Araştırma Makalesi
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Yıl 2020, Cilt: 12 Sayı: 1, 13 - 20, 31.01.2020

Abdullah Erdal Tümer

https://doi.org/10.29137/umagd.472269
Cited By: 1

Öz

Kaynakça

  • W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5 (1943) 115–133. doi:10.1007/BF02478259.
  • T. Köhler, R. Bock, J. Hornegger, G. Michelson, Computer-Aided Diagnostics and Pattern Recognition: Automated Glaucoma Detection, in: Teleophthalmology Prev. Med., Springer Berlin Heidelberg, Berlin, Heidelberg, 2015: pp. 93–104. doi:10.1007/978-3-662-44975-2_9.
  • H. Gao, L. Liang, X. Chen, G. Xu, Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization, Chinese J. Mech. Eng. 28 (2015) 96–105. doi:10.3901/CJME.2014.1103.166.
  • A. Azarpour, S.R. Wan Alwi, G. Zahedi, M. Madooli, G.J. Millar, Catalytic activity evaluation of industrial Pd/C catalyst via gray-box dynamic modeling and simulation of hydropurification reactor, Appl. Catal. A Gen. 489 (2015) 262–271. doi:10.1016/j.apcata.2014.10.048.
  • S. Lertworasirikul, Y. Tipsuwan, Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network, J. Food Eng. 84 (2008) 65–74. doi:10.1016/j.jfoodeng.2007.04.019.
  • I. Chairez, I. García-Peña, A. Cabrera, Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network, J. Process Control. 19 (2009) 1103–1110. doi:10.1016/j.jprocont.2008.12.009.
  • N. Bounar, A. Boulkroune, F. Boudjema, M. M’Saad, M. Farza, Adaptive fuzzy vector control for a doubly-fed induction motor, Neurocomputing. 151 (2015) 756–769. doi:10.1016/j.neucom.2014.10.026.
  • W. Li, Z. Zhu, F. Jiang, G. Zhou, G. Chen, Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method, Mech. Syst. Signal Process. 50–51 (2015) 414–426. doi:10.1016/j.ymssp.2014.05.034.
  • C. Voyant, M.L. Nivet, C. Paoli, M. Muselli, G. Notton, Heterogeneous transfer functions multi-layer perceptron (MLP) for meteorological time series forecasting, Int. J. Model. Simulation, Sci. Comput. 6 (2015). doi:10.1142/S1793962315500130.
  • R. Todoran, D. Todoran, Z. Szakács, Optical luminescence studies of the ethyl xanthate adsorption layer on the surface of sphalerite minerals, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 152 (2016) 591–595. doi:10.1016/j.saa.2015.01.001.
  • A.M. Ghaedi, M. Ghaedi, P. Karami, Comparison of ultrasonic with stirrer performance for removal of sunset yellow (SY) by activated carbon prepared from wood of orange tree: Artificial neural network modeling, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 138 (2015) 789–799. doi:10.1016/j.saa.2014.11.019.
  • A.R. Amani-Ghadim, M.S.S. Dorraji, Modeling of photocatalyatic process on synthesized ZnO nanoparticles: Kinetic model development and artificial neural networks, Appl. Catal. B Environ. 163 (2015) 539–546. doi:10.1016/j.apcatb.2014.08.020.
  • G. Halder, S. Dhawane, P.K. Barai, A. Das, Optimizing chromium (VI) adsorption onto superheated steam activated granular carbon through response surface methodology and artificial neural network, Environ. Prog. Sustain. Energy. 34 (2015) 638–647. doi:10.1002/ep.12028.
  • E. Tomczak, Application of ANN and EA for description of metal ions sorption on chitosan foamed structure-Equilibrium and dynamics of packed column, Comput. Chem. Eng. 35 (2011) 226–235. doi:10.1016/j.compchemeng.2010.05.012.
  • S. Dutta, S. Parsons, C. Bhattacharjee, Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO 2 surface, Expert Syst. with. (2010). http://www.sciencedirect.com/science/article/pii/S0957417410005944 (accessed December 22, 2016).
  • Aber, A. Amani-Ghadim, V. Mirzajani, Removal of Cr (VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network, J. Hazard. Mater. (2009). http://www.sciencedirect.com/science/article/pii/S0304389409009406 (accessed December 22, 2016).
  • A. Sato, Z. Sha, S. Palosaari, Neural networks for chemical engineering unit operations, Chem. Eng. (1999). http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1521-4125(199909)22:9%3C732::AID-CEAT732%3E3.0.CO;2-1/full (accessed December 22, 2016).
  • P. Nayak, Y. Rao, K. Sudheer, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour. Manag. (2006). http://link.springer.com/article/10.1007/s11269-006-4007-z (accessed December 22, 2016).
