The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii

被引:35
作者
Podder, M. S. [1 ]
Majumder, C. B. [1 ]
机构
[1] Indian Inst Technol, Dept Chem Engn, Roorkee 247667, Uttar Pradesh, India
关键词
Arsenic; Wastewater; Phycoremediation; Botryococcus braunii; Artificial neural network (ANN); HEAVY-METAL REMOVAL; AQUEOUS-SOLUTION; ARSENIC REMOVAL; ANN APPROACH; LIVING CELLS; BIOSORPTION; ADSORPTION; IONS; EQUILIBRIUM; SURFACE;
D O I
10.1016/j.saa.2015.11.011
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
In the present study, a thorough investigation has been done on the removal efficiency of both As(III) and As (V) from synthetic wastewater by phycoremediation of Botryococcus braunii algal biomass. Artificial neural networks (ANNs) are practised for predicting % phycoremediation efficiency of both As(III) and As(V) ions. The influence of several parameters for example initial pH, inoculum size, contact time and initial arsenic concentration (either As(III) or As (V)) was examined systematically. The maximum phycoremediation of As(III) and As(V) was found to be 85.22% and 88.15% at pH 9.0, equilibrium time of 144 h by using algal inoculum size of 10% (v/v) and initial arsenic concentration of 50 mg/L. The data acquired from laboratory scale experimental set up was utilized for training a three-layer feed-forward back propagation (BP) with Levenberg-Marquardt (LM) training algorithm having 4:5:1 architecture. A comparison between the experimental data and model outputs provided a high correlation coefficient (R-all_ANN(2) equal to 0.9998) and exhibited that the model was capable for predicting the phycoremediation of both As(III) and As(V) from wastewater. The network topology was optimized by changing number of neurons in hidden layers. ANNs are efficient to model and simulate highly non-liner multivariable relationships. Absolute error and Standard deviation (SD) with respect to experimental output were calculated for ANN model outputs. The comparison of phycoremediation efficiencies of both As(III) and As(V) between experimental results and ANN model outputs exhibited that ANN model can determine the behaviour of As(III) and As(V) elimination process under various circumstances. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:130 / 145
页数:16
相关论文
共 61 条
  • [51] A Review on Heavy Metals (As, Pb, and Hg) Uptake by Plants through Phytoremediation
    Tangahu, Bieby Voijant
    Abdullah, Siti Rozaimah Sheikh
    Basri, Hassan
    Idris, Mushrifah
    Anuar, Nurina
    Mukhlisin, Muhammad
    [J]. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING, 2011, 2011
  • [52] Fixed-bed study for lanthanide (La, Eu, Yb) ions removal from aqueous solutions by immobilized Pseudomonas aeruginosa:: experimental data and modelization
    Texier, AC
    Andrès, Y
    Faur-Brasquet, C
    Le Cloirec, P
    [J]. CHEMOSPHERE, 2002, 47 (03) : 333 - 342
  • [53] Tien VN, 2004, J IND ENG CHEM, V10, P337
  • [54] Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent
    Turan, N. Gamze
    Mesci, Basak
    Ozgonenel, Okan
    [J]. CHEMICAL ENGINEERING JOURNAL, 2011, 173 (01) : 98 - 105
  • [55] The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice
    Turan, N. Gamze
    Mesci, Basak
    Ozgonenel, Okan
    [J]. CHEMICAL ENGINEERING JOURNAL, 2011, 171 (03) : 1091 - 1097
  • [56] Biosorption and bioaccumulation of heavy metals on dead and living biomass of Bacillus sphaericus
    Velasquez, Lina
    Dussan, Jenny
    [J]. JOURNAL OF HAZARDOUS MATERIALS, 2009, 167 (1-3) : 713 - 716
  • [57] BIOSORPTION OF HEAVY-METALS
    VOLESKY, B
    HOLAN, ZR
    [J]. BIOTECHNOLOGY PROGRESS, 1995, 11 (03) : 235 - 250
  • [58] Biosorption and me
    Volesky, Bohumil
    [J]. WATER RESEARCH, 2007, 41 (18) : 4017 - 4029
  • [59] Two stage treatment of dairy effluent using immobilized Chlorella pyrenoidosa
    Yadavalli, Rajasri
    Heggers, Goutham Rao Venkata Naga
    [J]. JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE AND ENGINEERING, 2013, 11
  • [60] Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells
    Yetilmezsoy, Kaan
    Demirel, Sevgi
    [J]. JOURNAL OF HAZARDOUS MATERIALS, 2008, 153 (03) : 1288 - 1300