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Yapay Sinir Ağı Yaklaşımı ile Crystal Violet Katyonik Boyarmaddesinin Biyokütle-temelli Grafen Benzeri Gözenekli Karbon Üzerine Biyosorpsiyonunun Tahmin Edilmesi

Year 2021, Issue: 23, 456 - 464, 30.04.2021
https://doi.org/10.31590/ejosat.878772

Abstract

Tekstil endüstrisi su kirliliğinde ana aktörler olarak kabul edilmektedir. Biyosorbentlerin / adsorbanların tekstil boyası soğurma kapasitelerinin tahmini, tasarım konuları olarak çok önemlidir. Bu çalışmada, sulu çözeltiden Crystal Violet (CV) katyonik boyarmaddesinin uzaklaştırılması için düşük maliyetli bir biyosorbent olarak portakal kabuğu türevi grafen benzeri gözenekli karbonun (GCs) kullanılmasının fizibilitesi hem kesikli biyosorpsiyon deney düzeneği ile hem de yapay bir sinir ağı (YSA) yaklaşımı kullanılarak değerlendirilmiştir. Fizikokimyasal karakterizasyon sonuçları, sentezlenen GCs'nin 985 m2.g-1 özgül yüzey alanına, 1.04 cm3.g-1 gözenek hacmine ve 6.50 sıfır yük noktasına (pHPZC) sahip olduğunu ortaya çıkarmıştır. Biyosorbentin biyosorpsiyon kapasitesi, başlangıç pH’ı, bisorbent dozu, başlangıç boya konsantrasyonu ve sıcaklığın fonksiyonu olarak araştırılmıştır. En yüksek biyosorpsiyon performans değerleri pH 7.5, biyosorbent dozajı 1,0 g.L-1, 25 ° C sıcaklıkta elde edilmiştir ve burada başlangıçtaki CV'nin% 92'si başarıyla uzaklaştırılmıştır. Deneysel sonuçlar, biyosorpsiyon işleminin önemli ölçüde sıcaklığa bağlı olduğunu, ancak yaklaşık 15 dakikalık temas süresinin dengeye ulaşmak için yeterli olduğunu göstermiştir. GPC'nin biyosorpsiyon performansını tahmin etmek için YSA yaklaşımı kullanılmıştır. Önerilen YSA modeli, sırasıyla gizli katmanda ve çıktı katmanlarında purelin ve tansig fonksiyonlarının aktivasyon fonksiyonu kullanılarak, Levengberg-Marquardt geri yayılım algoritması ile eğitilmiştir. YSA modelini optimize etmek için farklı gizli topolojiler değerlendirilmiştir. Yüksek performanslı parametrelerle (doğrusal korelasyon katsayısı, R = 0.9995; ortalama kare hatası, MSE = 0.0004) CV'nin biyosorpsiyonunu tahmin etmek için 5 ve 10 nöronlu iki gizli katman ile yapılandırılmış optimal bir YSA modeli geliştirilmiştir. Bu çalışma, deneysel verilerin YSA temelli verilerle uyumlu olduğunu ortaya koymuştur, bu nedenle önerilen YSA yaklaşımının katyonik boya biyosorpsiyonunu tahmin etmek için kullanılabileceği söylenebilir.

