Modeling the Rheological Characteristics of Flexible High-Yield Pulp-Fibre-Reinforced Bio-Based Nylon 11 Bio-Composite

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53768#.VNHXMCzQrzE

The aim of this work was to develop a mathematical model to investigate the rheological characteristics of viscoelastic pulp-fibre composite materials. The rheological properties of High-Yield Pulp (HYP) reinforced bio-based Nylon 11 (Polyamide 11) (PA11) composite (HYP/PA11) were investigated using a capillary rheometer. Novel predicted multiphase rheological-model-based polymer, fibre, and interphasial phases were developed. Rheological characteristics of the compo-site components influence the development of resultant microstructures; this in turn affects mechanical characteristics of a multiphase composite. The main rheological characteristics of polymer materials are viscosity and shear rate. Experimental and theoretical test results of HYP/PA11 show a steep decrease in apparent viscosity with increasing shear rate, and this melt-flow characteristic corresponds to shear-thinning behavior in HYP/PA11. The non-linear mathematical model to predict the rheological behavior of HYP/PA11 was validated experimentally at 200°C and 5000 S-1 shear rate. Finally, predicted and experimental viscosity results were compared and found to be in a strong agreement.

Cite this paper

Cherizol, R. , Sain, M. and Tjong, J. (2015) Modeling the Rheological Characteristics of Flexible High-Yield Pulp-Fibre-Reinforced Bio-Based Nylon 11 Bio-Composite. Journal of Encapsulation and Adsorption Sciences, 5, 1-10. doi: 10.4236/jeas.2015.51001.

References

[1] Pervaiz, M. and Sain, M. (2003) Carbon Storage Potential In natural Fibre Composites. Resources, Conservation and Recycling, 39, 325-340. http://dx.doi.org/10.1016/S0921-3449(02)00173-8
[2] Bourmaud, A. and Baley, C. (2009) Rigidity Analysis of Polypropylene/Vegetal Fibre Composites after Recycling. Polymer Degradation and Stability, 39, 297-305.
http://dx.doi.org/10.1016/j.polymdegradstab.2008.12.010
[3] George, J., Sreekala, M.S. and Thomas, S. (2001) A Review on Interface Modification and Characterization of Natural Fiber Reinforced Plastic Composites. Polymer Engineering Science, 41, 1471-1485.
http://dx.doi.org/10.1002/pen.10846
[4] Gu, R. and Kokta, B. (2010) Mechanical Properties of PP Composites Reinforced with BCTMP Aspen Fiber. Journal of Thermoplastic Composite Materials, 23, 513-542.
http://dx.doi.org/10.1177/0892705709355232
[5] Bajpai, P. (2012) Brief Description of the Pulp and Paper Making Process. Biotechnology for Pulp and Paper Processing, 7-14.
[6] Thomen, H. (2001) Modeling the Physical Processes in Natural Fiber Composites during Batch and Continuous Pressing. Oregon State University, Corvallis.
[7] Plackett, D., Torgilsson, R. and Andersen, T. (2010) Influence of Fiber Type, Fiber Mat Orientation, and Process Time on the Properties of a Wood Fiber/Polymer Composite. International Journal of Polymeric Materials, 51, 1005-1018.
http://dx.doi.org/10.1080/714975684
[8] Uhlherr, P.H.T., Guo, J., Zhang, X.M., Zhou, J.Z.Q. and Tiu, C. (2005) The Shear-Induced Solid-Liquid Transition in Yield Stress Materials with Chemically Different Structures. Journal of Non-Newtonian Fluid Mechanics, 125, 101- 119. http://dx.doi.org/10.1016/j.jnnfm.2004.09.009
[9] Liu, Y.J., Xu, N. and Luo, J.F. (2000) Modeling of Interphases in Fiber-Reinforced Composites under Transverse Loading Using Boundary Element Method. Journal of Applied Mechanics, 67, 41. http://dx.doi.org/10.1115/1.321150
[10] Kaw, A. and Besterfield, G. (1998) Effect of Interphase on Mechanical Behavior of Composites. Journal of Engineering Mechanics, 117, 2641-2658.
http://dx.doi.org/10.1061/(ASCE)0733-9399(1991)117:11(2641)
[11] Yeh, J.R. (1992) The Effect of Interface on the Transverse Properties of Composites. International Journal of Solids and Structures, 29, 2493-2502. http://dx.doi.org/10.1016/0020-7683(92)90005-E
[12] Gohil, P. and Shaikh, A. (2010) Analytical Investigation and Comparative Assessment of Interphase Influence on Elastic Behavior of Fiber Reinforced Composites. Journal of Reinforced Plastics and Composites, 29, 685-699.
[13] Kari, S., Berger, H., Rodriguez, R.R. and Gabbert, U. (2005) Computational Evaluation of Effective Material Properties of Composites Reinforced by Randomly Distributed Spherical Particles. Composite Structures, 71, 397-400.
[14] Lamnawar, K. and Maazouz, A. (2008) Rheology at the Interface and the Role of the Interphase in Reactive Functionalized Multilayer Polymers in Coextrusion Process. American Institute of Physics, 978, 7354-0549.
[15] Larache, M., Agbossou, A., Pastor, J. and Muller, D. (1994) Role of Interphase on the Elastic Behavior of Composite Materials: Theoretical and Experimental Analysis. Journal of Composite Materials, 28, 1141-1157.
[16] Deshpande, K. (2004) k-Version of Finite Element Method for Polymer Flows using Giesekus Constitutive Model. Ph.D. Thesis, University of Kansas, Lawrence.
[17] Hosseinalipour, S., Tohidi, A. and Shokrpour, M. (2012) A Review of Dough Rheological Models Used in Numerical Applications. Journal of Computational and Applied Research in Mechanical Engineering, 1, 129-147.
[18] Giesekus, H. (1982) A Simple Constitutive Equation for Polymer Fluids Based on the Concept of Deformation-Dependent Tensorial Mobility. Journal of Non-Newtonian Fluid Mechanics, 11, 69-109.
http://dx.doi.org/10.1016/0377-0257(82)85016-7                                                                eww150204lx

