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Computational intelligence techniques for modeling of dynamic adsorption of organic pollutants on activated carbon

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Abstract

The objective of this work is to compare the efficiency of three computational intelligence techniques: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to model the dynamic adsorption of organic pollutants on activated carbon. A comparison study was enhanced using five models: ANN with conventional transfer functions, ANN with new transfer function called “SPOCU”, SVM, SVM hybrid with Dragonfly optimisation algorithm (SVM-DA) and ANFIS. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics of adsorption on activated carbon, was used in the modelling process. The studied parameters were molar mass, initial concentration, flow rate, bed height, BET surface area, time and concentration of non-dimensional effluents. Overall, the developed model was able to accurately estimate 11,763 experimental data points gathered from the literature. The performance of the optimised models has been evaluated using different metrics between the experimental and the predicted data. Results show that SVM-DA model can estimate accurately the dynamic adsorption of organic pollutants on activated carbon against the other tested models. Also a graphical user interface is developed in this paper in order to keep the traceability of the estimated results.

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Abbreviations

AAD:

Average absolute deviation

ANNs:

Artificial Neural Networks

Af :

Precision factor

Bf :

Biais factor

ANFISs:

Adaptive Neuro-Fuzzy Inference Systems

MSE:

Mean square error

MSRE:

Mean Square Relative Error

RMSE:

Root mean square error

RAE:

Relative Absolute Error

RRSE:

Root Relative Square Error

R:

Correlation coefficient

SVMs:

Support Vector Machines

M:

Molar mass

C0 :

Initial concentration

Q:

Flow rate

H:

Bed height

As:

Specific surface area

T:

Time

C/C0 :

Non-dimensional effluent concentration

LM:

Levenberg–Marquardt

BP:

Back-propagation

RBF:

Radial basis function

MFs:

Membership functions

ε :

Slump coefficient

C :

Cost factor

σ :

Kernel parameter

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Authors and Affiliations

  1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University Yahia Fares of Medea, Medea, Algeria

    Yamin Mesellem, Maamar Laidi, Salah Hanini & Mohamed Hentabli

  2. Faculty of Science, University Saad Dahleb of Blida, (USDB-1-), Somaa, Blida, Algeria

    Abdallah Abdallah El Hadj

  3. Laboratory Quality Control, Physico-Chemical Department, Antibiotical Saidal of Medea, Medea, Algeria

    Mohamed Hentabli

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  1. Yamin Mesellem
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Mesellem, Y., Hadj, A.A.E., Laidi, M. et al. Computational intelligence techniques for modeling of dynamic adsorption of organic pollutants on activated carbon. Neural Comput & Applic 33, 12493–12512 (2021). https://doi.org/10.1007/s00521-021-05890-2

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