  • R. Khandanlou, H. Masoumi, M. Ahmad, Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe 3 O 4 nanoparticles using artificial neural network (ANN), Ecological. (2016). http://www.sciencedirect.com/science/article/pii/S0925857416301616 (accessed December 22, 2016).
  • U. Yurtsever, M. Yurtsever, İ. Şengil, Fast artificial neural network (FANN) modeling of Cd (II) ions removal by valonia resin, Water Treat. (2015). http://www.tandfonline.com/doi/abs/10.1080/19443994.2014.937756 (accessed December 22, 2016).
  • N. Mahmoodi, Dye adsorption from single and binary systems using NiO‐MnO2 nanocomposite and artificial neural network modeling, Environmental. (2016). http://onlinelibrary.wiley.com/doi/10.1002/ep.12452/full (accessed December 22, 2016).
  • A. Babaei, A. Khataee, E. Ahmadpour, Optimization of cationic dye adsorption on activated spent tea: Equilibrium, kinetics, thermodynamic and artificial neural network modeling, Korean J. (2016). http://link.springer.com/article/10.1007/s11814-014-0334-6 (accessed December 22, 2016).
  • M. Kooh, M. Dahri, L. Lim, L. Lim, Batch adsorption studies of the removal of methyl violet 2B by soya bean waste: isotherm, kinetics and artificial neural network modelling, Environ. Earth. (2016). http://link.springer.com/article/10.1007/s12665-016-5582-9 (accessed December 22, 2016).
  • N. Mahmoodi, Z. Hosseinabadi-Farahani, Synthesis of CuO–NiO nanocomposite and dye adsorption modeling using artificial neural network, Water Treat. (2016). http://www.tandfonline.com/doi/abs/10.1080/19443994.2015.1086895 (accessed December 22, 2016).
  • B. Heibati, S. Rodriguez-Couto, A modeling study by artificial neural network on ethidium bromide adsorption optimization using natural pumice and iron-coated pumice, Water Treat. (2016). http://www.tandfonline.com/doi/abs/10.1080/19443994.2015.1060906 (accessed December 22, 2016).
  • F. Fu, Q. Wang, Removal of heavy metal ions from wastewaters: a review, J. Environ. Manage. (2011). http://www.sciencedirect.com/science/article/pii/S0301479710004147 (accessed December 22, 2016).
  • D. Zhao, A. SenGupta, L. Stewart, Selective removal of Cr (VI) oxyanions with a new anion exchanger, Ind. Eng. (1998). http://pubs.acs.org/doi/abs/10.1021/ie980227r (accessed December 22, 2016).
  • A. Sengupta, D. Clifford, Important process variables in chromate ion exchange, Environ. Sci. Technol. (1986). http://pubs.acs.org/doi/abs/10.1021/es00144a006 (accessed December 22, 2016).
  • M. Asfari, V. Böhmer, J. Harrowfield, J. Vicens, Calixarenes 2001, (2007). https://www.google.com/books?hl=tr&lr=&id=M_fZBwAAQBAJ&oi=fnd&pg=PP8&dq=Calixarenes+2001&ots=dVUdxSFD1W&sig=k2tRhxNKOK_ajdUwHZtxZLtBo0M (accessed December 22, 2016).
  • C. Gutsche, Calixarenes: an introduction, (2008). https://www.google.com/books?hl=tr&lr=&id=Hi3YFJ2lcCgC&oi=fnd&pg=PA1&dq=Calixarenes:+an+introduction+2008&ots=X-utTwmczA&sig=fOaY5ozxFhYX952QOwuqdsb7Utg (accessed December 22, 2016).
  • H. Kang, C. Yoon, Neural network approaches to aid simple truss design problems, Comput. Civ. Infrastruct. (1994). http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8667.1994.tb00374.x/abstract (accessed December 22, 2016).
  • Heaton, Introduction to neural networks with Java, (2008). https://www.google.com/books?hl=tr&lr=&id=Swlcw7M4uD8C&oi=fnd&pg=PR35&dq=Introduction+to+Neural+Networks+with+Java&ots=TJB5Q39Xw7&sig=bWPN9b8SGlnpY2gMN60RNZ6eDt0 (accessed December 22, 2016).
  • R. Fu, T. Xu, Z. Pan, Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network, J. Memb. Sci. (2005). http://www.sciencedirect.com/science/article/pii/S0376738804007525 (accessed December 22, 2016).
  • D. Rumelhart, G. Hinton, R. Williams, Learning internal representations by error propagation, (1985). http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA164453 (accessed December 22, 2016).
  • J. Self, Artificial intelligence and human learning, (1988). http://tocs.ulb.tu-darmstadt.de/8608482.pdf (accessed December 22, 2016).
  • M. Tabakci, Immobilization of calix arene bearing carboxylic acid and amide groups on aminopropyl silica gel and its sorption properties for Cr (VI), J. Incl. Phenom. Macrocycl. (2008). http://link.springer.com/article/10.1007/s10847-007-9392-2 (accessed December 22, 2016).

