Effects of process conditions in submerged ultrafiltration for refinery wastewater treatment: Optimization of operating process by response surface methodology

The Effect of Nanosilica Extracted from Sugarcane Bagasse on Formulation of Flat Sheet Nanofiltration Membrane

“…The width of the nanofiltration membrane for dope S3 is 101µm. The result was similar to the previous work by [9,10] showed the membranes consist of a dense skin layer at top surface and short finger-like structure. This relates to high rejection due to the effective rejection that flow through dense skin layer and finger-like structure explaining the high water flux obtained.…”

“…According to the results, the 3% SCBN show the highest NaCl rejection of 75.35% due to the optimal mixture of SCBN that result in good hydrophilic properties. The high hydrophilicity membrane may reduce interaction between hydrophobic contaminants and the membrane surface; hence, the rejection properties were improved effectively [9]. This is clearly show by the rejection properties when SCBN is added as additive in nanofiltration membrane.…”

“…After addition of 4% to 6% SCBN, the PWF were slightly similar with average flux of 42.54 L/m 2 hr. Previous study reported by [9] shows that 2% of silica give the best pure water flux of 25.30 L/m 2 hr. This is due to the interaction between contaminants and membrane surface were reduced by silica particles of the membrane surface [9].…”

“…Previous study reported by [9] shows that 2% of silica give the best pure water flux of 25.30 L/m 2 hr. This is due to the interaction between contaminants and membrane surface were reduced by silica particles of the membrane surface [9]. In this study, the optimum flux rate can be considered at membrane which contains 3% of SCBN which indicate that hydrophilicity of the membrane increases and consequently it attract water molecules into the composite membrane.…”

Biodegradation of BTEX: Optimization through Response Surface Methodology

“…Thus, through the use Response Surface Method (RSM), one can optimize all the major parameters and eliminate the limitations of oneparameter optimization (Choudhury and El-Baradie, 1999). RSM has been used in various fields such as machining techniques (Choudhury and El-Baradie, 1999), chemical reactions (Sultania et al, 2011;Setiabudi et al, 2013) enzyme and catalytic reactions (Hosseinpour et al, 2011;Thouchprasitchai et al, 2011;Yüksel and Yeşilçubuk, 2012) and with recent broader application extended to the optimization of water and water treatment processes (Güven et al, 2008;Körbahti and Tanyolaç, 2008;Trinh and Kang, 2011;Kumar and Pal, 2012;Yuliwati et al, 2012), in biochemistry (Wang et al, 2012) toxicology studies (Carter et al, 1985;Kiran and Thanasekaran, 2011;Jeong et al, 2012) and in biotechnology for studying biodegradation of phenol (Sheeja and Murugesan, 2002). However, a limited number of studies (Jo et al, 2008;Jin et al, 2013) were reported on the optimization of BTEX's removal efficiency.…”