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.