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Neural networks and differential evolution algorithm applied for modelling the depollution process of some gaseous streams

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Abstract

The depollution of some gaseous streams containing n-hexane is studied by adsorption in a fixed bed column, under dynamic conditions, using granular activated carbon and two types of non-functionalized hypercross-linked polymeric resins. In order to model the process, a new neuro-evolutionary approach is proposed. It is a combination of a modified differential evolution (DE) with neural networks (NNs) and two local search algorithms, the global and local optimizers, working together to determine the optimal NN model. The main elements that characterize the applied variant of DE consist in using an opposition-based learning initialization, a simple self-adaptive procedure for the control parameters, and a modified mutation principle based on the fitness function as a criterion for reorganization. The results obtained prove that the proposed algorithm is able to determine a good model of the considered process, its performance being better than those of an available phenomenological model.

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References

  • Ahmad AA, Hameed BH (2010) Fixed-bed adsorption of reactive azo dye onto granular activated carbon prepared from waste. J Hazard Mater 175:298–303

    Article  CAS  Google Scholar 

  • Brest, J. (2009). Constrained real-parameter optimization with e-self-adaptive differential evolution. In E. Mezura-Montes (Ed.), Constraint-handling in evolutionary optimization. Studies in Computational Intelligence Springer Berlin/Heidelberg, pp. 73-93.

  • Buburuzan AM, Catrinescu C, Macoveanu M (2009) Adsorption of n-hexane vapors onto non-functionalised hypercrosslinked polymers (hypersol-macronettm) and activated carbon: thermodynamic and kinetic studies. Environ Eng Manag J 8:259–265

    CAS  Google Scholar 

  • Buburuzan AM, Catrinescu C, Macoveanu M (2010) Comparative study of the adsorption-desorption cycles of hexane over hypercrosslinked polymeric adsorbents and activated carbon. Environ Eng Manag J 9:125–132

    CAS  Google Scholar 

  • Caicedo Torres W, Quintana M, Pinzon H (2013) Differential diagnosis of hemorrhagic fevers using ARTMAP and artificial immune system. Int J Artif Intell 11:150–169

  • Chandra A, Yao X (2006) Ensemble learning using multi-objective evolutionary algorithms. J Math Model Algoritm 5:417–445

    Article  Google Scholar 

  • Curteanu S, Cartwright H (2012) Neural networks applied in chemistry. I Determination of the optimal topology of multilayer perceptron neural networks J Chemom 25:527–549

    Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15:4–31

    Article  Google Scholar 

  • Dragoi EN, Curteanu S, Leon F, Galaction AI, Cascaval D (2011) Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm. Eng Appl Artif Intell 24:1214–1226

    Article  Google Scholar 

  • Dragoi EN, Curteanu S, Lisa C (2012a) A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds. Eng Optim 44:1261–1277

    Article  Google Scholar 

  • Dragoi EN, Curteanu S, Fissore D (2012b) Freeze-drying modeling and monitoring using a new neuro-evolutive technique. Chem Eng Sci 72:195–204

    Article  CAS  Google Scholar 

  • Dragoi EN, Curteanu S, Galaction AI, Cascaval D (2013) Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process. Appl Soft Comput 13:222–238

    Article  Google Scholar 

  • Feoktistov, V. (2006). Differential evolution: in search of solutions. Springer

  • Floreano D, Durr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1:47–62

    Article  Google Scholar 

  • Furtuna R, Curteanu S, Leon F (2012) Multi-objective optimization of a stacked neural network using an evolutionary hyper-heuristic. Appl Soft Comput 12:133–144

    Article  Google Scholar 

  • Gupta VK, Verma N (2002) Removal of volatile organic compounds by cryogenic condensation followed by adsorption. Chem Eng Sci 57:2679–2696

    Article  CAS  Google Scholar 

  • Halteta Buburuzan MB, Catrinescu C, Macoveanu M (2009) Adsorption of n-hexane vapors onto non-functionalized hypercrosslinked polymers (hypersol-macronettm) and activated carbon: equilibrium studies. Environ Eng Manag J 8:173–181

  • Han L, Shi X, Wu W, Kirk FL, Luo J, Wang L et al (2005) Nanoparticle-structured sensing array materials and pattern recognition for VOC detection. Sens Actuators B: Chem 106:431–441

    Article  CAS  Google Scholar 

  • Hernandez RP, Alvarez-Gallegos J, Reyes J (1998) Simple recurrent neural network: a neural network structure for control systems. Neurocomput 3:277–289

    Article  Google Scholar 

  • Hu Q, Li JJ, Hao ZP, Li LD, Qiao SZ (2009) Dynamic adsorption of volatile organic compounds on organofunctionalized SBA-15 materials. Chem Eng J 149:281–288

    Article  CAS  Google Scholar 

  • Khan FI, Ghoshal A (2000) Removal of volatile organic compounds from polluted air. J Loss Prev Process Ind 13(6):527–545

    Article  Google Scholar 

  • Kim KJ, Kang CS, You YJ, Chung MC, Woo MW, Jeong WJ et al (2006) Adsorption-desorption characteristics of VOCs over impregnated activated carbons. Catal Today 111:223–228

    Article  CAS  Google Scholar 

  • Leeghim H, Seo IH, Bang H (2008) Adaptive nonlinear control using input normalized neural networks. J Mech Sci Technol 22:1073–1083

    Article  Google Scholar 

  • Lillo-Rodenas MA, Fletcher AJ, Thomas KM, Cazorla-Amoris D, Linares-Solano A (2006) Competitive adsorption of a benzene-toluene mixture on activated carbons at low concentration. Carbon 44:1455–1463

