Modeling and optimization of the hot compressed water extraction of palm oil using artificial neural network
Hot compressed water extraction (HCWE) is a promising green alternative to the screw press in the palm oil processing. In this study, the steady-state characteristic of the HCWE was modeled by using an artificial neural network (ANN). The overall oil yield and other outputs; β-carotene, α-tocopherol...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Society of Chemical Engineers, Japan
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/71726/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978645366&doi=10.1252%2fjcej.15we251&partnerID=40&md5=7d1c0e1b230a7b89fc174600d304b8a0 |
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| Summary: | Hot compressed water extraction (HCWE) is a promising green alternative to the screw press in the palm oil processing. In this study, the steady-state characteristic of the HCWE was modeled by using an artificial neural network (ANN). The overall oil yield and other outputs; β-carotene, α-tocopherol and α-tocotrienol concentration, were described by the pressure and temperature in the HCWE. The results show that the predicted yield and concentrations agree well with experimental data. These models were used to estimate the optimum conditions of the HCWE process. |
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