TY - GEN
T1 - Validation of Nonlinear PID Controllers on a Lab-Scale Batch Reactor
AU - Shettigar J, Prajwal
AU - Pai, Ankitha
AU - Joshi, Yuvanshu
AU - Indiran, Thirunavukkarasu
AU - Chokkadi, Shreesha
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - With wide acceptance of batch processes for polymer production, this study aims to model the temperature dynamics of a batch polymerization reactor using Hammerstein and neural network approaches. And to design nonlinear PID controllers in combination to the models to control the temperature of exothermic reactions happening inside the reactor. A temperature trajectory is used as reference signal which is designed based on reactor safety constraints for the reaction of choice Acrylamide Polymerization. The Hammerstein Model based nonlinear PID (HNPID) makes use of polynomial structures to approximated the inverse of the model, so that a higher order model can be used that provides better accuracy. On the other hand, the neural network based PID (NNPID) uses an optimization approach to tune the PID gains. With Batch Reactor as system of interest, both the controllers are validated in simulation to account for energy consumption by each of them. It is noted HNPID consumes less power than NNPID with better tracking hence a perfect candidate for polymer production in realtime.
AB - With wide acceptance of batch processes for polymer production, this study aims to model the temperature dynamics of a batch polymerization reactor using Hammerstein and neural network approaches. And to design nonlinear PID controllers in combination to the models to control the temperature of exothermic reactions happening inside the reactor. A temperature trajectory is used as reference signal which is designed based on reactor safety constraints for the reaction of choice Acrylamide Polymerization. The Hammerstein Model based nonlinear PID (HNPID) makes use of polynomial structures to approximated the inverse of the model, so that a higher order model can be used that provides better accuracy. On the other hand, the neural network based PID (NNPID) uses an optimization approach to tune the PID gains. With Batch Reactor as system of interest, both the controllers are validated in simulation to account for energy consumption by each of them. It is noted HNPID consumes less power than NNPID with better tracking hence a perfect candidate for polymer production in realtime.
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U2 - 10.1007/978-3-031-28180-8_4
DO - 10.1007/978-3-031-28180-8_4
M3 - Conference contribution
AN - SCOPUS:85151150301
SN - 9783031281792
T3 - Communications in Computer and Information Science
SP - 47
EP - 59
BT - Advanced Network Technologies and Intelligent Computing - 2nd International Conference, ANTIC 2022, Proceedings
A2 - Woungang, Isaac
A2 - Dhurandher, Sanjay Kumar
A2 - Pattanaik, Kiran Kumar
A2 - Verma, Anshul
A2 - Verma, Pradeepika
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2022
Y2 - 22 December 2022 through 24 December 2022
ER -
