مجله فصل مشترک ها، لایه های نازک و سیستم های با بعد کم
رهیافت شبکه عصبی مصنوعی برای مدلسازی جذب Cr(VI) از محلول آبی با MR, MAC, MS
نوع مقاله : مقاله پژوهشی
نویسندگان
چکیده
در این تحقیق توان جذب رزین دوکس اپتیپور493Lکه توسط نمک آمونیوم 336 (MR) اصلاح شده است، توان جذب کربن فعال که با نمک آمونیوم 336 (MAC) اصلاح شده است و توان جذب خاک اره که توسط نمک آمونیوم 336 (MS) اصلاه شده است را برای حذف Cr(VI)از محلول آبی در یک سیستم مرکب مورد مطالعه قرار می دهیم. اثرات پارامترهای عملیاتی مانند دوز جاذب، غلظت اولیه یون های Cr (VI)، pH، دما و زمان تماس مورد مطالعه قرار گرفته است. مدل شبکه عصبی مصنوعی (ANN) برای پیش بینی کارایی حذف یونCr (VI)توسعه داده شده است. نتایج نشان می دهد که ایزوترم لانگمویر بهتر از ایزوترم فرویندلیش است. آهنگ جذب بهترین حالت را در مدل مرتبه شبه دوم نشان می دهد. پارامترهای ترمودینامیکی نشان می دهند که جذب Cr (VI)امکان پذیر، خود به خودی و گرمازا است. مقایسه کارایی حذف کروم (VI) با استفاده از مدل شبکه عصبی مصنوعی و نتایج تجربی نشان می دهد که مدل شبکه عصبی مصنوعی می تواند رفتار فرایند حذف Cr (VI)را در شرایط مختلف برآورد کند.
کلیدواژه‌ها
موضوعات
[1] S. Aber, A.R. Amani-Ghadim, V. Mirzajani. “Removal of Cr(VI) from polluted solutions by electrocoagulation: Modeling ofexperimental results using artificial neural network.” J. Hazardous Materials, 171 (2009) 484.
[2] A. H. Aktas, S. Yasar. “Potentiometric titration of some hydroxylated benzoic acids and cinnamic acids by artificial neural network calibration.” Acta Chimica Slovenica, 51 (2004) 273.
[3] S. M. H. Asl, M. Ahmadi, M. Ghiasvand, A. Tardast, R. Katal. “Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA).” J. Industrial and Engineering Chemistry, 19 (2013) 1044.
[4] E. Dincturk-Atalay. “Determination and removal of chromium with solid phase extraction method in industrial waste water.” Süleyman Demirel University, Graduate School of Applied and Natural Sciences Chemistry Department, 139 pages (2012).
[5] S. Elemen, E. P. Akçakoca Kumbasar, S. Yapar. “Modeling the adsorption of textile dye on organoclay using an artificial neural network.” Dyes and Pigments, 95 (2012) 102.
[6] K. Z. Elwakeel. “Removal of Cr(VI) from alkaline aqueous solutions using chemically modified magnetic chitoan resins.” Desalination, 250 (2010) 105.
[7] A. M. Ghaedi, A. Vafaei. “Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review.” Advances in Colloid and Interface Science, in press.
[8] F. Gode, E. Dincturk Atalay, E. Pehlivan. “Removal of Cr(VI) from aqueous solutions using modified red pine sawdust.” J. Hazardous Materials, 152 (2003) 1201.
[9] F. Gode, E. Pehlivan. “A comparative study of two chelating ion-exchange resins for the removal of Chromium(III) from aqueous solution.” J. Hazardous Materials, B100 (2003) 231.
[10] F. Gode, E. Pehlivan. “Removal of Cr(VI) from aqueous solution by two Lewatit-anion exchange resins.” J. Hazardous Materials, B119 (2005) 175.
[12] F. Gode, E. Pehlivan. “Removal of Chromium(III) from aqueous solutions using Lewatit S 100: The effect of pH, time, metal concentration and temperature.” J. Hazardous Materials, B136 (2006) 330.
[13] F. Gode., E. Pehlivan. “Adsorption of Cr(III) ions by Turkish brown coals.” Fuel Processing Technology, 86 (2005) 875
[14] F. Gode, E. Pehlivan (2007). “Sorption of Cr(III) onto chelating b-DAEG–sporopollenin and CEP–sporopollenin resins.” Bioresource Technology, 98 (2007) 904.
[15] A. Gok, F. Gode, B. Esencan Turkaslan. “Chromium(VI) ion removal from solution polyaniline/pumice composite.” Asian J. of Chemistry, 19 (2007) 3023.
[16] R. Gomez-Gonzalez, F. J. Cerino-Córdova, A. M. Garcia-León, E. Soto-Regalado, N. E. Davila-Guzman, J. J. Salazar-Rabago. “Lead biosorption onto coffee grounds: Comparative analysis of several optimization techniques using equilibrium adsorption models and ANN.” J. the Taiwan Institute of Chemical Engineers, 68 (2016) 201.
