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Neural Modeling Adsorption of Copper, Chromium, Nickel, and Lead from Aqueous Solution by Natural Wastes

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Progress in Clean Energy, Volume 1

Abstract

An artificial neural network (ANN) is used to model the static adsorption of copper, chromium, lead, and nickel by natural wastes, which are respectively charred cereal waste, Mediterranean biomass (Posidonia oceanica (L) DELILE), activated carbon as well as olive kernel and pulp. This intelligent model is used to predict and estimate the amount of adsorbed metal per mass unit of adsorbent or the yield percentage of the adsorption. The results obtained using multilayer neural network shows its effectiveness in predicting the experimental results. The relative error is 0.2 mg/g for charred cereal waste/Copper, Biomass/Chromium, and 1.9 % for the combinations activated carbon/Lead, olive kernel/Nickel, and olive pulp/Nickel, respectively. Furthermore, the same artificial neural network is exploited to predict the effect of some operating parameters (pH, temperature, initial metal concentration, contact time, agitation speed, ionic strength, and adsorbent weight) that affect the static adsorption of these metals by several types of adsorbents.

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Abbreviations

AC:

Activated carbon

ANN:

Artificial neural network

b i :

Bias of the ith node

BM:

Mediterranean biomass

C in :

Initial concentration, mg/g

Cu:

Copper

I s :

Inonic strength, mol m3

J :

Jacobian matrix of weights and biases derivate

L :

ANN output dimension

M :

Number of iterations

Ni:

Nickel

OK:

Olive kernel

OP:

Olive pulp

Pb:

Lead

s :

Neuron inputs sum

S a :

Agitation speed, round per minute (rpm)

T :

Temperature, °C

t c :

Contact time

w ij :

Connection weight between node and node j

WCC:

Waste charred cereal

W d :

Adsorbent weight

y i :

ith desired output

z i :

ith neuron output

μ :

Learning coefficient

ε i :

Output error of the ith node

i :

Node and output index

j :

Node index

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Authors and Affiliations

  1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Avenue de l’ALN, Ain Dheb, Medea, 26000, Algeria

    Samia Rebouh, Salah Hanini & Abdenour Djellal

  2. Laboratory of Advanced Electronic Systems (LSEA), University of Medea, Nouveau pôle urbain, Medea, 26000, Algeria

    Mounir Bouhedda

Authors
  1. Samia Rebouh
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  2. Mounir Bouhedda
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  3. Salah Hanini
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  4. Abdenour Djellal
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Corresponding author

Correspondence to Samia Rebouh .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Ontario, Oshawa, Ontario, Canada

    Ibrahim Dincer

  2. Department of Mechanical Engineering, Dokuz Eylul University, Izmir, Turkey

    C. Ozgur Colpan

  3. Department of Energy Systems Engineering, Suleyman Demirel University, Isparta, Turkey

    Onder Kizilkan

  4. Department of Mechanical Engineering, Dokuz Eylul University, Izmir, Turkey

    M. Akif Ezan

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Rebouh, S., Bouhedda, M., Hanini, S., Djellal, A. (2015). Neural Modeling Adsorption of Copper, Chromium, Nickel, and Lead from Aqueous Solution by Natural Wastes. In: Dincer, I., Colpan, C., Kizilkan, O., Ezan, M. (eds) Progress in Clean Energy, Volume 1. Springer, Cham. https://doi.org/10.1007/978-3-319-16709-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-16709-1_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16708-4

  • Online ISBN: 978-3-319-16709-1

  • eBook Packages: EnergyEnergy (R0)

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