Biomass-Based Iron Removal: Adsorption Kinetics, Isotherm and Machine Learning Modelling with <italic>Ocimum sanctum</italic> And <italic>Syzygium cumini</italic>

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Biomass-Based Iron Removal: Adsorption Kinetics, Isotherm and Machine Learning Modelling with Ocimum sanctum And Syzygium cumini

Paranjape, Praveda / Sadgir, Parag

Iron concentrations in drinking water beyond the acceptable level adversely affect human health and aquatic life. Innovative chemical-based iron extraction processes exist, albeit being costly. Therefore, iron removal using easily accessible plants is the most sustainable alternative. The bioadsorption of iron by Ocimum sanctum Linn. leaves (OSL) and Syzygium cumini seed (SCS) biomass was examined considering several parameters viz. pH, biomass quantity, time of contact, initial iron metal concentration and temperature, etc. The adsorption process appears spontaneous, feasible, and exothermic. The batch adsorption of iron obeyed Langmuir isotherm and pseudo first order kinetic model for both bioadsorbents. Monolayer adsorption capacities (mg/g) were 123.26 (OSL) and 96.25 (SCS). OSL biomass proves more effective in removing iron than SCS biomass. Artificial neural network (ANN) and Support vector machine (SVM) were used to develop predictive model. The results showed that both models revealed good abilities for predicting the % removal of iron. To assess the prediction performance of ANN and SVM models statistical methods (R² and MSE) were employed. The SVM outperforms ANN in terms of prediction performance. Future perspectives of the applicability of OSL and SCS bioadsorbents for industrial purposes comprise extensive research with real wastewaters containing iron and/or other heavy metals.