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Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi

Yıl 2019, Cilt: 34 Sayı: 4, 2033 - 2044, 25.06.2019
https://doi.org/10.17341/gazimmfd.418184

Öz

Bu çalışmada,
tekstil endüstrisi atıksularının giderimi için Türkiye’ye katma değer
sağlayacak pratik bir yaklaşım hedeflenmiştir. Bu bağlamda, ülkemizin dünya
birincisi olduğu fındık üreticiliği göz önüne alınarak zararsız bir atık olan
fındık kabuğu, laboratuvar ortamında hazırlanmış sentetik atık suların
gideriminde değerlendirilmiştir. FTIR, SEM-EDS ve XRD analizleri ile
gerçekleştirilen karakterizasyon çalşmaları, FK’nin organik bağ yapısı, morfolojik
yapısı ve elementel içeriği ortaya konulmuştur. Çalışmanın devamında kısmında
farklı tekstil model boyalarla hazırlanan sentetik çözeltilerin farklı işletme
şartlarında FK ile giderimi incelenmiştir. Bu bağlamda; başlangıç pH’ı,
başlangıç boya konsantrasyonu, adsorbent konsantrasyonu, reaksiyon süresi ve
sıcaklığın etkileri incelenmiştir. Kinetik analizler, adsorpsiyonun sözde
ikinci mertebe model ve partiküler arası difüzyonun kontrolünde gerçekleştiğini
göstermiştir. Denge çalışmaları, Langmuir izoterminin süreci daha iyi ifade
ettiğini göstermiştir. Termodinamik parametreler ise, sürecin endotermik
olduğunu, kendiliğinden gerçekleştiğini ve sıcaklıla artan bir affiniteye sahip
olduğunu göstermiştir.

