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Determining optimum carob powder adsorbtion for cleaning wastewater: intelligent optimization with electro-search algorithm

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Abstract

In this study, effective and fast removal efficiency of carob powder (as an absorbent material from liquid phase) was investigated by focusing on the dangerous paint methylene blue mixture. The surface texture developed adsorbent was revealed to be porous by characterizing done thanks to scanning through electron microscope. Experiment parameters of pH, ultrasonic frequency, particle size, contact time, temperature and initial concentration of dissolved methylene blue dye were investigated accordingly. Thereafter, Box–Behnken design experiment was applied for adsorption experiments. Regression analysis findings demonstrated that the experimental data is good for to the non-linear model with correlation coefficients of correction value at 0.8899 and 0.9830. The maximum adsorption value was determined as around 256.44 mg/g thanks to the Electro-Search Algorithm, a recent Artificial Intelligence based intelligent optimization technique. Additionally, some alternative intelligent optimization algorithms were also used for determining optimum values. According to the results of the study, the carob bean can be used as an alternative adsorbent and the found optimum values can be employed for that purpose.

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Authors and Affiliations

  1. Usak University, Usak, Turkey

    Bahdisen Gezer

  2. Department of Computer Engineering, Faculty of Engineering, Suleyman Demirel University, E9 Block, Z-23, 32260, Isparta, Turkey

    Utku Kose

  3. University of Information Science and Technology St. Paul the Apostle, Ohrid, Macedonia

    Dmytro Zubov

  4. Afyon Kocatepe University, Afyonkarahisar, Turkey

    Omer Deperlioglu

  5. Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia

    Pandian Vasant

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  1. Bahdisen Gezer
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Gezer, B., Kose, U., Zubov, D. et al. Determining optimum carob powder adsorbtion for cleaning wastewater: intelligent optimization with electro-search algorithm. Wireless Netw 26, 5665–5679 (2020). https://doi.org/10.1007/s11276-019-02035-1

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