Industrial SBR Process: Computer Simulation Study for Online Estimation of Steady‐State Variables Using Neural Networks

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 first ANN estimates monomer 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 closed-loop control strategies.

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