Artificial Neural Network Modeling of The Removal of Cr (VI) on by Polymeric Calix[6]arene in aqueous solutions

Yıl 2020, Cilt: 12 Sayı: 1, 13 - 20, 31.01.2020

Abdullah Erdal Tümer

https://doi.org/10.29137/umagd.472269
Cited By: 1

Öz

The artificial neural network-based model was
developed to predict the sorption capacity and removal efficiency of calixarene
for Cr(VI) in aqueous solutions. The input variables were initial concentration
of Cr(VI), adsorbent dosage, contact time, and pH, while the sorption capacity
and the removal efficiency were considered as output. They have been used for
the training and simulation of the network in the current work. The training
results were tested using the input data (simulated data) that were not shown
to the network. According to the indicator, the optimum and most reliable model
was found based on the test results.

Anahtar Kelimeler

Artificial Neural Network Modeling Sorption Removal Efficiency Sorption Capacity

Kaynakça

  • W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5 (1943) 115–133. doi:10.1007/BF02478259.
  • T. Köhler, R. Bock, J. Hornegger, G. Michelson, Computer-Aided Diagnostics and Pattern Recognition: Automated Glaucoma Detection, in: Teleophthalmology Prev. Med., Springer Berlin Heidelberg, Berlin, Heidelberg, 2015: pp. 93–104. doi:10.1007/978-3-662-44975-2_9.
  • H. Gao, L. Liang, X. Chen, G. Xu, Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization, Chinese J. Mech. Eng. 28 (2015) 96–105. doi:10.3901/CJME.2014.1103.166.
  • A. Azarpour, S.R. Wan Alwi, G. Zahedi, M. Madooli, G.J. Millar, Catalytic activity evaluation of industrial Pd/C catalyst via gray-box dynamic modeling and simulation of hydropurification reactor, Appl. Catal. A Gen. 489 (2015) 262–271. doi:10.1016/j.apcata.2014.10.048.
  • S. Lertworasirikul, Y. Tipsuwan, Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network, J. Food Eng. 84 (2008) 65–74. doi:10.1016/j.jfoodeng.2007.04.019.
  • I. Chairez, I. García-Peña, A. Cabrera, Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network, J. Process Control. 19 (2009) 1103–1110. doi:10.1016/j.jprocont.2008.12.009.
  • N. Bounar, A. Boulkroune, F. Boudjema, M. M’Saad, M. Farza, Adaptive fuzzy vector control for a doubly-fed induction motor, Neurocomputing. 151 (2015) 756–769. doi:10.1016/j.neucom.2014.10.026.
  • W. Li, Z. Zhu, F. Jiang, G. Zhou, G. Chen, Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method, Mech. Syst. Signal Process. 50–51 (2015) 414–426. doi:10.1016/j.ymssp.2014.05.034.
  • C. Voyant, M.L. Nivet, C. Paoli, M. Muselli, G. Notton, Heterogeneous transfer functions multi-layer perceptron (MLP) for meteorological time series forecasting, Int. J. Model. Simulation, Sci. Comput. 6 (2015). doi:10.1142/S1793962315500130.
  • R. Todoran, D. Todoran, Z. Szakács, Optical luminescence studies of the ethyl xanthate adsorption layer on the surface of sphalerite minerals, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 152 (2016) 591–595. doi:10.1016/j.saa.2015.01.001.
  • A.M. Ghaedi, M. Ghaedi, P. Karami, Comparison of ultrasonic with stirrer performance for removal of sunset yellow (SY) by activated carbon prepared from wood of orange tree: Artificial neural network modeling, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 138 (2015) 789–799. doi:10.1016/j.saa.2014.11.019.