Mass Transfer in a Centrifugal Turbine Agitator-Pump

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=45304#.VL3_FizQrzE

ABSTRACT

This article is a continuation of the research, centering on a vacuum-filtration system, which is designed to reduce the concentration of calcium in water; a process is also known as—water softening. The problem of solving the concentration distribution of the initial (embryonic) particles of CaCO3-particles, which were introduced into the limited volume of the apparatus with a turbine agitator-pump, is addressed through the use of diffusion and deterministic-stochastic models of mass transfer. The solution of the extreme problem allows determining the most important process parameters, such as time of dispersions homogenization and the dispersion mass flow rate to the surface of a special filter. For these parameters a comparative analysis of the adequacy of the theory was found through experiments, performed in the study. We found that uniform distribution of concentrations along the height of the apparatus is achieved by the angular velocity of the rotation 400 rpm for the turbine with 6 – 7 blades at the time of homogenization 14s. In this case, the dispersion mass flow to the surface of the cylindrical filter is 3 50 mg/s at an average concentration of the introduced CaCO3 particles, which is equal to 10 g/L. We determined that the accuracy of the results depends on: the coordinates of the material input in the apparatus volume, the surface shape of the filter and the volumetric flow rate of the liquid (water), being discarded by the turbine blades in the normal direction to their surface.

Cite this paper

Katz, V. and Mazor, G. (2014) Mass Transfer in a Centrifugal Turbine Agitator-Pump. Journal of Water Resource and Protection, 6, 463-472. doi: 10.4236/jwarp.2014.65046.

