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Response surface methodology for predicting the dimethylphenol removal from wastewater via reverse osmosis process

Al-Obaidi, Mudhar A.A.R.
Al-Nedawe, B.
Mohammad, A.
Publication Date
2020-07-27
End of Embargo
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Rights
(c) 2020 de Gruyter. Full-text reproduced in accordance with the publisher's self-archiving policy.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2020-05-19
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
Reverse Osmosis (RO) process can be considered as one of the intensively used pioneering equipment for reusing wastewater of several applications. The recent study presented the development of an accurate model for predicting the dimethylphenol removal from wastewater via RO process. The Response Surface Methodology (RSM) was applied to carry out this challenge based on actual experimental data collected from the literature. The independent variables considered are the inlet pressure (5.83-13.58) atm, inlet temperature (29.5-32) ° C, inlet feed flow rate (2.166-2.583) × 10-4 m3/s, and inlet concentration (0.854-8.049) × 10-3 kmol/m3 and the dimethylphenol removal is considered as the response variable. The analysis of variance showed that the inlet temperature and feed flow rate have a negative influence on dimethylphenol removal from wastewater while the inlet pressure and concentration show a positive influence. In this regard, F-value of 240.38 indicates a considerable contribution of the predicted variables of pressure and concentration against the process dimethylphenol rejection. Also, the predicted R2 value of 0.9772 shows the high accuracy of the model. An overall assessment of simulating the performance of RO process against the operating parameters has been systematically demonstrated using the proposed RSM model.
Version
Accepted manuscript
Citation
Al-Obaidi MAAR, Al-Nedawe B, Mohammad A et al (2020) Response surface methodology for predicting the dimethylphenol removal from wastewater via reverse osmosis process. Chemical Product and Process Modeling. 16(3): 193-203.
Link to publisher’s version
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Type
Article
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Notes