Kaynakça

  • Buyukada M., Removal of yellow f3r, di maria brilliant blue r and reactive brilliant red-3me from aqueous solutions by a rapid and efficient ultrasound–assisted process with a novel biosorbent of cottonseed cake: Statistical modeling, kinetic and thermodynamic studies, Arab. J. Sci. Eng., 40(8), 2153–2168, 2015.
  • Rafatullah M., Sulaiman O., Hashim R., Ahmad A., Adsorption of methylene blue on low-cost adsorbents: a review, J. Hazard. Mater. 177 (2010) 70–80.
  • A, Celekli, S.S. Bilecikligil, F. Geyik, H. Bozkurt, Prediction of removal efficiency Lanaset Red G on walnut husk using artificial neural network model, Bioresour. Technol., 103, 64–70, 2012.
  • Srinivasan A., Viraraghavan T., Decolorization of dye wastewaters by biosorbents: a review, J. Environ. Manage., 91, 1915–1929, 2010.
  • Celekli A., Geyik F., Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G Chara contraria, Bioresour. Technol., 102, 5634–5638, 2011.
  • Buyukada M., Evrendilek F., Modeling Efficiency of Dehydrated Sunflower Seed Cake as a Novel Biosorbent to Remove a Toxic Azo Dye, Chem. Eng. Commun., 203(6), 746–757, 2016.
  • Uzuner S., Cekmecelioglu D., Enhanced pectinase production by optimizing fermentation conditions of Bacillus subtilis growing on hazelnut shell hydrolyzate, J. Mol. Catal. B Enzym., 113, 62–67, 2015.
  • Ozkal S.G., Yener, M.E., Supercritical carbon dioxide extraction of flaxseed oil: effect of extraction parameters and mass transfer modeling, J. Supercrit. Fluids, 112, 76–80, 2016.
  • Buyukada M., Co-combustion of peanut hull and coal blends: Artificial neural networks, particle swarm optimization and Monte Carlo simulation, Bioresour. Technol., 216, 280–286, 2016.
  • Celekli A., Bozkurt H., Geyik F., Use of artificial neural networks and genetic algorithm for prediction of sorption of an azo-metal complex dye onto lentil straw, Bioresour. Technol., 129, 396–401, 2013.
  • Gajic D., Savic-Gajic I., Savic I., Georgieva O., Gennaro S., Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks, Energy, 108(1), 132–139, 2016.
  • Evrendilek G.A., Avsar Y.K., Evrendilek F., Modeling stochastic variability and uncertainty in aroma active compounds of PEF-treated peach nectar as a function of physical and sensory properties, and treatment time, Food Chem., 190, 634–642, 2016.
  • Buyukada M., Probabilistic uncertainty analysis based on Monte Carlo simulation of co-combustion of hazelnut hull and coal belnds: Data-driven modeling and response surface optimization, Bioresour. Technol., 225, 106–112, 2017.
  • Buyukada M., Modeling of decolorization of synthetic reactive dyestuff solutions with response surface methodology by a rapid and efficient process of ultrasound–assisted ozone oxidation, Des. Wat. Treat., 57(32), 14973–14985, 2016.
  • Kumar K.V., Porkodi K., Modelling the solid–liquid adsorption processes using artificial neural networks rained by pseudo second order kinetics, Chem. Eng. J., 148, 20–25, 2009.
  • Yao Y.J., Xu F.F., Chen M., Xu Z.X., Zhu Z.W., Adsorption behavior of methylene blue on carbon nanotubes, Bioresour. Technol., 101, 3040–3046, 2010.
  • Yang G., Wang B., Wang Z., Li X., Jia Q., Zhou Y., Biosorption of Acid Black 172 and Congo Red from aqueous solution by nonviable Penicillium YW 01: kinetic study, equilibrium isotherm and artificial neural network modeling, Bioresour. Technol., 102, 828–834, 2011.
  • Wang P.F., Cao M.H., Wang C., Ao Y.H., Hou J., Qian J., Kinetics and thermodynamics of adsorption of methylene blue by a magnetic graphene-carbon nanotube composite, Appl. Surf. Sci., 290, 116–124, 2014.