References

[1] Oren, V.K. and Daltorphe, N. (2001) Improved Compact Accelerated Precipitation Softening. A Pretreatment for Membrane Processes. Ben-Gurion University of the Negev, Institute for Applied Research, Beer-Sheva, Israel.
[2] Katz, V.Y. and Mazor, G. (2011) Hydrodynamics of a Centrifugal Turbine Agitator-Pump. Russian Journal of Applied Chemistry, Modeling and Calculation of Technological Processes, 84, 1655-1669. http://dx.doi.org/10.1134/S1070427211090345
[3] Katz, V.Y. and Mazor, G. (2013) Hydrodynamics and Mass Transfer in a Turbine Pump-Mixer. Proceedings of XXVI International Scientific Conference on Mathemati-cal Methods in Technique and Technologies-MMTT-26, May 27-30, 2013, N. Novgorod State Technical University named after Alekseev, N. Novgorod, Russia.
[4] Tikhonov, V.I. and Mironov, M.A. (1977) Markov Processes. Soviet Radio.
[5] Chandrasekhar, S. (1947) Stochastic Problems in Physics and Astronomy. Moscow.
[6] Landau, L.D. and Lifshitz, E.M. (1988) Theoretical Physics: A Training Manual, Vol. 6, Hydrodynamics. 4th Edition, Science, Moscow.
[7] Katz, V.Y. (1990) Doctoral Sci. (Tech.) Dissertation. KKhTI, Kazan.
[8] Kutepov, A.M. (1987) Stochastic Analysis of Hydro-Mecha-Nical Processes of Separation of Heterogeneous Systems. Theoretical Foundations of Chemical Engineering, 21, 147-153.
[9] Korn, G. and Korn, T. (1968) Mathematical Handbook for Scientists and Engineers. Translation from English, Moscow.                                                                                                     eww150120lx

A Fuzzy Logic Model to Predict the Bioleaching Efficiency of Copper Concentrates in Stirred Tank Reactors

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53226#.VLhyzcnQrzE

ABSTRACT

Multiplicity of the chemical, biological, electrochemical and operational variables and nonlinear behavior of metal extraction in bioleaching environments complicate the mathematical modeling of these systems. This research was done to predict copper and iron recovery from a copper flotation concentrate in a stirred tank bioreactor using a fuzzy logic model. Experiments were carried out in the presence of a mixed culture of mesophilic bacteria at 35°  C, and a mixed culture of moderately thermophilic bacteria at 50°  C. Input variables were method of operation (bioleaching or electrobioleaching), the type of bacteria and time (day), while the recoveries of copper and iron were the outputs. A relationship was developed between stated inputs and the outputs by means of “if-then” rules. The resulting fuzzy model showed a satisfactory prediction of the copper and iron extraction and had a good correlation of experimental data with R-squared more than 0.97. The results of this study suggested that fuzzy logic provided a powerful and reliable tool for predicting the nonlinear and time variant bioleaching processes.

Cite this paper

Ahmadi, A. and Hosseini, M. (2015) A Fuzzy Logic Model to Predict the Bioleaching Efficiency of Copper Concentrates in Stirred Tank Reactors. International Journal of Nonferrous Metallurgy, 4, 1-8. doi: 10.4236/ijnm.2015.41001.

