The removal of textile dyes using low – cost and locally available agro wastes as adsorbents have been investigated. Activated carbon was produced from mucuna seed shells (MSS), rubber seed shells (RSS) and African pear seeds (APS) using the chemical activation method. The physiochemical properties of the activated carbon were determined. FTIR, XRF and XRD were used to identify the various functional groups, chemical compositions and; the mineralogical composition and diffraction pattern respectively, present in the activated carbon. Its performance in the removal of the vat yellow 4 (VY4), congo red (CR) and malachite green (MG) dyes from aqueous solution were found to depend on the particle size, adsorbent dose, initial pH of solution, adsorbate concentration, contact time and solution temperature. From the batch adsorption studies, the experimental batch equilibrium data was correlated by Langmuir, Freundlich, Temkin and Dubinin-Rudushkevich isotherm. The Langmuir adsorption isotherm was found to have the best fit to the experimental data, suggesting monolayer adsorption on a homogeneous surface. The kinetic models; first-order, pseudo first-order, second order, pseudo second-order, Elovich, intraparticle diffusion and Bhattacharya – Venochabor were applied for the adsorption of dyes on the adsorbents. Results show that pseudo second-order kinetic model was found to correlate the experimental data well. The dimensionless separation factor (RL) has shown that the adsorbents can be used for the removal of these dyes from aqueous solutions. The adsorption process has been found to be endothermic in nature and the thermodynamics parameters Gibb’s free energy (ΔG0), change in enthalpy (ΔG0) and change in entropy (ΔH0) has been calculated. The negative free energy of adsorption indicated that the adsorption processes were feasible and spontaneous. Response Surface Methodology (RSM) based on Central Composite Rotatable Design (CCRD) was employed for the optimization studies. A predictive model describing the percentage efficiency in terms of the parameters was derived from multiple regression analysis.

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