  • Buyukada M., Uzuner S., Evrendilek F., Utilization of (Modified-) Ground Hazelnut Shells for Adsorption of Azo-metal Toxic Dyes: Empirical and ANFIS Modeling and Optimization, Chiang Mai J. Sci., 45(1), 342–354, 2018.
  • Yildiz Z., Uzun H., Ceylan S., Topcu Y., Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends, Bioresour. Technol., 200, 42–47, 2016.
  • Khataee A.R., Kasiri M.B., Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis, J. Mol. Catal. A-Chem., 331, 86–100, 2010.
  • Mikulandric R., Loncar D., Böhning D., Böhme R., Beckmann M., Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers, Energy Convers. Manage, 87, 1210–1223, 2014.
  • Ata R., Artificial neural networks applications in wind energy systems: a review. Rene. Sustain. Energy Rev. 49, 534–562, 2015.
  • Sahin F., Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network, Appl. Therm. Eng., 90, 94–101, 2015.
  • Yetilmezsoy K., Demirel S., Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia vera L.) shells, J. Hazard. Mater., 153, 1288–1300, 2008.
  • Vani S., Sukumaran R.K., Savithri S., Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling, Bioresour. Technol., 188, 128–135, 2015.
  • Chiou M.S., Li H.Y., Equilibrium and kinetic modeling of adsorption of reactive dye on cross-linked chitosan beads, J. Hazard. Mater., 93, 233–248, 2002.
  • Dutta S., Optimization of Reactive Black 5 removal by adsorption process using Box-Behnken design. Des. Water Treat., 51, 40–42, 2013.
  • Jumasiah A., Chuah T.G., Gimbon J., Choong T.S.Y., Azni I., Adsorption of basic dye onto palm kernel shell activated carbon: sorption equilibrium and kinetics studies, Desalination, 186, 57–64, 2005.
  • Al-Ghouti M., Khraisheh, M.A.M., Ahmad M.N.M., Allen S., Thermodynamic behaviour and the effect of temperature on the removal of dyes from aqueous solution using modified diatomite: a kinetic study, J. Colloid. Interf. Sci., 287, 6–13, 2005.
  • Ozacar M., Sengil I.A., Adsorption of reactive dyes on calcined alunite from aqueous solutions, J. Hazard. Mater., B98, 211–224, 2003.
  • Gong R.M., Ding Y., Lie M., Yang C., Liu H.J., Sun Y.Z., Utilization of powdered peanut hull as biosorbent for removal of anionic dyes from aqueous solution, Dyes Pigments, 64, 187–192, 2005.
  • Arami M., Limaee N.Y., Mahmoodi NM. Tabrizi NS., Equilibrium and kinetics studies for the adsorption of direct and acid dyes from aqueous solution by sol meal hull, J. Hazard. Mater., 135, 171–179, 2006.
  • Hashemian S., Misrhamsi M., Kinetic and thermodynamic of adsorption of 2–picoline by sawdust from aqueous solution, J. Ind. Eng. Chem., 18, 2010–2015, 2012.
  • Hanafiah M.A.K.M., Ngah W.S.W., Zolkafly S.H., Teong J.C., Majid Z.A.A., Acid Blue 25 adsorption on base treated Shorea dasyphylla sawdust: Kinetic, isotherm, thermodynamic and spectroscopic analysis, J. Environ. Sci., 24(2), 261–268, 2012.
  • Mittal A., Adsorption kinetics of removal of a toxic dye, Malachite Green, from wastewater by using hen feathers, J. Hazard. Mater., 133, 196–202, 2006.
  • Wu C.H., Yu C.H., Effects of TiO2 dosage, pH and temperature on decolorization of C.I. Reactive Red 2 in a UV/US/TiO2 system, J. Hazard. Mater., 169, 1179–1183, 2009.
  • Cicek F., Ozer, D., Ozer, A., Ozer A., Low cost removal of reactive dyes using wheat bran, J. Hazar. Mater. 146, 408–416, 2007.
  • Ozer A., Dursun G., Removal of methylene blue from aqueous solution by dehydrated wheat bran carbon, J. Hazar. Mater, 146, 262–269, 2007.
Yıl 2019, Cilt: 34 Sayı: 4, 2033 - 2044, 25.06.2019
https://doi.org/10.17341/gazimmfd.418184