References

[1] Rossi, G. (1990) Biohydrometallurgy. McGraw-Hill, Boston.
[2] Pham, D. and Pham, P. (1999) Artificial Intelligence in Engineering. International Journal of Machine Tools and Manufacture, 39, 937-949. http://dx.doi.org/10.1016/S0890-6955(98)00076-5
[3] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[4] Hsiang, S. and Lin, Y. (2008) Application of Fuzzy Theory to Predict Deformation Behaviors of Magnesium Alloy Sheets under Hot Extrusion. Journal of Materials Processing Technology, 201, 138-144.
http://dx.doi.org/10.1016/j.jmatprotec.2007.11.222
[5] Bergh, L., Yianatos, J. and Leiva, C. (1998) Fuzzy Supervisory Control of Flotation Columns. Minerals Engineering, 11, 739-748. http://dx.doi.org/10.1016/S0892-6875(98)00059-4
[6] http://www.outotec.com/en/Products–services/Analyzers-and-automation/Zinc-refining-control-solutions/
[7] Abou, S.C. and Dao, T.-M. (2009) Fuzzy Logic Controller Based on Association Rules Mining: Application to Mineral Processing. Proceedings of the World Congress on Engineering and Computer Science, 2.
[8] Carvalho, M.T. and Durão, F. (2002) Control of a Flotation Column Using Fuzzy Logic Inference. Fuzzy Sets and Systems, 125, 121-133. http://dx.doi.org/10.1016/S0165-0114(01)00048-3
[9] Vieira, S., Sousa, J. and Durao, F. (2005) Fuzzy Modelling Strategies Applied to a Column Flotation Process. Minerals Engineering, 18, 725-729. http://dx.doi.org/10.1016/j.mineng.2004.10.008
[10] Saravani, A., Mehrshad, N. and Massinaei, M. (2014) Fuzzy-Based Modeling and Control of an Industrial Flotation Column. Chemical Engineering Communications, 201, 896-908. http://dx.doi.org/10.1080/00986445.2013.790815
[11] Petersen, J. and Dixon, D. (2007) Modelling Zinc Heap Bioleaching. Hydrometallurgy, 85, 127-143.
http://dx.doi.org/10.1016/j.hydromet.2006.09.001
[12] Bennett, C., McBride, D., Cross, M. and Gebhardt, J. (2012) A Comprehensive Model for Copper Sulphide Heap Leaching: Part 1 Basic Formulation and Validation Through Column Test Simulation. Hydrometallurgy, 127, 150-161.
http://dx.doi.org/10.1016/j.hydromet.2012.08.004
[13] Ahmadi, A., Ranjbar, M., Schaffie, M. and Petersen, J. (2012) Kinetic Modeling of Bioleaching of Copper Sulfide Concentrates in Conventional and Electrochemically Controlled Systems. Hydrometallurgy, 127, 16-23.
http://dx.doi.org/10.1016/j.hydromet.2012.06.010
[14] Leahy, M.J., Davidson, M.R. and Schwarz, M.P. (2005) A Two-Dimensional CFD Model for Heap Bioleaching of Chalcocite. ANZIAM Journal, 46, C439-C457.
[15] Gonzalez, R., Gentina, J.C. and Acevedo, F. (2004) Biooxidation of a Gold Concentrate in a Continuous Stirred Tank Reactor: Mathematical Model and Optimal Configuration. Biochemical Engineering Journal, 19, 33-42.
http://dx.doi.org/10.1016/j.bej.2003.09.007
[16] Pazouki, M., Ganjkhanlou, Y., Tofigh, A., Hosseini, M., Aghaie, E. and Ranjbar, M. (2012) Optimizing of Iron Bioleaching from a Contaminated Kaolin Clay by the Use of Artificial Neural Network. International Journal of Engineering-Transactions B: Applications, 25, 81-88.
[17] Ahmadi, A., Schaffie, M., Manafi, Z. and Ranjbar, M. (2010) Electrochemical Bioleaching of High Grade Chalcopyrite Flotation Concentrates in a Stirred Bioreactor. Hydrometallurgy, 104, 99-105.
http://dx.doi.org/10.1016/j.hydromet.2010.05.001
[18] Mendel, J.M. (1995) Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, 83, 345-377.
http://dx.doi.org/10.1109/5.364485
[19] Kasabov, N.K. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. Marcel Alencar, New York.
[20] Nguyen, H.T., Prasad, N.R., Walker, C.L. and Walker, E.A. (2010) A First Course in Fuzzy and Neural Control. CRC Press, Boca Raton.
[21] Nguyen, H.T. and Walker, E.A. (2005) A First Course in Fuzzy Logic. CRC Press, Boca Raton.    eww150116lx

A Fuzzy Logic Model to Predict the Bioleaching Efficiency of Copper Concentrates in Stirred Tank Reactors

Read  full  paper  at:

http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53226#.VLcsacnQrzE

ABSTRACT

Multiplicity of the chemical, biological, electrochemical and operational variables and nonlinear behavior of metal extraction in bioleaching environments complicate the mathematical modeling of these systems. This research was done to predict copper and iron recovery from a copper flotation concentrate in a stirred tank bioreactor using a fuzzy logic model. Experiments were carried out in the presence of a mixed culture of mesophilic bacteria at 35°  C, and a mixed culture of moderately thermophilic bacteria at 50°  C. Input variables were method of operation (bioleaching or electrobioleaching), the type of bacteria and time (day), while the recoveries of copper and iron were the outputs. A relationship was developed between stated inputs and the outputs by means of “if-then” rules. The resulting fuzzy model showed a satisfactory prediction of the copper and iron extraction and had a good correlation of experimental data with R-squared more than 0.97. The results of this study suggested that fuzzy logic provided a powerful and reliable tool for predicting the nonlinear and time variant bioleaching processes.

