Parameters optimization of rice husk ash (RHA)/CaO/CeO2 sorbent for predicting SO2/NO sorption capacity using response surface and neural network models.
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%.
School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
Irvan
I
Dahlan
Zainal
Z
Ahmad
Muhammad
M
Fadly
Keat Teong
KT
Lee
Azlina Harun
AH
Kamaruddin
Abdul Rahman
AR
Mohamed
D000042:Absorption; D000465:Algorithms; D000704:Analysis of Variance; D017610:Calcium Compounds / Q000737:chemistry*; D002586:Cesium / Q000737:chemistry*; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009569:Nitric Oxide / Q000737:chemistry*; D012275:Oryza / Q000737:chemistry*; D010087:Oxides / Q000737:chemistry*; D015203:Reproducibility of Results; D013458:Sulfur Dioxide / Q000737:chemistry*; D013816:Thermodynamics
D017610:Calcium Compounds; D010087:Oxides; D013458:Sulfur Dioxide; D002586:Cesium; D009569:Nitric Oxide; C016538:lime