References

  • Abid, M., Niazi, N. K., Bibi, I., Farooqi, A., Ok, Y. S., Kunhikrishnan, A., ... & Arshad, M. (2016). Arsenic (V) biosorption by charred orange peel in aqueous environments. International journal of phytoremediation, 18(5), 442-449.
  • Das, S. K., Das, A. R., & Guha, A. K. (2007). A study on the adsorption mechanism of mercury on Aspergillus versicolor biomass. Environmental science & technology, 41(24), 8281-8287.
  • Dehghani, M. H., Yetilmezsoy, K., Salari, M., Heidarinejad, Z., Yousefi, M., & Sillanpää, M. (2020). Adsorptive removal of cobalt (II) from aqueous solutions using multi-walled carbon nanotubes and γ-alumina as novel adsorbents: Modelling and optimization based on response surface methodology and artificial neural network. Journal of Molecular Liquids, 299, 112154.
  • Elemen, S., Kumbasar, E. P. A., & Yapar, S. (2012). Modeling the adsorption of textile dye on organoclay using an artificial neural network. Dyes and Pigments, 95(1), 102-111. Fawzy, M., Nasr, M., Nagy, H., & Helmi, S. (2018). Artificial intelligence and regression analysis for Cd (II) ion biosorption from aqueous solution by Gossypium barbadense waste. Environmental Science and Pollution Research, 25(6), 5875-5888.
  • Ghaedi, M., Ghaedi, A. M., Abdi, F., Roosta, M., Sahraei, R., & Daneshfar, A. (2014). Principal component analysis-artificial neural network and genetic algorithm optimization for removal of reactive orange 12 by copper sulfide nanoparticles-activated carbon. Journal of Industrial and Engineering Chemistry, 20(3), 787-795.
  • Karaman, C., Aktas, Z., Bayram, E., Karaman, O., & Kızıl, Ç. (2020). Correlation Between the Molecular Structure of Reducing Agent and pH of Graphene Oxide Dispersion On the Formation of 3D-Graphene Networks. ECS Journal of Solid State Science and Technology, 9(7), 071003.
  • Karaman, C., Aksu, Z. (2020). Modelling of Remazol Black-B adsorption on chemically modified waste orange peel: pH shifting effect of acidic treatment. Sakarya University Journal of Science, 24(5), 1127-1142.
  • Karaman, C. (2020). Modeling of Biosorption of Arsenic (V) On Waste Orange Peel Derived Graphene-Like Porous Carbon by Artificial Neural Network Approach. European Journal of Science and Technology, 91-100.
  • Kodal, Süheyla Pınar, and Zümriye Aksu. "Cationic surfactant-modified biosorption of anionic dyes by dried Rhizopus arrhizus." Environmental technology 38, no. 20 (2017): 2551-2561.
  • Liang, S., Guo, X., Feng, N., & Tian, Q. (2009). Application of orange peel xanthate for the adsorption of Pb2+ from aqueous solutions. Journal of Hazardous Materials, 170(1), 425-429.
  • Lu, D., Cao, Q., Li, X., Cao, X., Luo, F., & Shao, W. (2009). Kinetics and equilibrium of Cu (II) adsorption onto chemically modified orange peel cellulose biosorbents. Hydrometallurgy, 95(1-2), 145-152.
  • Pathak, P. D., Mandavgane, S. A., & Kulkarni, B. D. (2016). Characterizing fruit and vegetable peels as bioadsorbents. Current Science, 2114-2123.
  • Pauletto, P. S., Dotto, G. L., & Salau, N. P. (2020). Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption. Journal of Molecular Liquids, 320, 114418.
  • Rahaman, M. S., Basu, A., & Islam, M. R. (2008). The removal of As (III) and As (V) from aqueous solutions by waste materials. Bioresource technology, 99(8), 2815-2823. Su, T., Guan, X., Tang, Y., Gu, G., & Wang, J. (2010). Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach. Journal of hazardous materials, 176(1-3), 466-472.

Forecasting The Biosorption of Crystal Violet Cationic Dye onto Biomass-driven Graphene-Like Porous Carbon Through Artificial Neural Network Approach

Year 2021, Issue: 23, 456 - 464, 30.04.2021
https://doi.org/10.31590/ejosat.878772

Abstract

Textile industries are considered to be the main actors in water pollution. Estimation of the textile dye sorption capacities of the biosorbents/adsorbents are crucial as design considerations. Herein, the feasibility of utilizing orange-peel-derived graphene-like porous carbons (GCs) as a low-cost biosorbent for removal of Crystal Violet (CV) cationic dye from aqueous solution have been evaluated both by batch biosorption experimental-setup and by using an artificial neural network (ANN) approach. The physicochemical characterization results have indicated that as-synthesized GCs has a specific surface area of 985 m2.g-1, a pore volume of 1.04 cm3.g-1, and a point of zero charge (pHPZC) of 6.50. The biosorption capacity of the biosorbent has been investigated as a function of initial pH, bisorbent dosage, initial dye concentration, and temperature. The optimal biosorption performance values have been achieved at pH of 7.5, the biosorbent dosage of 3.0 g.L-1 , the temperature of 25 ℃, in which 91.6% of initial CV (initial dye concentration of 100 ppm) has been successfully removed. The experimental results have indicated that the biosorption process significantly depends on the temperature whereas ca.15 min of contact time is sufficient for reaching equilibrium. The ANN approach has been utilized to forecast the biosorption performance of GPC. The proposed ANN model has been trained by the Levengberg-Marquardt backpropagation algorithm, by using the activation function of purelin and tansig functions at hidden and output layers, respectively. Different hidden topologies have been evaluated to optimize the ANN model. An optimal ANN model structured with two hidden layers with 5 and 10 neurons in each layer has been developed to forecast the biosorption of CV with high-performance parameters (linear correlation coefficient, R= 0.9995; mean squared error, MSE=0.0004). This work has shown that the experimental data are in harmony with ANN-based data, so it can be speculated that the proposed ANN approach can be utilized for predicting the cationic dye biosorption.

