Modelling and Optimisation of Zinc (II) Removal from Synthetic Acid Mine Drainage via Three-Dimensional Adsorbent Using a Machine Learning Approach

• Published in: Engineering proceedings 2023-05, Vol.37 (1), p.52
• Main Authors: Musamba Banza, Tumisang Seodigeng
• Format: Article
• Language: English
• Subjects: adaptive neuro-fuzzy inference system; artificial neural network; machine learning; response surface methodology; three-dimensional adsorbent
• ISSN: 2673-4591
• DOI: 10.3390/ECP2023-14711

This work uses three-dimensional green and biodegradable adsorbent from cellulose nanocrystals and a machine learning technique to simulate and optimise the removal of zinc (II) from synthetic acid mine drainage. The adsorption process was modelled and optimised using three machine learning algorithms: response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN). According to the findings, the created models successfully predicted the adsorption behaviour, with the ANN model performing best with the lowest error rate. The study also looked at the impact of other factors on the adsorption process, such as pH, adsorbent dosage, temperature, and starting concentration. The RSM was used to optimise the process, and the ideal conditions for the maximal zinc (II) removal efficiency were established. The best conditions were established to be an initial pH of 6, an initial concentration of 175 mg/L, a contact period of 100 min, and a sorbent dosage of 6 mg/L. The results show that the created three-dimensional adsorbent and machine learning approach, namely, the ANFIS model, are promising strategies for removing zinc (II) from acid drainage. The study’s findings might help develop cost-effective and efficient systems for treating polluted water supplies.