Nanoparticle modelling allows concentration simulations in industrial settings to estimate the effect of different air extraction strategies in scenarios with nanoparticle emitting processes. Several artificial intelligence-based techniques can evaluate these strategies to find the optimal one. Moreover, they can simultaneously minimise the energy cost of the extraction process by coordinating the industrial activity with the hourly grid energy cost fluctuation. Consequently, two artificial intelligence algorithms are proposed based on genetic algorithms and reinforcement learning. For the first, a population generator manages system’s restrictions based on real operative scenarios and then individuals change through time imitating natural selection, reproduction and mutation processes. For the second, a meta-heuristics policy is designed from state space and actions consisting on different heuristic strategies to explore potential solutions. Preliminary results evaluating the energy cost performance show that both algorithms reach similar solutions, registering the expected population features curve for the genetic algorithm but not illustrating a clear learning curve for the reinforcement learning study.

This research is part of a LIFE-funded project (LIFE20 ENV/ES/000187). It was also supported by the Spanish Ministry of Science and Innovation (Project CEX2018-000794-S) and by AGAUR (projects 2017 SGR41 and 2021 SGR 00341). Finally, the first author gratefully acknowledges the Universitat Politècnica de Catalunya for the financial support of his predoctoral grant FPU-UPC, with the collaboration of Banco de Santander

28th International Congress on Project Management and Engineering, CIDIP 2024 Jaen 3 July 2024 through 4 July 2024 Code 204780

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