Evaluation and machine learning-based prediction of Zn2+treatment by the cyanobacterium biomaterial at packed columns

Excessive accumulation of zinc in the environment poses ecological risks. Hence, an approach with cost-effective and eco-friendly treatment media, such as microbial biomass, should be explored to remediate zinc from effluents. This study demonstrated the removal of Zn2+ by packed columns, comprising Spirulina platensis (cyanobacterium) biomass fixed on polyurethane. Machine learning (ML) was also used to optimize and predict the concentration of Zn2+ in the effluent. The results show optimum condition for Zn2+ removal at a biomaterial height of 25 cm, a flow rate of 5 mL/min, and an inlet Zn2+ concentration of 100 mg/L. Further, the Cubist algorithm predicted the Zn2+ concentrations (new data) with R2 of 0.988 and 5.34 mg/L in RMSE, and artificial neural networks (ANN) achieved 0.979 in R2 and 6.94 mg/L in RMSE. Based on both the estimated accuracy and computing time, the Cubist model was found more advantageous than the ANN and linear regression models. This study provides optimum conditions for scaling up Zn2+ treatment systems and an ML-based tool to estimate Zn2+ effluents to support the design of adsorption column systems.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).