期刊
APPLIED WATER SCIENCE
卷 7, 期 7, 页码 3949-3971出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13201-017-0547-z
关键词
Arsenic; Wastewater; Phycoremediation; Chlorella pyrenoidosa; Artificial neural network
资金
- Ministry of Human Resource Development, Government of India
An artificial neural network (ANN) model was developed to predict the phycoremediation efficiency of Chlorella pyrenoidosa for the removal of both As(III) and As(V) from synthetic wastewater based on 49 data-sets obtained from experimental study and increased the data using CSCF technique. The data were divided into training (60%) validation (20%) and testing (20%) sets. The data collected was used for training a three-layer feed-forward back propagation (BP) learning algorithm having 4-5-1 architecture. The model used tangent sigmoid transfer function at input to hidden layer (tansing) while a linear transfer function (purelin) was used at output layer. Comparison between experimental results and model results gave a high correlation coefficient (R (allANN) (2) equal to 0.99987 for both ions and exhibited that the model was able to predict the phycoremediation of As(III) and As(V) from wastewater. Experimental parameters influencing phycoremediation process like pH, inoculum size, contact time and initial arsenic concentration [either As(III) or As(V)] were investigated. A contact time of 168 h was mainly required for achieving equilibrium at pH 9.0 with an inoculum size of 10% (v/v). At optimum conditions, metal ion uptake enhanced with increasing initial metal ion concentration.
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