use-cases - s-X-AIPI Asphalt Use Case
Country: ES
Address: SE-445 road, Sevilla 41710
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Demonstrator (project outcome type)
Industrial pilot or use case
MiE KPI section - Impacts
Demonstrator showcasing the realisation of new resilient value chains
Demonstrator showcasing the realisation of new innovative circular value chains
Demonstrator showcasing human and technology complementarity
Demonstrator showcasing digital platforms and engineering tools supporting creativity and productivity of R&D processes
MiE KPI section - Outcomes
Demonstrator showcasing an increased uptake of green manufacturing
Comment:

To improve the efficiency of the whole value chain, AI apps will be used in predictive diagnosis of equipment efficiency to infer energy savings in production plant and maintenance optimization.

Demonstrator showcasing the uptake of de-manufacturing, re-manufacturing and recycling technologies for more efficient manufacturing
Demonstrator showcasing virtual end-to-end life-cycle engineering and manufacturing
Demonstrator targeting supply chain innovations
Demonstrator showcasing reduction of supply chain response-time
Demonstrator showcasing artificial intelligence (AI) and data analytics tools’ uptake
C MANUFACTURING
C19 Manufacture of coke and refined petroleum products
C19.2 Manufacture of refined petroleum products and fossil fuel products
C19.20 Manufacture of refined petroleum products and fossil fuel products
Comment:

Asphalt manufacturing

F CONSTRUCTION
F42 Civil engineering
F42.1 Construction of roads and railways
F42.11 Construction of roads and motorways
Comment:

Asphalt paving of roads