Abstract
Genetic programming (GP) is an orderly method based on natural evolution rules for getting computers to regularly solve a problem. In the present study, GP is presented as a novel approach for modeling the gas sparging assisted microfiltration of oil-in-water emulsion process. The effects of gas flow rate (QG), oil concentration (Coil), transmembrane pressure (TMP), and liquid flow rate (QL) on the permeate flux and oil rejection were studied and the GP models were developed to predict the membrane performance. Coil and TMP showed significant effects on both permeate flux and rejection. An interaction between Coil and TMP was detected, at low Coil and high TMP, in which the permeate flux increased considerably. It was found that QL has a low effect on permeate flux, but its impact on rejection was significant. Increasing QL from 0.5 to 2.75 L/min led to a considerable increment in rejection; however, further increase in the liquid flow rate decreased the oil rejection. On the contrary, QG showed a small effect on oil rejection, but its effect on permeate flux was notable. To determine the optimum conditions, the performance index was maximized using the developed genetic algorithm. Under the obtained optimal conditions, maximum permeate flux and rejection (%) were 121.6 (Lm2/h) and 93.0%, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
