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Recent attempts to produce effective adsorbents for wastewater treatment purposes have led to development of multiphase nanocomposite consisting of a bed, nanoparticle enhancer(s) and one or more polymeric modifiers. However, availability and final price have been always prohibitive factors challenging the wide application of many of multiphase adsorbents. In this research work, a novel bipolymeric/Fe3O4 nanocomposite loaded on coal tailings was synthesized using a two-step procedure and evaluated as a potential low-cost adsorbent for treating synthetic wastewater polluted by heavy metals. Starting material and synthesized quadriphase nanocomposite were characterized using different instrumental analysis techniques. The adsorptive capacity of the nanocomposite was assessed using response surface methodology (RSM) by considering the nanocomposite-to-cadmium ratio (7–233) and initial pH of solution (3–11) as operating factors and cadmium removal as process response. The significance of the effect of operating variables on removal efficiency was assessed and confirmed using analysis of variance (ANOVA) method. The maximum cadmium removal of 99.30% was obtained under optimum conditions including pH of 10 and quadriphase nanocomposite/cadmium ratio of 40 after 2 h of adsorption. Kinetics and multistep adsorption studies showed that an optimum adsorption of 97% could be obtained only after 1.2 min following a first-order single-step process. Isotherm plots revealed that cadmium adsorb as monolayer on the surface of synthesized quadriphase nanocomposite based on Temkin model with the highest correlation coefficient of 97.67% and the least relative error of 16.15%. [ABSTRACT FROM AUTHOR]

The present work aims at optimization and advanced simulation of removal efficiency of dye material from a synthetic wastewater using a locally generated rhamnolipid (RL) biosurfactant. For this purpose, bio-treatment of dye polluted synthetic wastewater was experimentally, kinetically, and statistically investigated by the ion flotation process in the presence of the RL. The removal rate of methylene blue (MB) as the dye material was assessed by the ultraviolet (UV)-visible absorbance measurements. The impact of operating variables including RL concentration (as a dye collector, 5–50 ppm), methyl isobutyl carbinol (MIBC) dosage (as a frother, 10–70 ppm), solution pH (2–12) and aeration rate (1–5 l/min) were assessed through one-way analysis of variance (ANOVA) and Anderson-Darling as the normality analysis strategy. The process was simulated using two artificial neural network (ANN) optimization algorithms, i.e., genetic algorithm (GA) and artificial bee colony (ABC) as a novel approach. The statistical results indicated that the dye removal process was significantly influenced by all operating variables (p value <0.05) while their relative intensity followed the order of aeration rate > solution pH > RL concentration > MIBC dosage. Anderson-Darling approach disclosed that the all factors were perfectly followed the normal trend with A2 less than unity and p-value of greater than 0.05 at 95% confidence level. Main effect plots revealed that except MIBC dosage with nonlinear trend, the rest of factors had an ascending influence on the removal efficiency. The process was optimized by interpreting the interaction effect among various variables to reach the maximum dye bioflotation. The maximum removal of 97 ± 0.13% was achieved at pH 12, airflow rate of 5 l/min, MIBC and rhamnolipid concentrations of 30 and 40 ppm, respectively with a flotation kinetic rate of 0.015 sec−1. Finally, the intelligent simulation results showed that the process could be modelled using an artificial bee colony algorithm of 4-7-1 structure with 99% and 98.8% accuracies in the training and testing steps, respectively. Further, we found that the artificial bee colony algorithm was superior to the genetic algorithm in terms of complexity analysis. [Display omitted] • Dye removal from a synthetic wastewater using rhamnolipidic flotation was studied. • Process optimization was simulated by genetic algorithm and artificial bee colony. • RL and MIBC dosages, pH and aeration rate were assessed by ANOVA and AD analyses. • ABC algorithm with 4-7-1 structure provided 99% accuracy at all simulation steps. • Maximum removal of 97% was achieved at optimal operational conditions. [ABSTRACT FROM AUTHOR]

This work aims at presenting an advanced simulation approach for a novel rhamnolipidic-based bioflotation process to remove chromium from wastewater. For this purpose, the significance of key influential operating variables including initial solution pH (2, 4, 6, 8, 10 and 12), rhamnolipid to chromium ratio (RL:Cr = 0.010, 0.025, 0.050, 0.075 and 0.100), reductant (Fe) to chromium ratio (Fe:Cr of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), and air flowrate (50, 100, 150, 200 and 250 mL/min) were investigated and evaluated using Analysis of Variance (ANOVA) method. The RL as both collector and frother was produced using a pure strain of Pseudomonas aeruginosa MA01 under specific conditions. The bioflotation tests were carried out within a bubbly regimed column cell with the dimensions of 60 × 5.70 × 0.1 cm. Four optimization techniques based on Artificial Neural Network (ANN) including Cuckoo, genetic, firefly and biogeography-based optimization algorithms were applied to 113 experiments to identify the optimum values of studied factors. The ANOVA results revealed that all four variables influence the bioflotation performance through a non-linear trend. Their influences, except for aeration rate, were found statistically significant (p-value < 0.05), and all parameters followed the normal distribution according to Anderson-Darlin (AD) criterion. Maximum chromium removal of about 98% was achieved at pH of 6, rhamnolipid to chromium ratio of 0.05, air flowrate of 150 mL/min, and Fe to Cr ratio of 1.0. Flotation kinetics study indicated that chromium bioflotation follows the first-order kinetic model with a rate of 0.023 sec−1. According to the statistical assessment of the model accuracy, the firefly algorithm (FFA) with a structure of 4-9-1 yielded the highest level of reliability with the mean squared, root mean squared, percentage errors and correlation coefficient values of test-data of 0.0038, 0.0617, 3.08% and 96.92%, respectively. These values were evidences of the consistency of the well-structured ANN method to simulate the process. [ABSTRACT FROM AUTHOR]