Abstract
This study proposes the use of neural networks, specifically NARX networks, in the modeling of non-linear chemical systems with the use of the control field systems identification methodology. The chemical reactor of the Tennessee Eastman, responsible for the greater non-linearities of the plant, is studied. First, a simple decentralized control scheme is proposed for the stabilization of the plant, an identification experiment is designed, and two sub-models are trained for the level and pressure of the reactor, obtaining satisfactory results.
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Oramas Rodríguez, R.A., González Santos, A.I., García González, L. (2021). Identification of Non-linear Chemical Systems with Neural Networks. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_10
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