[17] A. Hassani, F. Vafaei, S. Karaca, A. R. Khataee. “Adsorption of a cationic dye from aqueous solution using Turkish lignite: Kinetic, isotherm, thermodynamic studies and neural network modeling.” J. Industrial and Engineering Chemistry, 20 (2014) 2615.
[18] R. Karimi, F. Yousefi, M. Ghaedi, K. Dashtian. “Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur(II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study.” Chemometrics and Intelligent Laboratory Systems, 159 (2016) 127.
[19] U. Maheshwari, S. Gupta. “A novel method to identify optimized parametric values for adsorption of heavy metals from wastewater.” J. Water Process Engineering, 9 (2016) e21.
[20] S. Mandal, S. S. Mahapatra, M. K. Sahu, R. K. Patel. “Artificial neural network modelling of As(III) removal from water by novel hybrid material.” Process Safety and Environmental Protection, 93 (2015a) 249.
[21] S. Mandal, S.S. Mahapatra, R.K. Patel. “Enhanced removal of Cr(VI) by cerium oxide polyaniline composite: Optimization and modeling approach using response surfacemethodology and artificial neural networks.” J. Environmental Chemical Engineering, 3 (2015b) 870.
[22] E. Oguz. “Fixed-bed column studies on the removalof Fe3+ and neural network modelling.” Arabian J. of Chemistry, 10 (2017) 313.
[23] E. Oguz, M. Ersoy. “Biosorption of Cobalt(II) with sunflower biomass from aqueous solutions in a fixed bed column and neural networks modelling.” Ecotoxicology and Environmental Safety, 99 (2014) 54.
[24] E. Oguz, M. Ersoy. “Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and Neural Network Modelling.” Chemical Engineering Journal, 164 (2010) 56.
[25] A. A. Oladipo, M. Gazi. “Nickel removal from aqueous solutions by alginate-based composite beads: Central composite design and artificial neural network modeling.” J. Water Process Engineering, 8 (2010) e81.
[26] M. Parveen, S. Zaidi, M. Danish. “Development of SVR-based model and comparative analysis with MLR and ANN models for predictingthe sorption capacity of Cr(VI).” Process Safety and Environmental Protection, 107 (2017) 428.
[27] D. Podstawczyk, A. Witek-Krowiak, A. Dawiec, A. Bhatnagar. “Biosorption of Copper(II) ions by flax meal: Empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation.” Ecological Engineering, 83 (2015) 364.
[28] M. Prakash, S. A. Manikandan, L. Govindarajan, V. Vijayagopal. “Prediction of biosorption efficiency for the removal of Copper(II) using artificial neural networks.” J. Hazardous Materials, 152 (2008) 1268.
[29] M. Shanmugaprakash, V. Sivakumar. “Development of experimental design approach and ANN-based modelsfor determination of Cr(VI) ions uptake rate from aqueous solution ontothe solid biodiesel waste residue.” Bioresource Technology, 148 (20136) 550.
[30] T. Shojaeimehr, F. Rahimpour, M. A. Khadivi, M. Sadeghi (2014). “A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA).” J. Industrial and Engineering Chemistry, 20(2014) 870.
[31] B. Singha, N. Bar, S. K. Das. “The use of artificial neural network (ANN) for modeling of Pb(II) adsorption in batch process.” J. Molecular Liquids, 211 (2015) 228.
[32] R. R. Siva Kiran, G.M. Madhu, S. V. Satyanarayana, P. Kalpana, G. Subba Rangaiah. “Applications of Box–Behnken experimental design coupled with artificialneural networks for biosorption of low concentrations of cadmium using Spirulina (Arthrospira) spp.”Resource-Efficient Technologies, 3 (2017) 113.
[33] N. G. Turan, B. Mesci, O. Ozgonenel. “Artificial neural network (ANN) approach for modeling Zn(II) adsorption fromleachate using a new biosorbent.” Chemical Engineering Journal, 173 (2011) 98.
[34] N. G. Turan, B. Mesci, O. Ozgonenel. “The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II)from industrial leachate by pumice.” Chemical Engineering Journal, 171 (2011) 1091.
[35] A. G. Yavuz, E. Dincturk-Atalay, A. Uygun, F. Gode, E. Aslan. “A comparison study of adsorption of Cr(VI) from aqueous solutions ontoalkyl-substituted polyaniline/chitosan composites.” Desalination, 279 (2011) 325.
[36] K. Yetilmezsoy, S. Demirel. “Artificial neural network (ANN) approach for modeling of Pb(II) adsorptionfrom aqueous solution by Antep pistachio (Pistacia Vera L.) shells.” J. Hazardous Materials, 153 (2008) 1288.
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