Use Cases
Circular TwAIn promotes circularity and end-to-end sustainability through trusted AI technologies in three industrial use cases both in the discrete manufacturing and the process industry.
De- and Re-manufacturing of Li-Ion battery packs in e- mobility
The strategic importance of this demonstrator lies in the automotive LIBs end-of-life treatment by performing the remanufacturing and the re-use of the disassembled cells with proper residual characteristics into second-life stationary applications. The battery cells which are not compatible with second-life applications, are recycled with the aim of recovering high-value materials and allowing those materials’ re-use as secondary raw materials (e.g., Li, Co, Ni, Mn, Fe, C, and other metals).
The mission of this use case lies in five operational goals
Computer-vision driven collaborative robotics for the disassembly of LIB packs
Machine learning aided automated disassembly of LIB modules
AI tool for the characterization of the LIBs state -of-health combining historical and testing data
AI tool for optimised mechanical recycling of degraded LIBs
Market oriented holistic decision-support-system for the LIBs de - and remanufacturing
De- and Re- manufacturing of consumer WEEE
This demonstrator aims at overcoming limitations in the de-manufacturing and re-manufacturing of consumer WEEE and in particular IT and telecommunications equipment which makes more than 10% of the total WEEE in Spain. These elements, which are commonly discarded because of devaluation, can be easily reintroduced to the market if they are still functional or recycled into valuable materials if not. Circular TwAIn framework and AI modules will help overcome limitations in the de-manufacturing processes, by enabling the automation of the disassembly of IT products and assisting in the decision making to optimise the value and impact of the reused products.
The demonstrator is developed through five use cases
Computer-vision driven product identification for the disassembly of IT equipment
Characterisation and assessment of components and subcomponents
Real time planning of the disassembling operations
Collaborative robotics for the support of manual operations
Market oriented holistic decision-support-system for WEEE de- and re-manufacturing
Petrol-Chemical Pilot: Hybrid Circular Twins for Process Industry
This pilot focuses on reducing the CO2 emissions and energy consumption that derive from the production of Ethylene Oxide and Ethylene Glycol by applying artificial intelligence and machine learning algorithms to enable the optimum use of data in the production facilities. EO/EG plant is a production facility with high energy consumption. With the energy optimisation to be made in this equipment, it is possible to reduce the steam consumption and, accordingly, to reduce the CO2 emission and the unit production cost.
The demonstrator is developed through five use cases
Data acquisition and representation for AI framework
Developing hybrid circular twin of the process
Use of data analytics, AI and model verification to understand process unit failures
Developing AutoML module for Process Industry
Generation of a tool for process optimisation
