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<p>A method was designed to determine the adsorption of RHB from wastewater. On this note, the properties of the developed material were assessed utilizing FTIR, XRD, SEM, and TGA. The study analyzed various parameters, including pH, concentration, contact time, adsorbent dose, and temperature. These parameters were used as input data, while the output data was based on RHB removal efficiency. For prediction and optimization, response surface methodology/central composite design (RSM-CCD), artificial neural network (ANN), and adaptive neuron-fuzzy inference system (ANFIS) models were applied for RHB adsorption. Additionally, the relevance of these models was analyzed using statistical metrics. In developing the ANN and ANFIS models, 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 pH 7, contact time of 55&#xa0;min, 20&#xa0;g/L of adsorbent, temperature of 40&#xa0;°C, and RHB concentration of 100&#xa0;mg/L. However, an ideally trained neural network is described using training, testing, and validation phases, and the R<sup>2</sup> values at these phases were found to be 1, 0.96837, and 0.96146, respectively. The statistical results demostrated that the ANFIS approach outperforms the RSM and ANN model methods. The results from actual water samples also suggested that the synthesized GCCH was quite effective for real-world treatment procedures. Overall, the findings showed that the suggested adsorbent is economical and effective for the adsorptive removal of dye from polluted waters.</p>

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The Adsorption of Rhodamine-B (RHB) By Modified Chitosan Beads in Synthetic Wastewater: An Evaluation of RSM, ANN, and ANFIS Techniques in Predicting Model Outcomes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Ephraim Igberase,
  • Innocentia G. Mkhize

摘要

A method was designed to determine the adsorption of RHB from wastewater. On this note, the properties of the developed material were assessed utilizing FTIR, XRD, SEM, and TGA. The study analyzed various parameters, including pH, concentration, contact time, adsorbent dose, and temperature. These parameters were used as input data, while the output data was based on RHB removal efficiency. For prediction and optimization, response surface methodology/central composite design (RSM-CCD), artificial neural network (ANN), and adaptive neuron-fuzzy inference system (ANFIS) models were applied for RHB adsorption. Additionally, the relevance of these models was analyzed using statistical metrics. In developing the ANN and ANFIS models, 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 pH 7, contact time of 55 min, 20 g/L of adsorbent, temperature of 40 °C, and RHB concentration of 100 mg/L. However, 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 results demostrated that the ANFIS approach outperforms the RSM and ANN model methods. The results from actual water samples also suggested that the synthesized GCCH was quite effective for real-world treatment procedures. Overall, the findings showed that the suggested adsorbent is economical and effective for the adsorptive removal of dye from polluted waters.