Öz

Kaynakça

  • Buyukada M., Removal of yellow f3r, di maria brilliant blue r and reactive brilliant red-3me from aqueous solutions by a rapid and efficient ultrasound–assisted process with a novel biosorbent of cottonseed cake: Statistical modeling, kinetic and thermodynamic studies, Arab. J. Sci. Eng., 40(8), 2153–2168, 2015.
  • Rafatullah M., Sulaiman O., Hashim R., Ahmad A., Adsorption of methylene blue on low-cost adsorbents: a review, J. Hazard. Mater. 177 (2010) 70–80.
  • A, Celekli, S.S. Bilecikligil, F. Geyik, H. Bozkurt, Prediction of removal efficiency Lanaset Red G on walnut husk using artificial neural network model, Bioresour. Technol., 103, 64–70, 2012.
  • Srinivasan A., Viraraghavan T., Decolorization of dye wastewaters by biosorbents: a review, J. Environ. Manage., 91, 1915–1929, 2010.
  • Celekli A., Geyik F., Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G Chara contraria, Bioresour. Technol., 102, 5634–5638, 2011.
  • Buyukada M., Evrendilek F., Modeling Efficiency of Dehydrated Sunflower Seed Cake as a Novel Biosorbent to Remove a Toxic Azo Dye, Chem. Eng. Commun., 203(6), 746–757, 2016.
  • Uzuner S., Cekmecelioglu D., Enhanced pectinase production by optimizing fermentation conditions of Bacillus subtilis growing on hazelnut shell hydrolyzate, J. Mol. Catal. B Enzym., 113, 62–67, 2015.
  • Ozkal S.G., Yener, M.E., Supercritical carbon dioxide extraction of flaxseed oil: effect of extraction parameters and mass transfer modeling, J. Supercrit. Fluids, 112, 76–80, 2016.
  • Buyukada M., Co-combustion of peanut hull and coal blends: Artificial neural networks, particle swarm optimization and Monte Carlo simulation, Bioresour. Technol., 216, 280–286, 2016.
  • Celekli A., Bozkurt H., Geyik F., Use of artificial neural networks and genetic algorithm for prediction of sorption of an azo-metal complex dye onto lentil straw, Bioresour. Technol., 129, 396–401, 2013.
  • Gajic D., Savic-Gajic I., Savic I., Georgieva O., Gennaro S., Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks, Energy, 108(1), 132–139, 2016.
  • Evrendilek G.A., Avsar Y.K., Evrendilek F., Modeling stochastic variability and uncertainty in aroma active compounds of PEF-treated peach nectar as a function of physical and sensory properties, and treatment time, Food Chem., 190, 634–642, 2016.
  • Buyukada M., Probabilistic uncertainty analysis based on Monte Carlo simulation of co-combustion of hazelnut hull and coal belnds: Data-driven modeling and response surface optimization, Bioresour. Technol., 225, 106–112, 2017.
  • Buyukada M., Modeling of decolorization of synthetic reactive dyestuff solutions with response surface methodology by a rapid and efficient process of ultrasound–assisted ozone oxidation, Des. Wat. Treat., 57(32), 14973–14985, 2016.
  • Kumar K.V., Porkodi K., Modelling the solid–liquid adsorption processes using artificial neural networks rained by pseudo second order kinetics, Chem. Eng. J., 148, 20–25, 2009.
  • Yao Y.J., Xu F.F., Chen M., Xu Z.X., Zhu Z.W., Adsorption behavior of methylene blue on carbon nanotubes, Bioresour. Technol., 101, 3040–3046, 2010.
  • Yang G., Wang B., Wang Z., Li X., Jia Q., Zhou Y., Biosorption of Acid Black 172 and Congo Red from aqueous solution by nonviable Penicillium YW 01: kinetic study, equilibrium isotherm and artificial neural network modeling, Bioresour. Technol., 102, 828–834, 2011.
  • Wang P.F., Cao M.H., Wang C., Ao Y.H., Hou J., Qian J., Kinetics and thermodynamics of adsorption of methylene blue by a magnetic graphene-carbon nanotube composite, Appl. Surf. Sci., 290, 116–124, 2014.
  • Buyukada M., Uzuner S., Evrendilek F., Utilization of (Modified-) Ground Hazelnut Shells for Adsorption of Azo-metal Toxic Dyes: Empirical and ANFIS Modeling and Optimization, Chiang Mai J. Sci., 45(1), 342–354, 2018.
  • Yildiz Z., Uzun H., Ceylan S., Topcu Y., Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends, Bioresour. Technol., 200, 42–47, 2016.
  • Khataee A.R., Kasiri M.B., Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis, J. Mol. Catal. A-Chem., 331, 86–100, 2010.
  • Mikulandric R., Loncar D., Böhning D., Böhme R., Beckmann M., Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers, Energy Convers. Manage, 87, 1210–1223, 2014.
  • Ata R., Artificial neural networks applications in wind energy systems: a review. Rene. Sustain. Energy Rev. 49, 534–562, 2015.
  • Sahin F., Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network, Appl. Therm. Eng., 90, 94–101, 2015.
  • Yetilmezsoy K., Demirel S., Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia vera L.) shells, J. Hazard. Mater., 153, 1288–1300, 2008.
  • Vani S., Sukumaran R.K., Savithri S., Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling, Bioresour. Technol., 188, 128–135, 2015.
  • Chiou M.S., Li H.Y., Equilibrium and kinetic modeling of adsorption of reactive dye on cross-linked chitosan beads, J. Hazard. Mater., 93, 233–248, 2002.
  • Dutta S., Optimization of Reactive Black 5 removal by adsorption process using Box-Behnken design. Des. Water Treat., 51, 40–42, 2013.
  • Jumasiah A., Chuah T.G., Gimbon J., Choong T.S.Y., Azni I., Adsorption of basic dye onto palm kernel shell activated carbon: sorption equilibrium and kinetics studies, Desalination, 186, 57–64, 2005.
  • Al-Ghouti M., Khraisheh, M.A.M., Ahmad M.N.M., Allen S., Thermodynamic behaviour and the effect of temperature on the removal of dyes from aqueous solution using modified diatomite: a kinetic study, J. Colloid. Interf. Sci., 287, 6–13, 2005.
  • Ozacar M., Sengil I.A., Adsorption of reactive dyes on calcined alunite from aqueous solutions, J. Hazard. Mater., B98, 211–224, 2003.
  • Gong R.M., Ding Y., Lie M., Yang C., Liu H.J., Sun Y.Z., Utilization of powdered peanut hull as biosorbent for removal of anionic dyes from aqueous solution, Dyes Pigments, 64, 187–192, 2005.
  • Arami M., Limaee N.Y., Mahmoodi NM. Tabrizi NS., Equilibrium and kinetics studies for the adsorption of direct and acid dyes from aqueous solution by sol meal hull, J. Hazard. Mater., 135, 171–179, 2006.
  • Hashemian S., Misrhamsi M., Kinetic and thermodynamic of adsorption of 2–picoline by sawdust from aqueous solution, J. Ind. Eng. Chem., 18, 2010–2015, 2012.
  • Hanafiah M.A.K.M., Ngah W.S.W., Zolkafly S.H., Teong J.C., Majid Z.A.A., Acid Blue 25 adsorption on base treated Shorea dasyphylla sawdust: Kinetic, isotherm, thermodynamic and spectroscopic analysis, J. Environ. Sci., 24(2), 261–268, 2012.
  • Mittal A., Adsorption kinetics of removal of a toxic dye, Malachite Green, from wastewater by using hen feathers, J. Hazard. Mater., 133, 196–202, 2006.
  • Wu C.H., Yu C.H., Effects of TiO2 dosage, pH and temperature on decolorization of C.I. Reactive Red 2 in a UV/US/TiO2 system, J. Hazard. Mater., 169, 1179–1183, 2009.
  • Cicek F., Ozer, D., Ozer, A., Ozer A., Low cost removal of reactive dyes using wheat bran, J. Hazar. Mater. 146, 408–416, 2007.
  • Ozer A., Dursun G., Removal of methylene blue from aqueous solution by dehydrated wheat bran carbon, J. Hazar. Mater, 146, 262–269, 2007.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Musa Büyükada 0000-0001-6841-6457

