Phytoextraction potential of water fern (Azolla pinnata) in the removal of a hazardous dye, methyl violet 2B: Artificial neural network modelling

被引:22
作者
Kooh, Muhammad Raziq Rahimi [1 ]
Lim, Linda B. L. [1 ]
Lim, Lee-Hoon [1 ]
Malik, Owais Ahmed [2 ]
机构
[1] Univ Brunei Darussalam, Fac Sci, Chem Sci, Jalan Tungku Link, BE-1410 Gadong, Brunei
[2] Univ Brunei Darussalam, Fac Sci, Comp Sci, Jalan Tungku Link, Gadong, Brunei
关键词
Methyl violet 2b; phytoextraction; water fern (Azolla pinnata); PHYTOREMEDIATION; ADSORPTION; PIGMENTS; GROWTH; LEMNA;
D O I
10.1080/15226514.2017.1365337
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study investigated the potential of Azolla pinnata (AP) in the removal of toxic methyl violet 2B (MV) dye wastewater using the phytoextraction approach with the inclusion of an Artificial Neural Network (ANN) modelling. Parameters examined included the effects of dye concentration, pH and plant dosage. The highest removal efficiency was 93% which was achieved at a plant dosage of 0.8g (dye volume = 200 mL, initial pH = 6.0, initial dye concentration = 10 mg L-1). A significant decrease in relative frond number (RFN), a growth rate estimator, observed at a dye concentration of 20 mg L-1 MV indicated some toxicity, which coincided with the plant pigments studies where the chlorophyll a content was lower than the control. There were little differences in the plant pigment contents between the control and those in the presence of dye (5 to 15 mg L-1) indicating the tolerance of AP to MV at lower concentrations. A three-layer ANN model was optimized (6 neurons in the hidden layer) and successfully predicted the phytoextraction of MV (R = 0.9989, RMSE = 0.0098). In conclusion, AP proved to be a suitable plant that could be used for the phytoextraction of MV while the ANN modelling has shown to be a reliable method for the modelling of phytoextraction of MV using AP.
引用
收藏
页码:424 / 431
页数:8
相关论文
共 35 条
[1]  
Ahmed T.F., 2012, Int. Res. J. Environ. Sci., V1, P41
[2]  
Al-Shayea Q. K., 2011, International Journal of Computer Science Issues, V8, P150
[3]  
[Anonymous], 2009, SIGKDD Explorations, DOI DOI 10.1145/1656274.1656278
[4]  
[Anonymous], PRACTICAL PHARM
[5]   ERROR MEASURES FOR GENERALIZING ABOUT FORECASTING METHODS - EMPIRICAL COMPARISONS [J].
ARMSTRONG, JS ;
COLLOPY, F .
INTERNATIONAL JOURNAL OF FORECASTING, 1992, 8 (01) :69-80
[6]   COPPER ENZYMES IN ISOLATED CHLOROPLASTS - POLYPHENOLOXIDASE IN BETA-VULGARIS [J].
ARNON, DI .
PLANT PHYSIOLOGY, 1949, 24 (01) :1-15
[7]   Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA) [J].
Asl, SeyedMostafa Hosseini ;
Ahmadi, Maral ;
Ghiasvand, Mohamad ;
Tardast, Ali ;
Katal, Reza .
JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY, 2013, 19 (03) :1044-1055
[8]   Artificial neural networks: fundamentals, computing, design, and application [J].
Basheer, IA ;
Hajmeer, M .
JOURNAL OF MICROBIOLOGICAL METHODS, 2000, 43 (01) :3-31
[9]  
Bello OS, 2013, INT J BASIC APPL SCI, V13, P98
[10]   Non-conventional low-cost adsorbents for dye removal: A review [J].
Crini, G .
BIORESOURCE TECHNOLOGY, 2006, 97 (09) :1061-1085