@article{Igberase_Sithole_2024, title={[Not quotable] The adsorption of Pb2+ and Ni2+ ions utilizing modified chitosan beads: A response surface methodology and artificial neural network modelling study}, volume={61}, url={https://eqa.unibo.it/article/view/18471}, DOI={10.6092/issn.2281-4485/18471}, abstractNote={<p><strong>Editorial Note - 2024-05-31</strong><br /><span class="ui-provider ed bhx bhy bhz bia bib bic bid bie bif big bih bii bij bik bil bim bin bio bip biq bir bis bit biu biv biw bix biy biz bja bjb bjc bjd bje" dir="ltr">The article has been temporarily retracted </span><span class="ui-provider ed bhx bhy bhz bia bib bic bid bie bif big bih bii bij bik bil bim bin bio bip biq bir bis bit biu biv biw bix biy biz bja bjb bjc bjd bje" dir="ltr">due to ongoing investigations</span>.<br />---<br />This work investigates the application of artificial neural networks (ANN) and response surface methodology (RSM) in developing a technique for removing Pb<sup>2+</sup> and Ni<sup>2+</sup> ions from wastewater using chitosan derivative. The materials including chitosan beads (CS) and grafted chitosan beads (MCS) were evaluated using infrared spectroscopy (FTIR) and a scanning electron microscope (SEM). The process factors were modeled and optimized using the central composite design (CCD) derived from RSM. Removal efficiency was described as the response for the output layer. However, the input layer feed data consists of pH, adsorbent dose, contact duration, temperature, and concentration. Two neurons were used as the ANN algorithm’s output layers, which correspond to the adsorption of Pb<sup>2+</sup> and Ni<sup>2+</sup> ions. Both models were measured using statistical metrics like average relative errors (ARE), coefficient of determination (R<sup>2</sup>), Marquart’s percentage standard deviation (MPSD), mean squared error (MSE), Pearson’s Chi-square (), root means square errors (RMSE), and the sum of squares of errors (SSE). The ideal trained neural network depicts the training, validation, and testing phases, with R<sup>2</sup> values of 1.0, 0.968, and 0.961, respectively. The findings, however, showed that the ANN technique is superior to the RSM-CCD model approach. At pH 5, starting concentration of 100 mg/L, an adsorbent mass of 6.0 g, a reaction time of 55 min, and a temperature of 40 <sup>o</sup>C, the RSM-CCD model’s optimization results for the process variables were achieved. The greatest removal percentages for Pb<sup>2+</sup> and Ni<sup>2+</sup> ion was 98.14% and 98.12%, respectively. The findings suggest that ANN can be utilized in forecasting the removal of adsorbates from wastewater.</p>}, journal={EQA - International Journal of Environmental Quality}, author={Igberase, Ephraim and Sithole, Nastassia Thandiwe}, year={2024}, month={May}, pages={1–15} }