INVESTIGADORES
CHIOTTI Omar Juan Alfredo
artículos
Título:
Industrial SBR process. Computer simulation study for on-line estimation of steady-state variables using Neural Networks
Autor/es:
ROQUE MINARI,; GEORGINA STEGMAYER; LUIS GUGLIOTTA,; CHIOTTI, OMAR; JORGE VEGA,
Revista:
MACROMOLECULAR REACTION ENGINEERING
Editorial:
John Wiley & Sons
Referencias:
Año: 2007 vol. 1 p. 405 - 412
ISSN:
1862-832X
Resumen:
This work investigates the industrial production of styrene-butadiene rubber in a continuous
reactor train, and proposes a soft sensor for online monitoring of several processes and polymer
quality variables in each reactor. The soft sensor includes two independent artificial neural
networks (ANN). The firstANNestimatesmonomer conversion, solid content, polymer production,
average particle diameter, and average copolymer composition; the second ANN estimates
average molecular weights and average branching degrees. The required ANN inputs are:
(i) the reagent feed rates into the first reactor
and (ii) the reaction heat rate in each
reactor. The proposed ANN-based soft sensor
proved robust to several measurement errors,
and is suitable for online estimation and closedloop
control strategies.