In copper mining, after the mineral extraction and processing through flotation, copper concentrates and tailings (mining waste) are produced. On one hand, tailings are thickened to recover a fraction of its water content and then carried to the storage site, a tailing dam, through a transportation system formed by pipes and/or channels. On the other hand, the copper concentrate is carried from the processing plants to the harbour through a piping system. These ducts are generally very long (over 30 km) and run mostly underground.

There have been occasional damages in this type of ducts, causing leakages with a high environmental impact. Knowing the condition of the ducts in real time would enable the anticipation of faults and the implementation of the necessary prevention actions to stop future damage.

ACCIONA is looking for startups that help us with the management and maintenance optimization of the piping system for both tailings and copper concentrates. How can both types of ducts be monitored in real time? How can incidents in the system be detected in real time to activate an action plan? How can the maintenance plans for the pipes be optimized? How could the monitoring solution be used to improve future piping systems?

Currently, the monitoring of resources (material and human) in a construction project is done manually with the combination of physical measurements, visual inspections and the creation of reports, whether on paper or digital. However, the monitoring of these resources should be done as autonomously as possible, with minimum participation on the part of the construction team. This would come into play in two areas: safety and construction supervision.

At ACCIONA, we’re looking for start-ups that can help us to improve the management and supervision of civil engineering works. How can we roll out an autonomous system for identifying the resources for a construction project? How can we better monitor the locations of the machinery? How can we improve the control of access to restricted zones? How could we automatically identify the tasks that workers are engaged in? How can we monitor different teams to check that the plan is being followed, evaluate performance and identify any divergences?

The use of laser scanning tools to obtain 3D models from point clouds is increasing fast in the construction and civil engineering sectors. However, the processing of the information obtained is still slow, manual and incomplete despite new developments in the market. This means that the point cloud information is just a mere visual record with little parametric and analysis value.

ACCIONA is looking for start-ups that will propose a post-processing workflow/system for 3D capture information. How do we move from raw data to smart data, eliminating repetitive tasks with no added value derived from processing tasks such as cleaning, adjustment, asset segmentation, etc.? How can we obtain reference geometries through point clouds that allow the creation of 3D and BIM models?