Micellar-Enhanced Ultrafiltration to Remove Nickel Ions: A Response Surface Method and Artificial Neural Network Optimization

Water

Weiyun Lin¹  Baiyu Zhang¹  Liang Jing¹ 

[1] Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada;

关键词: nickel removal;    optimization;    micellar-enhanced ultrafiltration (MEUF);    response surface methodology (RSM);    Box–Behnken design (BBD);    artificial neural network (ANN);   

DOI  :  10.3390/w12051269

来源: DOAJ

【 摘 要 】

Nickel ions from aqueous solutions were removed by micellar-enhanced ultrafiltration (MEUF), using the surfactant sodium dodecyl sulfate (SDS) as a chelating agent. Process variables and indicators were modeled and optimized by a response surface methodology (RSM), using the Box–Behnken design (BBD). The generated quadratic models described the relationship between a performance indicator (nickel rejection rate or permeate flux) and process variables (pressure, nickel concentration, SDS concentration, and molecular weight cut-off (MWCO)). The analysis of variance (ANOVA) showed that both models are statistically significant. To remove 1 mM of nickel ions, the optimal condition for maximum nickel removal and flux were: pressure = 30 psi, C_SDS = 10.05 mM, and MWCO = 10 kDa, resulting in a rejection rate of 98.16% and a flux of 119.20 L/h∙m². Experimental verification indicates that the RSM model could adequately describe the performance indicators within the examined ranges of the process variables. An artificial neural network (ANN) modelling followed to predict the MEUF performance and validate the RSM results. The obtained ANN models showed good fitness to the experimental data.

【 授权许可】

Unknown