Yayımlanma Tarihi 25 Haziran 2019
Gönderilme Tarihi 24 Nisan 2018
Kabul Tarihi 31 Ocak 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 34 Sayı: 4

Kaynak Göster

APA Büyükada, M. (2019). Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(4), 2033-2044. https://doi.org/10.17341/gazimmfd.418184
AMA Büyükada M. Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi. GUMMFD. Haziran 2019;34(4):2033-2044. doi:10.17341/gazimmfd.418184
Chicago Büyükada, Musa. “Fındık Kabukları Ile Farklı Model boyaların Gideriminin Kinetik Ve Termodinamik Incelemesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34, sy. 4 (Haziran 2019): 2033-44. https://doi.org/10.17341/gazimmfd.418184.
EndNote Büyükada M (01 Haziran 2019) Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34 4 2033–2044.
IEEE M. Büyükada, “Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi”, GUMMFD, c. 34, sy. 4, ss. 2033–2044, 2019, doi: 10.17341/gazimmfd.418184.
ISNAD Büyükada, Musa. “Fındık Kabukları Ile Farklı Model boyaların Gideriminin Kinetik Ve Termodinamik Incelemesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34/4 (Haziran 2019), 2033-2044. https://doi.org/10.17341/gazimmfd.418184.
JAMA Büyükada M. Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi. GUMMFD. 2019;34:2033–2044.
MLA Büyükada, Musa. “Fındık Kabukları Ile Farklı Model boyaların Gideriminin Kinetik Ve Termodinamik Incelemesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 34, sy. 4, 2019, ss. 2033-44, doi:10.17341/gazimmfd.418184.
Vancouver Büyükada M. Fındık kabukları ile farklı model boyaların gideriminin kinetik ve termodinamik incelemesi. GUMMFD. 2019;34(4):2033-44.