Serpil Edebali

Background: In recent years, low-cost biomaterials have been used for sustainable applications to reduce the impact of wastewater treatment. The preparation of bio-based materials with a strong affinity for chromate plays a crucial role in the adsorption process. Methods: In this study, chitosan-halloysite nanotubes composite hydrogel beads (Ch-HNTs) were prepared for the removal of hexavalent chromium (Cr6+) from aqueous solutions. Ch-HNTs hydrogel beads were generated by incorporating HNTs into chitosan using a glutaraldehyde solution to achieve efficient crosslinking. The structure of the Ch-HNTs was characterized by SEM and FTIR analysis. These novel adsorbents were then tested for the adsorption of Cr6+ in serial batch experiments. For this purpose, the effect of pH, contact time, temperature, concentration of adsorbate, and adsorbent concentration on the extent of adsorption were investigated. Results: It is proposed that Ch-HNTs can be potential adsorbents for Cr6+ removal from dilute solutions. Nonetheless, further studies on adsorbing and removing various heavy metals using these novel beads in column systems can be planned. Conclusion: It is proposed that Ch-HNTs can be potential adsorbents for Cr6+ removal from dilute solutions. Nonetheless, further studies on adsorbing and removing various heavy metals using these novel beads in column systems can be planned.

In this study, the malachite green adsorption process using Amberlite IRC-748 and Diaion CR-11 resins was modelled by artificial neural network method. In the model created for this study, adsorbent dosage, initial malachite green concentration and contact time parameters, which are the independent variables of the adsorption process, were used as input. Adsorption percentage values, which are the dependent variables of the adsorption process, were obtained as output. Mean squared error (MSE) and determination coefficient (R2) values were obtained from the models created using thirty-one experimental data for adsorption of malachite green with Amberlite IRC-748 and thirty-eight experimental data for adsorption with Diaion CR-11. By evaluating these values together, the most appropriate training algorithm, transfer function in the hidden layer and the number of neurons in the hidden layer were defined. Accordingly, for both Amberlite IRC-748 and Diaion CR-11 resins, the optimum training algorithm was determined as Levenberg-Marquardt back-propagation and the optimum hidden layer transfer function as tan sigmoid. The optimum number of neurons in the hidden layer was identified as 13 for Amberlite IRC-748 and 12 for Diaion CR11. The MSE, R2all and R2test values of the models produced with the optimum parameters were obtained as 0.000261, 0.9972, 0.9903 for Amberlite IRC-748 and 0.000482, 0.9932, 0.9931 for Diaion CR11, respectively.

Synthetic dyes are extensively used in industrial areas, including plastic, textile, and food. However, they are a major environmental problem due to their negative effects on water quality and living organisms. To address one of these problems, MIL-53 (Al) is served as an adsorbent for removing indigo carmine dye, being widely used in textile industries. The synthesis of MIL-53 (Al) was carried through the hydrothermal method and different synthesis conditions were studied to find the best adsorbent to remove indigo carmine. FTIR, XRD, SEM, and EDS were used to assess materials. Isotherm models and kinetic models were investigated for indigo carmine adsorption, revealing that the Langmuir isotherm model and pseudo-second-order kinetic model provided best fit to data. The highest adsorption capacity was calculated as 145 mg/g. The study contributes valuable insights into the adsorption of indigo carmine by MIL-53 (Al).

In this study, an artificial neural network model was developed to estimate the removal efficiency of Cr (VI) ion from waste water by Lewatit MP64 and Dowex 1×8 resins. For this purpose, 36 experimental data obtained in a laboratory batch study. In the developed model, contact time, adsorbent dosage, pH and concentration were used as the input parameters, and removal efficiency for Lewatit MP64 and Dowex 1×8 were also used as output parameters. The model performances were determined by the mean square error and the coefficient of determination. The model using the Levenberg-Marquardt backpropagation algorithm (TrainLM) was found the best prediction. This model also has a hidden layer and 15 neurons (4-15-1). The coefficient of determination between experimental and estimates was found to be 0.99 removal efficiency for Lewatit MP64 and 0.92 for Dowex 1×8. The results show that removal efficiency can be predicted successfully with artificial neural networks.

In this research, artificial neural network (ANN) model having three layers was developed for precise estimation of Cr(III) sorption rate varying from 17% to 99% by commercial resins as a result of obtaining 38 experimental data. ANN was trained by using the data of sorption process obtained at different pH (2–7) values with Amberjet 1200H and Diaion CR11 amount (0.01–0.1 g) dosage, initial metal concentration (4.6–31.7 ppm), contact time (5–240 min), and a temperature of 25°C. A feed-forward back propagation network type with one hidden layer, different algorithm (transcg, trainlm, traingdm, traincgp, and trainrp), different transfer function (logsig, tansig, and purelin) for hidden layer and purelin transfer function for output layer were used, respectively. Each model trained for cross-validation was compared with the data that were not used. The trainlm algorithm and purelin transfer functions with five neurons were well fitted to training data and cross-validation. After the best suitable coefficient of determination and mean squared error values were found in the current network, optimal result was searched by changing the number of neurons range from 1 to 20 in the current network hidden layer.