  • A.R. Amani-Ghadim, M.S.S. Dorraji, Modeling of photocatalyatic process on synthesized ZnO nanoparticles: Kinetic model development and artificial neural networks, Appl. Catal. B Environ. 163 (2015) 539–546. doi:10.1016/j.apcatb.2014.08.020.
  • G. Halder, S. Dhawane, P.K. Barai, A. Das, Optimizing chromium (VI) adsorption onto superheated steam activated granular carbon through response surface methodology and artificial neural network, Environ. Prog. Sustain. Energy. 34 (2015) 638–647. doi:10.1002/ep.12028.
  • E. Tomczak, Application of ANN and EA for description of metal ions sorption on chitosan foamed structure-Equilibrium and dynamics of packed column, Comput. Chem. Eng. 35 (2011) 226–235. doi:10.1016/j.compchemeng.2010.05.012.
  • S. Dutta, S. Parsons, C. Bhattacharjee, Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO 2 surface, Expert Syst. with. (2010). http://www.sciencedirect.com/science/article/pii/S0957417410005944 (accessed December 22, 2016).
  • Aber, A. Amani-Ghadim, V. Mirzajani, Removal of Cr (VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network, J. Hazard. Mater. (2009). http://www.sciencedirect.com/science/article/pii/S0304389409009406 (accessed December 22, 2016).
  • A. Sato, Z. Sha, S. Palosaari, Neural networks for chemical engineering unit operations, Chem. Eng. (1999). http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1521-4125(199909)22:9%3C732::AID-CEAT732%3E3.0.CO;2-1/full (accessed December 22, 2016).
  • P. Nayak, Y. Rao, K. Sudheer, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour. Manag. (2006). http://link.springer.com/article/10.1007/s11269-006-4007-z (accessed December 22, 2016).
  • R. Khandanlou, H. Masoumi, M. Ahmad, Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe 3 O 4 nanoparticles using artificial neural network (ANN), Ecological. (2016). http://www.sciencedirect.com/science/article/pii/S0925857416301616 (accessed December 22, 2016).
  • U. Yurtsever, M. Yurtsever, İ. Şengil, Fast artificial neural network (FANN) modeling of Cd (II) ions removal by valonia resin, Water Treat. (2015). http://www.tandfonline.com/doi/abs/10.1080/19443994.2014.937756 (accessed December 22, 2016).
  • N. Mahmoodi, Dye adsorption from single and binary systems using NiO‐MnO2 nanocomposite and artificial neural network modeling, Environmental. (2016). http://onlinelibrary.wiley.com/doi/10.1002/ep.12452/full (accessed December 22, 2016).
  • A. Babaei, A. Khataee, E. Ahmadpour, Optimization of cationic dye adsorption on activated spent tea: Equilibrium, kinetics, thermodynamic and artificial neural network modeling, Korean J. (2016). http://link.springer.com/article/10.1007/s11814-014-0334-6 (accessed December 22, 2016).
  • M. Kooh, M. Dahri, L. Lim, L. Lim, Batch adsorption studies of the removal of methyl violet 2B by soya bean waste: isotherm, kinetics and artificial neural network modelling, Environ. Earth. (2016). http://link.springer.com/article/10.1007/s12665-016-5582-9 (accessed December 22, 2016).
  • N. Mahmoodi, Z. Hosseinabadi-Farahani, Synthesis of CuO–NiO nanocomposite and dye adsorption modeling using artificial neural network, Water Treat. (2016). http://www.tandfonline.com/doi/abs/10.1080/19443994.2015.1086895 (accessed December 22, 2016).
