Aramix (3rdPlace Srl), part of the Datrix group, is leading the project CAusal Modeling and Bayesian Optimization for predictive DIAgnostics (CAMBODIA), born from the collaboration with TekRevolution and the University of Messina, with the aim of developing innovative solutions based on Artificial Intelligence to support industrial safety, risk management and the sustainability of production processes.

The solution: AI, data science and multidisciplinary know-how

Within Spoke 6 – FP2 Big Data Analytics & AI and FP3 Computational Fluid Dynamics, CAMBODIA aims to:

• Develop AI solutions that combine causal models and Bayesian optimization algorithms to support decisions related to the predictive diagnostics of complex systems.
• Create causal models integrated with BOED (Bayesian Optimization of Experimental Design) to improve complex CFD models.

As a deep-tech company, Aramix will apply these solutions to support the development of advanced engineering systems, increasing their safety, efficiency and reliability.

Partners and expertise

• Aramix (3rdPlace Srl): leader in the application of Artificial Intelligence to industrial and managerial processes, with consolidated experience in data governance, machine learning and predictive analytics.
• TekRevolution: technology company specializing in high-tech digital solutions. With extensive experience in Computational Fluid Dynamics, it brings fundamental know-how in the development and optimization of components through innovative materials and advanced production processes for industrial applications.
• University of Messina: center of excellence in research, with an expert team in chemical-industrial safety, risk management, catalysis and innovative processes, which enriches the project with scientific knowledge and advanced analytical capabilities.

Main advantages

• Effective predictive maintenance: evaluation of the real impact of different maintenance actions by answering “what if” questions. This allows choosing the optimal intervention to maximize system availability and reduce costs in the manufacturing, aerospace and energy sectors.
• Improved CFD modeling: use of causal analysis of experimental data to refine and simplify complex Computational Fluid Dynamics models. This allows for more accurate and computationally efficient simulations for the design of innovative systems in the energy and aerospace sectors.
• Optimization of renewable energy production: analysis of meteorological data to optimize the integration of wind and solar energy into the power grid. This causal understanding helps reduce dependence on fossil fuels and improve overall grid management.

Want to know more?
Write to info@aramix.ai for more information.
Follow updates on the Aramix LinkedIn page.