References

  • Abid, M., Niazi, N. K., Bibi, I., Farooqi, A., Ok, Y. S., Kunhikrishnan, A., ... & Arshad, M. (2016). Arsenic (V) biosorption by charred orange peel in aqueous environments. International journal of phytoremediation, 18(5), 442-449.
  • Das, S. K., Das, A. R., & Guha, A. K. (2007). A study on the adsorption mechanism of mercury on Aspergillus versicolor biomass. Environmental science & technology, 41(24), 8281-8287.
  • Dehghani, M. H., Yetilmezsoy, K., Salari, M., Heidarinejad, Z., Yousefi, M., & Sillanpää, M. (2020). Adsorptive removal of cobalt (II) from aqueous solutions using multi-walled carbon nanotubes and γ-alumina as novel adsorbents: Modelling and optimization based on response surface methodology and artificial neural network. Journal of Molecular Liquids, 299, 112154.
  • Elemen, S., Kumbasar, E. P. A., & Yapar, S. (2012). Modeling the adsorption of textile dye on organoclay using an artificial neural network. Dyes and Pigments, 95(1), 102-111. Fawzy, M., Nasr, M., Nagy, H., & Helmi, S. (2018). Artificial intelligence and regression analysis for Cd (II) ion biosorption from aqueous solution by Gossypium barbadense waste. Environmental Science and Pollution Research, 25(6), 5875-5888.
  • Ghaedi, M., Ghaedi, A. M., Abdi, F., Roosta, M., Sahraei, R., & Daneshfar, A. (2014). Principal component analysis-artificial neural network and genetic algorithm optimization for removal of reactive orange 12 by copper sulfide nanoparticles-activated carbon. Journal of Industrial and Engineering Chemistry, 20(3), 787-795.
  • Karaman, C., Aktas, Z., Bayram, E., Karaman, O., & Kızıl, Ç. (2020). Correlation Between the Molecular Structure of Reducing Agent and pH of Graphene Oxide Dispersion On the Formation of 3D-Graphene Networks. ECS Journal of Solid State Science and Technology, 9(7), 071003.
  • Karaman, C., Aksu, Z. (2020). Modelling of Remazol Black-B adsorption on chemically modified waste orange peel: pH shifting effect of acidic treatment. Sakarya University Journal of Science, 24(5), 1127-1142.
  • Karaman, C. (2020). Modeling of Biosorption of Arsenic (V) On Waste Orange Peel Derived Graphene-Like Porous Carbon by Artificial Neural Network Approach. European Journal of Science and Technology, 91-100.
  • Kodal, Süheyla Pınar, and Zümriye Aksu. "Cationic surfactant-modified biosorption of anionic dyes by dried Rhizopus arrhizus." Environmental technology 38, no. 20 (2017): 2551-2561.
  • Liang, S., Guo, X., Feng, N., & Tian, Q. (2009). Application of orange peel xanthate for the adsorption of Pb2+ from aqueous solutions. Journal of Hazardous Materials, 170(1), 425-429.
  • Lu, D., Cao, Q., Li, X., Cao, X., Luo, F., & Shao, W. (2009). Kinetics and equilibrium of Cu (II) adsorption onto chemically modified orange peel cellulose biosorbents. Hydrometallurgy, 95(1-2), 145-152.
  • Pathak, P. D., Mandavgane, S. A., & Kulkarni, B. D. (2016). Characterizing fruit and vegetable peels as bioadsorbents. Current Science, 2114-2123.
  • Pauletto, P. S., Dotto, G. L., & Salau, N. P. (2020). Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption. Journal of Molecular Liquids, 320, 114418.
  • Rahaman, M. S., Basu, A., & Islam, M. R. (2008). The removal of As (III) and As (V) from aqueous solutions by waste materials. Bioresource technology, 99(8), 2815-2823. Su, T., Guan, X., Tang, Y., Gu, G., & Wang, J. (2010). Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach. Journal of hazardous materials, 176(1-3), 466-472.
There are 14 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ceren Karaman 0000-0001-9148-7253

Publication Date April 30, 2021
Published in Issue Year 2021 Issue: 23

Cite

APA Karaman, C. (2021). Forecasting The Biosorption of Crystal Violet Cationic Dye onto Biomass-driven Graphene-Like Porous Carbon Through Artificial Neural Network Approach. Avrupa Bilim Ve Teknoloji Dergisi(23), 456-464. https://doi.org/10.31590/ejosat.878772