COD Reduction in Coal Gasifier Effluent using Adsorbents Derived from Agricultural Waste

Volume 12 Issue 2 November - January 2017

Research Paper

COD Reduction in Coal Gasifier Effluent using Adsorbents Derived from Agricultural Waste

M. Sivanagasai*, V. Sridevi**

* Postgraduate, Department of Chemical Engineering, Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India.

** Professor, Department of Chemical Engineering, Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India.

Sivanagasai, M., and Sridevi, V., (2017). COD Reduction in Coal Gasifier Effluent Using Adsorbents Derived From Agricultural Waste. i-manager’s Journal on Future Engineering and Technology, 12(2), 27-34. https://doi.org/10.26634/jfet.12.2.10371

Abstract

The reduction of COD (Chemical Oxygen Demand) from coal gasifier has been carried out using agricultural tobacco waste, and Madhuca longifolia seed as low cost adsorbents. The adsorption efficiency of agricultural tobacco waste, and Madhuca longifolia seed was investigated. In this study, a batch experimental process were carried out using agricultural tobacco waste, and Madhuca longifolia seed adsorbents for COD reduction in coal gasifier effluent. Batch experiments were setup to study the adsorbent dose, pH, agitation time, and initial concentration of adsorption on the reduction of COD levels were analyzed for each adsorbent individually. Based on the efficiency order, the study result showed that Madhuca longifolia seed is greater than agricultural tobacco waste. The experimental data is an excellent fit with Langmuir, Freundlich, and Temkin isotherm models for both adsorbents. Freundlich equation was observed to be more suitable for both adsorbents. The adsorption followed pseudo second order kinetic suited for both adsorbents. The parameters were changed at three levels according to the CCD to evaluate their effects on COD reduction through 2 analysis of variance and high correlation coefficient (R ) of 0.96 shows high correlation between experimental and predicted values.