Application of ANN and RSM on fluoride removal using chemically activated D. sissoo sawdust

Environmental Science and Pollution Research International
Saurabh JoshiSomen Jana

Abstract

Response surface methodology (RSM) and artificial neural network (ANN) were used to generate a model for the optimization of fluoride removal using chemically activated Dalbergia sissoo sawdust (CADS). The single and collective effects of process parameters, i.e., solution pH, CADS dose, initial fluoride concentration, and contact time, were studied. The point of zero charge was found to be 4.2 with zeta potential analysis. In the first phase, a single-parameter study was performed to reveal dependency of fluoride removal on a particular process parameter. Positive effects of increment in CADS dose and contact time and negative effects of solution pH and initial fluoride concentration were observed. The second phase included RSM in which analysis of variance (ANOVA) was applied to test the feasibility of the mathematical model. The F value 1.91, R2 value 0.87, and P value 0.11 show significance of the proposed model. Results obtained from the experiment set for central composite design (CCD) were used to predict the ANN response. Reasonable acceptable values of regression for training, test, and validation (0.76, 0.93, and 0.37) represent the suitability of the model. The ANN predicted 22.1% fluoride removal, which was close to...Continue Reading

References

Jan 27, 2007·Environmental Geochemistry and Health·F M FordyceJ Baker
Mar 4, 2008·Journal of Hazardous Materials·Mohamed Tir, Nadji Moulai-Mostefa
May 3, 2011·Bioresource Technology·Kaushik Nath, Debabrata Das
Oct 25, 2011·Bioresource Technology·Abuzer ÇelekliHüseyin Bozkurt
Apr 24, 2014·Environmental Science and Pollution Research International·Ali BoubakriSalah Al-Tahar Bouguecha

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Citations

Nov 6, 2021·Environmental Geochemistry and Health·Musa AtaşAyşegül Demir Yetiş

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