Application of Response Surface Methodology and Artificial Neural Network in the Adsorption of Methylene Blue Using Enhansed Chitosan Beads
A technique was developed to determine the adsorption of methylene blue (MB) from synthetic wastewater. On this note, chitosan beads (CS) were developed, the beads were cross-linked with glutaraldehyde (CCS) and thereafter grafted with aniline (GCCS). The properties of the developed materials were a...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2025-07-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/15451 |
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| Summary: | A technique was developed to determine the adsorption of methylene blue (MB) from synthetic wastewater. On this note, chitosan beads (CS) were developed, the beads were cross-linked with glutaraldehyde (CCS) and thereafter grafted with aniline (GCCS). The properties of the developed materials were assessed utilizing XRD and BET. The study analyzed parameters, including pH, contact time, adsorbent dose, and initial concentration. These parameters were used as input data, while the output data was based on MB removal efficiency. For prediction and optimization, response surface methodology/central composite design (RSM-CCD) and artificial neural network (ANN) were applied for MB adsorption. Additionally, the relevance of these models was analyzed using statistical metrics. However, in developing the ANN model, 70% of the data was allocated for training, 15% for validation, and 15% for testing. Based on the RSM-CCD findings, the optimization outcome for the process parameters was obtained at a pH 7 adsorbent dose of 6 g, contact time of 55 min, and initial concentration of 125 mg/L. Consequently, an ideally trained neural network is described using training, testing, and validation phases, and the R2 values at these phases were found to be 1, 0.96837, and 0.96146, respectively. The statistical findings showed that the ANN approach outperforms the RSM model approach. |
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| ISSN: | 2283-9216 |