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Polymers 2018, 10(2), 132; https://doi.org/10.3390/polym10020132

Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates

1
Bioseparation Research Group, Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
2
Biorefinery and Bioprocess Engineering Laboratory, Department of Chemical Engineering and Materials Science, Yuan Ze University, No. 135 Yuan-Tung Road, Chung-Li, Tao-Yuan 32003, Taiwan
3
Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
4
School of Biosciences, Faculty of Science, University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
5
School of Chemical Engineering, University Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia
*
Authors to whom correspondence should be addressed.
Received: 28 November 2017 / Revised: 25 January 2018 / Accepted: 26 January 2018 / Published: 30 January 2018
(This article belongs to the Special Issue Polymers for Bioseparations)
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Abstract

At present, polyhydroxyalkanoates (PHAs) have been considered as a promising alternative to conventional plastics due to their diverse variability in structure and rapid biodegradation. To ensure cost competitiveness in the market, thermoseparating aqueous two-phase extraction (ATPE) with the advantages of being mild and environmental-friendly was suggested as the primary isolation and purification tool for PHAs. Utilizing two-level full factorial design, this work studied the influence and interaction between four independent variables on the partitioning behavior of PHAs. Based on the experimental results, feed forward neural network (FFNN) was used to develop an empirical model of PHAs based on the ATPE thermoseparating input-output parameter. In this case, bootstrap resampling technique was used to generate more data. At the conditions of 15 wt % phosphate salt, 18 wt % ethylene oxide–propylene oxide (EOPO), and pH 10 without the addition of NaCl, the purification and recovery of PHAs achieved a highest yield of 93.9%. Overall, the statistical analysis demonstrated that the phosphate concentration and thermoseparating polymer concentration were the most significant parameters due to their individual influence and synergistic interaction between them on all the response variables. The final results of the FFNN model showed the ability of the model to seamlessly generalize the relationship between the input–output of the process. View Full-Text
Keywords: aqueous two-phase extraction; bioseparations; design of experiment; purification; polyhydroxyalkanoates aqueous two-phase extraction; bioseparations; design of experiment; purification; polyhydroxyalkanoates
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Leong, Y.K.; Chang, C.-K.; Arumugasamy, S.K.; Lan, J.C.-W.; Loh, H.-S.; Muhammad, D.; Show, P.L. Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates. Polymers 2018, 10, 132.

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