scholarly journals Neural Network-based Hybrid Estimator for Estimating Concentration in Ethylene Polymerization Process: An Applicable Approach

2020 ◽  
Vol 24 (2) ◽  
pp. 29-39
Author(s):  
Wachira Daosud ◽  
Mohd Azlan Hussain ◽  
Paisan Kittisupakorn
Keyword(s):  
Neural Network ◽  
Ethylene Polymerization ◽  
Polymerization Process ◽  
Hybrid Estimator

Hybrid Model for Biopolymerization Process (ε-Caprolactone to Polycaprolactone)

2014 ◽  
Vol 625 ◽  
pp. 77-80 ◽  
Author(s):  
Senthil Kumar Senthil ◽  
Z. Ahmad
Keyword(s):  
Neural Network ◽  
Molecular Weight ◽  
Molecular Weight Distribution ◽  
Hybrid Model ◽  
Weight Distribution ◽  
Mechanistic Model ◽  
Feedforward Neural Network ◽  
Experimental Results ◽  
Polymerization Process ◽  
Mathematical Equations

A very attractive and accepted approach to the modeling problem is building a hybrid model, where certain amounts of both phenomenological and empirical information are used. In this paper the mechanistic model is created by using the mathematical equations which are represented in the MatlabTM Simulink environment so as to achieve a control over the bio-polymerization process. This mechanistic model was connected to a Feedforward Neural Network (FANN) model to complete the hybrid model of the process to predict the molecular weight distribution. The hybrid model in the Simulink environment was validated by comparing the results of the hybrid model with that of the experimental results carried out in a bioreactor.

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