Wong, Yong Jie and Arumugasamy, Senthil Kumar and Jewaratnam, Jegalakshimi (2018) Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization. Clean Technologies and Environmental Policy, 20 (9). pp. 1971-1986. ISSN 1618-954X, DOI https://doi.org/10.1007/s10098-018-1577-4.
Full text not available from this repository.Abstract
This paper reports the biopolymerization of ε-caprolactone, using lipase Novozyme 435 catalyst at varied impeller speeds and reactor temperatures. A multilayer feedforward neural network (FFNN) model with 11 different training algorithms is developed for the multivariable nonlinear biopolymerization of polycaprolactone (PCL). In previous works, biopolymerization carried out in scaled-up bioreactors is modeled through FFNN. No review discussed the role of different training algorithms in artificial neural network on the estimation of biopolymerization performance. This paper compares mean absolute error, mean square error, and mean absolute percentage error (MAPE) in the PCL biopolymerization process for 11 different training algorithms that belong to six classes, namely (1) additive momentum, (2) self-adaptive learning rate, (3) resilient backpropagation, (4) conjugate gradient backpropagation, (5) quasi-Newton, and (6) Bayesian regulation propagation. This paper aims to identify the most effective training method for biopolymerization. Results show that the quasi-Newton-based and Levenberg–Marquardt algorithms have the best performance with MAPE values of 4.512, 5.31, and 3.21% for the number of average molecular weight, weight average molecular weight, and polydispersity index, respectively.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya: RU Geran-Faculti Program Grant RF008A-2018 |
| Additional Information: | Wong, Yong Jie. Bachelor of Chemical Engineering. Department of Chemical Engineering, University of Malaya, Jalan Universiti, 50603 Kuala Lumpur, Wilayah Persekutuan, Malaysia |
| Uncontrolled Keywords: | Artificial neural network modeling; Biopolymerization; Bioreactor; Polycaprolactone (PCL); Ring opening polymerization |
| Subjects: | T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Feb 2019 03:02 |
| Last Modified: | 18 Feb 2019 03:02 |
| URI: | http://eprints.um.edu.my/id/eprint/20353 |
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