Parameters optimization of rice husk ash (RHA)/CaO/CeO2 sorbent for predicting SO2/NO sorption capacity using response surface and neural network models

Abstract

In this work, the application of response surface and neural network models in predicting and optimizing the preparation variables of RHA/CaO/CeO(2) sorbent towards SO(2)/NO sorption capacity was investigated. The sorbents were prepared according to central composite design (CCD) with four independent variables (i.e. hydration period, RHA/CaO ratio, CeO(2) loading and the use of RHA(raw) or pretreated RHA(600 degrees C) as the starting material). Among all the variables studied, the amount of CeO(2) loading had the largest effect. The response surface models developed from CCD was effective in providing a highly accurate prediction for SO(2) and NO sorption capacities within the range of the sorbent preparation variables studied. The prediction of CCD experiment was verified by neural network models which gave almost similar results to those determined by response surface models. The response surface models together with neural network models were then successfully used to locate and validate the optimum hydration process variables for maximizing the SO(2)/NO sorption capacities. Through this optimization process, it was found that maximum SO(2) and NO sorption capacities of 44.34 and 3.51 mg/g, respectively could be obtained by using RHA/CaO/CeO(2) sorbents prepared from RHA(raw) with hydration period of 12h, RHA/CaO ratio of 2.33 and CeO(2) loading of 8.95%.