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This work has been undertaken with the main purpose of applying recurrent neural networks to the simulation and control of batch reactors. The present study is divided in two parts. The first part is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The second part is focused on the development of control systems appropriate for batch reactors. Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real-time applications is generally limited. In the second part of this study neural inverse and predicitive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The results show that neural-based controllers improve the performance of the real plant.

Additional information

Authors: GALVÁN I M, Carlos III University of Madrid, Department of Computer Science (ES);ZALDÍVAR J M, JRC Ispra (IT)
Bibliographic Reference: EUR 17306 EN (1997) 68pp., FS, free of charge
Availability: Available from the Public Relations and Publications Unit, JRC Ispra, I-21020 Ispra (IT), Fax: +39-332-785818
Record Number: 199710589 / Last updated on: 1997-05-09
Original language: en
Available languages: en
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