Digital Twin to transform waste management
The waste management and treatment sector is undergoing a natural evolution in which innovation and new technologies are playing a fundamental role.
The recovery of materials present in the waste that is carried out in the waste selection and treatment plants is fundamental to consolidate the model of Circular Economy and it is necessary that the processes are transformed, either to maximize the recovery of materials, as well as to guarantee the quality of the recovered materials.
The change of paradigm in the industry makes it essential that we overcome the traditional barriers around the management and recovery of materials and move fully into the 4.0 industry, which will allow us to transform the treatment plants into guarantors of an effective circular economy, as well as to improve and optimize their operational and maintenance processes.
The Digital Twin is a unique opportunity to reinvent waste management and treatment.
The Digital Twin is a faithful representation of a physical asset, at the level of data and geometry, which incorporates the data of the real behaviour of the asset from the information of sensors and different operation and maintenance databases, capable of integrating and digitizing the whole value chain.
This perfect digital copy allows simulations, tests and optimizations in a completely virtual environment and, therefore, allows a more efficient management of these assets.
To this end, our process of creating the Digital Twin of the asset and its processes consists of the following elements:
- We model the asset, from the building, the facilities and the equipment.
- We link the information from the existing operation, maintenance and security tools.
- We incorporate the signals of the operations directly from the existing sensorization in-situ, or we install new sensors (IOT).
- We integrate all this into a DTwin platform located in the cloud, with collaborative web access, as an information manager and we build the dashboards and alerts that facilitate the interpretation of this data.
Some of the benefits we obtain with the construction of the Digital Twin for the management and treatment of waste are the following:
- Interoperability of data with its geometry (georeferencing all data)
- Digitization of all processes and therefore integrity of all asset information, from design and construction to operation.
- Having real data with which to create dashboards and alerts for decision making, as well as implementing AI algorithms for simulation and prediction.
- To have asset models in virtual reality and augmented reality, either for dissemination or for training in operation and security.
- Reducing operating costs and therefore improving Ebitda.
- Monitoring of processes in real time and remote access for better identification and resolution of incidents.
Real case: The Metgrow+ project for the development of new technologies in the valorization of low-grade metals.
The European Metgrow+ project is part of the Horizon 2020 research and development programme, funded by the European Union, and aims to develop new innovative metallurgical tools and technologies that enable flexible, cost-effective and sustainable designs in the valorization of low-grade metals from both primary and secondary sources.
For this purpose, we have created the Digital Twin supported by the BIM (Building Information Modeling) methodology that allows us to study the CAPEX (capital expenditure) and OPEX (operating expenditure) of material treatment processes to determine their viability and how to optimize them.
This allows us to carry out simulations and study different scenarios to analyse the viability of the different techniques for recovering the final residual matrix in building materials, whether to produce cement or inorganic polymers to be used as thermal insulation.
Digitizing and sensorizing the assets, automating the processes and analyzing the data obtained in real time are the essential pillars for adapting the plants to the needs and challenges posed by this new context.