    Article  CAS  Google Scholar 

  • Llanos J, Rodrigo MA, Canizares P, Furtuna RP, Curteanu S (2013) Neuro-evolutionary modelling of the electrodeposition stage of a polymer-supported ultrafiltration-electrodeposition process for the recovery of heavy metals. Environ Model Softw 42:133–142

    Article  Google Scholar 

  • Matros YS, Noskov AS, Chumachenko VA, Goldman OV (1988) Theory and application of unsteady catalytic detoxication of effluent gases from sulfur dioxide, nitrogen oxides and organic compounds. Chem Eng Sci 43:2061–2066

    Article  CAS  Google Scholar 

  • Matthies M, Giupponi C, Ostendorf B (2007) Environmental decision support systems: current issues, methods and tools. Environ Model Softw 22:123–127

    Article  Google Scholar 

  • Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33:61–106

    Article  Google Scholar 

  • Odabasi M, Ongan O, Cetin E (2005) Quantitative analysis of volatile organic compounds (VOCs) in atmospheric particles. Atmospheric Environ 39:3763–3770

    Article  CAS  Google Scholar 

  • Pant M, Thangaraj R, Abraham A, Grosan C, Differential Evolution with Laplace mutation operator (2009) Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Trondheim. IEEE Press, Norway, pp 2841–2849

    Google Scholar 

  • Patan K, Approximation Abilities of Locally Recurrent Networks (2008) Artificial neural networks for the modelling and fault diagnosis of technical processes. Lecture notes in control and information sciences. Springer Berlin, Heidelberg, pp 65–75

    Book  Google Scholar 

  • Peng L, Wang Y (2010) Differential evolution using uniform-quasi-opposition for initializing the population. Inf Technol J 9:1629–1634

    Article  Google Scholar 

  • Pirdashti M, Curteanu S, Kamangar MH, Hassim M, Amid M (2013) Artificial neural networks: applications in chemical engineering. Rev Chem Eng 29:205–239

    Article  CAS  Google Scholar 

  • Precup RE, Tomescu ML, Preitl S (2007) Lorenz system stabilization using fuzzy controllers. Int J Comput Commun Control 2:279–287

    Google Scholar 

  • Shim WG, Lee JW, Moon H (2006) Adsorption equilibrium and column dynamics of VOCs on MCM-48 depending on pelletizing pressure. MicroporousMesoporous Mater 88:112–125

    Article  CAS  Google Scholar 

  • Silvestre-Albero A, Ramos-Fernandez JM, Martinez-Escandell M, Seplveda-Escribano A, Silvestre-Albero J, Rodriguez-Reinoso F (2010) High saturation capacity of activated carbons prepared from mesophase pitch in the removal of volatile organic compounds. Carbon 48:548–556

    Article  CAS  Google Scholar 

  • Srivastava AK (2003) Detection of volatile organic compounds (VOCs) using SnO2 gas-sensor array and artificial neural network. Sensors Actuators B Chem 96:24–37

    Article  CAS  Google Scholar 

  • Storn R, Differential Evolution Research - Trends and Open Questions. In U. Chakraborty (2008) Advances in differential evolution. Studies in computational intelligence. Springer Berlin, Heidelberg, pp 1–31

    Google Scholar 

  • Subudhi B, Jena D (2009) An improved differential evolution trained neural network scheme for nonlinear system identification. Int J Autom Comput 6:137–144

    Article  Google Scholar 

  • Tchoupo GN, Guiseppi-Elie A (2005) On pattern recognition dependency of desorption heat, activation energy, and temperature of polymer-based VOC sensors for the electronic NOSE. Sensors Actuators B Chem 110:81–88

    Article  CAS  Google Scholar 

  • Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: In: International Conference on Computational Intelligence for Modeling, Control and International Conference on Intelligent Agents, Web technologies and Internet Commerce. 28-30 Nov 2005., pp 695–701

    Google Scholar 

  • Vassileiou A, Maris F, Kitikidou K, Iliadis L (2012) Artificial neural networks for improved predictions in flow estimation. Int J Artif Intell 9:186–201

    Google Scholar 

  • Xin Y (1999) Evolving artificial neural networks. Proc IEEE 87:1423–1447

    Article  Google Scholar 

  • Yoon YH, Nelson JH (1984) Application of gas adsorption kinetics. I. A theoretical model for respirator cartridge service life. Am Ind Hyg Assoc J 45:509–516

    Article  CAS  Google Scholar 

  • Zarth, A., & Ludermir, T. B. (2009). Optimization of neural networks weights and architecture: a multimodal methodology. In: Ninth International Conference on Intelligent Systems Design and Applications (ISDA’09) pp. 209-214.

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Acknowledgments

This work was supported by the “Partnership in priority areas—PN-II” program, financed by ANCS, CNDI-UEFISCDI, project PN-II-PT-PCCA-2011-3.2-0732, No. 23/2012.

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Authors and Affiliations

  1. Faculty of Chemical Engineering and Environmental Protection, “Gheorghe Asachi” Technical University of Iasi, Bd. Prof. dr. doc. DimitrieMangeron, No. 73, 700050, Iasi, Romania

    Silvia Curteanu, Gabriel Dan Suditu, Adela Marina Buburuzan & Elena Niculina Dragoi

Authors
  1. Silvia Curteanu
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  2. Gabriel Dan Suditu
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  3. Adela Marina Buburuzan
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  4. Elena Niculina Dragoi
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Correspondence to Elena Niculina Dragoi.

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Responsible editor: Michael Matthies

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Curteanu, S., Suditu, G.D., Buburuzan, A.M. et al. Neural networks and differential evolution algorithm applied for modelling the depollution process of some gaseous streams. Environ Sci Pollut Res 21, 12856–12867 (2014). https://doi.org/10.1007/s11356-014-3232-x

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  • DOI: https://doi.org/10.1007/s11356-014-3232-x

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