Adsorptive removal of acid red 18 dye from aqueous solution using hexadecyl-trimethyl ammonium chloride modified nano-pumice.

Discharging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles.

Modeling, optimization and efficient use of MMT K10 nanoclay for Pb (II) removal using RSM, ANN and GA.

Regarding the long-term toxic effects of Pb (II) ions on human health and its bioaccumulation property, taking measures for its reduction in the environment is necessary. The MMT-K10 (montmorillonite-k10) nanoclay was characterized by XRD, XRF, BET, FESEM, and FTIR. The effects of pH, initial concentrations, reaction time, and adsorbent dosage were studied. The experimental design study was carried out with RSM-BBD method. Results prediction and optimization were investigated with RSM and artificial neural network (ANN)-genetic algorithm (GA) respectively. The RSM results showed that the experimental data followed the quadratic model with the highest regression coefficient value (R2 = 0.9903) and insignificant lack of fit (0.2426) showing the validity of the Quadratic model. The optimal adsorption conditions were obtained at pH 5.44, adsorbent = 0.98 g/l, concentration of Pb (II) ions = 25 mg/L, and reaction time = 68 min. Similar optimization results were observed by RSM and artificial neural network-genetic algorithm methods. The experimental data revealed that the process followed the Langmuir isotherm and the maximum adsorption capacity was 40.86 mg/g. Besides, the kinetic data indicated that the results fitted with the pseudo-second-order model. Hence, the MMT-K10 nanoclay can be a suitable adsorbent due to having a natural source, simple and inexpensive preparation, and high adsorption capacity.

Adsorptive removal of humic substances using cationic surfactant-modified nano pumice from water environment: Optimization, isotherm, kinetic and thermodynamic studies.

In this study, nano pumice (NP) and a cationic surfactant (hexadecyltrimethylammonium-chloride (HDTMA.Cl)) treated nano pumice (HMNP) were used for humic acid (HA) adsorption from an aqueous solution. The adsorption process was modeled and optimized using Response surface methodology-central composite design (RSM-CCD) and Artificial neural networks- Genetic algorithm (ANN-GA). The results show that the ANN model outperforms the RSM-CCD model in terms of response prediction. Optimization results based on the RSM-CCD approach proposed pH 3, adsorbent dose 3 g L-1, reaction time 60 min, and initial HA concentration 5 mg L-1 as optimal points of the variables, to reach the maximum adsorption efficiency of 100% and 65.4% by HMNP and NP adsorbents. The maximal adsorption capacity of NP was 1.21 mg g-1, while that of HMNP was 27.34 mg g-1. The optimal points of process parameters by the ANN-GA method are in accordance with the values suggested by the RSM-CCD method. In isotherm studies, Langmuir model was found to be the best-fitted model for both adsorbent with R2 = 0.97 for NP and 0.992 for HMNP, and also among three different kinetic models which were assessed, Pseudo-second-order model with R2 = 0.9989 for HMNP and R2 = 0.9957 for NP were the best-fitted models for HA removal. Thermodynamic studies indicated that the HA adsorption process by both of the adsorbents is endothermic and the nature of HMNP was found spontaneous while for NP was non-spontaneous. The value of ΔH for both adsorbents was in the range of 34-36.8 kJ mol-1 so the process is clarified as chemical-physical adsorption. The reusability test revealed that the adsorption effectiveness of HMNP drops from 100% to 82.4% after 10 consecutive recycles. The influence of interfacing anions indicated that the adsorption efficiency drops from 100% to 95.4% when the anions were added to the reaction solution.

Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm.

The need for fresh water is more than before by population growth, and industrial development have affected the quality of water supplies, one of the important reason for water contamination is synthetic dyes and their extensive use in industries. Adsorption has been considered as a common methods for dye removal from waters. In this study, Acid Red18 removal in batch mode by using Granular Ferric Hydroxide (GFH) was investigated. The GFH characterized by XRD, FESEM and FTIR analysis. Experiments were designed using RSM-CCD method. The maximum removal efficiency was obtained 78.59% at pH = 5, GFH dosage = 2 g/l, AR18 concentration = 77.5 mg/l and 85 min of contact time. Optimization with RSM and Genetic Algorithm carried out and is similar together. The non-linear adsorption Isotherm and kinetic fitted with Freundlich (R2 = 0.978) and pseudo-second-order (R2 = 0.989) models, respectively. Thermodynamic studies showed that the AR18 adsorption is endothermic process and GFH nature was found spontaneous.

Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches.

In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box-Behnken design (BBD), and process variables were modeled and optimized using Box-Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R2 = 0.9436), and ANN suggested a three-layer model with test-R2 = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg-Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L-1 and MSW compost ash dosage = 2 g L-1. The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale.