Characterization of activated carbons produced from agricultural waste with various techniques and application of dyestuff removal in batch system

In this study, eight different active carbon produced from walnut shells and peach stone whose properties determined are used for the removal of Reactive Black 5 ve Vat Yellow 33 from watery ambient.Adsorption process of activated carbon, produced from raw materials, of walnut and peach having the highest adsorption capacity in comparison of the other produced active carbons, was accomplished in batch systems. In order to determine the effect of removing characteristics of the Reactive Black 5 (RB 5) and Vat Yellow 33 (VY 33) dyestuffs, batch adsorpsiyon experiments have been carried out by using these activated carbons and the effect of initial dye concentration, contact time and temperature on the dye removal process have been investigated. Langmuir, Freundlich and Temkin İsotherms were used for the analysis of adsorption data for adsorbent. From the İsotherm Langmuir, Freundlich and Temkin constants were determined. Langmuir model have been found in order to provide the best fit for the Reactive Black 5 and Vat Yellow 33 dye adsorption on the selected activated carbon. For the temperature of 30 oC, Langmuir adsorption capacities of activated carbon ŞF 700 and CF 700 for Reactive Black 5 have been estimated as 61,35 - 42,86 mg/ L, ŞK 700 and CK 700 for Vat Yellow 33 have been estimated as 95,24 ? 75,76 mg/L. Batch studies were carried out to identify the potential rate controlling steps for dye adsorption and to determine external film mass transfer and intraparticle diffusion coefficients. The result of applying experimental datas to pseudo first and second order kinetic models have revealed that the most appropriate model for the adsorption process pseudo second order kinetic model. Activation energies calculated from Arrhenius equation by using rate constants obtained from this model, have been found to be in the range of 5,59 kj/mol - 16,75 kJ/mol. Thermodynamic parameters have been calculated using Langmuir energy consant and it has been found that the adsorption process is endothermic.Using the datas taken from the experimental study, the applicability of artificial neural network on the estimation of adsorption efficiency result were compared to experimental output. Back-propagation multi-layer artificial neural networks were used for the estimation of adsorption efficiency. Performance of artificial neural networks was adjusted by changing the number of neurons in hidden layer, learning rate and momentum constant.