Sustainable Cr (VI) removal using Ragi Husk: A data-driven metaheuristic optimization approach

Chromium, widely used in industries such as chemicals, textiles, and metal finishing, poses serious environmental and health hazards when discharged in its hexavalent form (Cr(VI)). This investigation highlights the use of Ragi Husk powder as a sustainable, resource-efficient and cost-effective bio-adsorbent for extracting Cr(VI) from industrial wastewater. Traditional optimization methods, like Box–Behnken Design (BBD), often fail to capture nonlinearities in adsorption processes. To overcome this, six machine learning (ML) models—three tree-based (M₁:Decision Tree Regressor (DTR), M₂:Random Forest Regressor (RFR), M₃:Extra Trees Regressor (ETR)) and three boosting-based (M₄:AdaBoost Regressor (ADBR), M₅:Gradient Boosting Regressor (GBR), M₆:LightGBR (LGBR))—were employed to predict Cr(VI) removal efficiency. These models integrated with Nelder–Mead Optimization (NMO), forming hybrid frameworks (NMO−M₁ to NMO−M₆) to identify optimal process parameters. Among them, LGBR and ETR exhibited superior prediction score (R²−Score) of 0.99%. The NMO-LGBR (NMO−M₆) optimization approach achieved a maximum Cr(VI) removal efficiency of 82.26%, which is 1%–4% higher than experimental results. The comparative analysis with existing literature revealed that the proposed models improved Cr(VI) removal efficiency by up to 4% over BBD and 1%–2% over ANN models. Experimental validation showed close alignment with model predictions, confirming the robustness of the proposed framework. Additionally, SEM analysis before and after Cr(VI) adsorption revealed that Ragi Husk retained its structural integrity, affirming its reusability and effectiveness. This work introduces a novel, scalable, data-driven approach that significantly enhances Cr(VI) removal efficiency while reducing experimental costs and time, demonstrating strong potential for eco-friendly wastewater treatment applications.