EKF-NN based Hybrid Estimator for Ethylene Polymerization Process

Daosud, Wachira; Hussain, Mohd Azlan; Kittisupakorn, Paisan

doi:10.1016/b978-0-444-63965-3.50245-2

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2020

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“…In a recent study, Daosud et al [ 15 ] developed a neural network‐based hybrid estimator for a multivariable nonlinear ethylene polymerization process. The hybrid system contains a feedforward neural network and an extended Kalman filter (EKF), the network having the role to predict the estimated error of monomer concentration for reducing the error of EKF estimation.…”

Section: Introductionmentioning

confidence: 99%

Neural network‐based hybrid models developed for free radical polymerization of styrene

Ghiba

Drăgoi

Curteanu

2020

Polymer Engineering & Sci

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In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.

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Section: Introductionmentioning

confidence: 99%