ORIGINAL RESEARCH
Applying an Artifi cial Neural Network
to Predict Coagulation Capacity of Reactive
Dyeing Wastewater by Chitosan
Ha Manh Bui1, Huong Thi Giang Duong1, Cuong Duc Nguyen2
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1Department of Environmental Sciences, Sai Gon University, Vietnam
2Department of Chemical Technology, Ho Chi Minh City Industry and Trade College, Vietnam
Submission date: 2015-09-13
Final revision date: 2015-12-21
Acceptance date: 2015-12-23
Publication date: 2016-03-17
Pol. J. Environ. Stud. 2016;25(2):545-555
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ABSTRACT
Chitosan derived from waste shrimp shells was experimentally evaluated to treat reactive dye (red
24) in aqueous solution. The research was conducted using one-factor-at-a-time experiment design (i.e.,
pH, reaction time, agitation speeds, initial dye, and chitosan concentration) to investigate dye removal
effi ciencies by reducing colour and COD parameters. The results obtained by performing Jar-tests indicated
that prepared chitosan successfully removed the reactive dye in aqueous solution. In particular, 99.5% of
colour and 72.7% COD removal effi ciencies were recorded under neutral conditions (pH 7) with chitosan
dosage 80 mg/L and agitation speed 60 rpm in 30 min. Fourier transform infrared spectra (FTIR) of the
chitosan, dye, and formed sludge were also investigated to elucidate the removal mechanism of the dye
by chitosan. These indicated the potential of using chitosan as a “green” coagulant to reduce pollutants of
textile wastewater. Moreover, three-layer feed-forward artifi cial neural network (ANN) models were applied
to model the coagulation processes with the determination coeffi cient (R2) 0.986 and the root mean square
error (RMSE) 2.951 between predicted and observed outputs. The ANN models were analysed with the
connection weight method, neural interpretation diagram, and 3D/contour plots to study the infl uences of
operation factors – both individually and combined – on both colour removal and COD removal process.