  • B. Heibati, S. Rodriguez-Couto, A modeling study by artificial neural network on ethidium bromide adsorption optimization using natural pumice and iron-coated pumice, Water Treat. (2016). http://www.tandfonline.com/doi/abs/10.1080/19443994.2015.1060906 (accessed December 22, 2016).
  • F. Fu, Q. Wang, Removal of heavy metal ions from wastewaters: a review, J. Environ. Manage. (2011). http://www.sciencedirect.com/science/article/pii/S0301479710004147 (accessed December 22, 2016).
  • D. Zhao, A. SenGupta, L. Stewart, Selective removal of Cr (VI) oxyanions with a new anion exchanger, Ind. Eng. (1998). http://pubs.acs.org/doi/abs/10.1021/ie980227r (accessed December 22, 2016).
  • A. Sengupta, D. Clifford, Important process variables in chromate ion exchange, Environ. Sci. Technol. (1986). http://pubs.acs.org/doi/abs/10.1021/es00144a006 (accessed December 22, 2016).
  • M. Asfari, V. Böhmer, J. Harrowfield, J. Vicens, Calixarenes 2001, (2007). https://www.google.com/books?hl=tr&lr=&id=M_fZBwAAQBAJ&oi=fnd&pg=PP8&dq=Calixarenes+2001&ots=dVUdxSFD1W&sig=k2tRhxNKOK_ajdUwHZtxZLtBo0M (accessed December 22, 2016).
  • C. Gutsche, Calixarenes: an introduction, (2008). https://www.google.com/books?hl=tr&lr=&id=Hi3YFJ2lcCgC&oi=fnd&pg=PA1&dq=Calixarenes:+an+introduction+2008&ots=X-utTwmczA&sig=fOaY5ozxFhYX952QOwuqdsb7Utg (accessed December 22, 2016).
  • H. Kang, C. Yoon, Neural network approaches to aid simple truss design problems, Comput. Civ. Infrastruct. (1994). http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8667.1994.tb00374.x/abstract (accessed December 22, 2016).
  • Heaton, Introduction to neural networks with Java, (2008). https://www.google.com/books?hl=tr&lr=&id=Swlcw7M4uD8C&oi=fnd&pg=PR35&dq=Introduction+to+Neural+Networks+with+Java&ots=TJB5Q39Xw7&sig=bWPN9b8SGlnpY2gMN60RNZ6eDt0 (accessed December 22, 2016).
  • R. Fu, T. Xu, Z. Pan, Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network, J. Memb. Sci. (2005). http://www.sciencedirect.com/science/article/pii/S0376738804007525 (accessed December 22, 2016).
  • D. Rumelhart, G. Hinton, R. Williams, Learning internal representations by error propagation, (1985). http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA164453 (accessed December 22, 2016).
  • J. Self, Artificial intelligence and human learning, (1988). http://tocs.ulb.tu-darmstadt.de/8608482.pdf (accessed December 22, 2016).
  • M. Tabakci, Immobilization of calix arene bearing carboxylic acid and amide groups on aminopropyl silica gel and its sorption properties for Cr (VI), J. Incl. Phenom. Macrocycl. (2008). http://link.springer.com/article/10.1007/s10847-007-9392-2 (accessed December 22, 2016).
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Abdullah Erdal Tümer 0000-0001-7747-9441

Yayımlanma Tarihi 31 Ocak 2020
Gönderilme Tarihi 19 Ekim 2018
Yayımlandığı Sayı Yıl 2020 Cilt: 12 Sayı: 1

Kaynak Göster

APA Tümer, A. E. (2020). Artificial Neural Network Modeling of The Removal of Cr (VI) on by Polymeric Calix[6]arene in aqueous solutions. International Journal of Engineering Research and Development, 12(1), 13-20. https://doi.org/10.29137/umagd.472269

Cited By

A Review of the Chemical Extraction of Chitosan from Shrimp Wastes and Prediction of Factors Affecting Chitosan Yield by Using an Artificial Neural Network
Marine Drugs
https://doi.org/10.3390/md20110675
 Kapak Resmi İndir

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