Cite this paper

Ahmadi, A. and Hosseini, M. (2015) A Fuzzy Logic Model to Predict the Bioleaching Efficiency of Copper Concentrates in Stirred Tank Reactors. International Journal of Nonferrous Metallurgy, 4, 1-8. doi: 10.4236/ijnm.2015.41001.

References

[1] Rossi, G. (1990) Biohydrometallurgy. McGraw-Hill, Boston.
[2] Pham, D. and Pham, P. (1999) Artificial Intelligence in Engineering. International Journal of Machine Tools and Manufacture, 39, 937-949. http://dx.doi.org/10.1016/S0890-6955(98)00076-5
[3] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[4] Hsiang, S. and Lin, Y. (2008) Application of Fuzzy Theory to Predict Deformation Behaviors of Magnesium Alloy Sheets under Hot Extrusion. Journal of Materials Processing Technology, 201, 138-144.
http://dx.doi.org/10.1016/j.jmatprotec.2007.11.222
[5] Bergh, L., Yianatos, J. and Leiva, C. (1998) Fuzzy Supervisory Control of Flotation Columns. Minerals Engineering, 11, 739-748. http://dx.doi.org/10.1016/S0892-6875(98)00059-4
[6] http://www.outotec.com/en/Products–services/Analyzers-and-automation/Zinc-refining-control-solutions/
[7] Abou, S.C. and Dao, T.-M. (2009) Fuzzy Logic Controller Based on Association Rules Mining: Application to Mineral Processing. Proceedings of the World Congress on Engineering and Computer Science, 2.
[8] Carvalho, M.T. and Durão, F. (2002) Control of a Flotation Column Using Fuzzy Logic Inference. Fuzzy Sets and Systems, 125, 121-133. http://dx.doi.org/10.1016/S0165-0114(01)00048-3
[9] Vieira, S., Sousa, J. and Durao, F. (2005) Fuzzy Modelling Strategies Applied to a Column Flotation Process. Minerals Engineering, 18, 725-729. http://dx.doi.org/10.1016/j.mineng.2004.10.008
[10] Saravani, A., Mehrshad, N. and Massinaei, M. (2014) Fuzzy-Based Modeling and Control of an Industrial Flotation Column. Chemical Engineering Communications, 201, 896-908. http://dx.doi.org/10.1080/00986445.2013.790815
[11] Petersen, J. and Dixon, D. (2007) Modelling Zinc Heap Bioleaching. Hydrometallurgy, 85, 127-143.
http://dx.doi.org/10.1016/j.hydromet.2006.09.001
[12] Bennett, C., McBride, D., Cross, M. and Gebhardt, J. (2012) A Comprehensive Model for Copper Sulphide Heap Leaching: Part 1 Basic Formulation and Validation Through Column Test Simulation. Hydrometallurgy, 127, 150-161.
http://dx.doi.org/10.1016/j.hydromet.2012.08.004
[13] Ahmadi, A., Ranjbar, M., Schaffie, M. and Petersen, J. (2012) Kinetic Modeling of Bioleaching of Copper Sulfide Concentrates in Conventional and Electrochemically Controlled Systems. Hydrometallurgy, 127, 16-23.
http://dx.doi.org/10.1016/j.hydromet.2012.06.010
[14] Leahy, M.J., Davidson, M.R. and Schwarz, M.P. (2005) A Two-Dimensional CFD Model for Heap Bioleaching of Chalcocite. ANZIAM Journal, 46, C439-C457.
[15] Gonzalez, R., Gentina, J.C. and Acevedo, F. (2004) Biooxidation of a Gold Concentrate in a Continuous Stirred Tank Reactor: Mathematical Model and Optimal Configuration. Biochemical Engineering Journal, 19, 33-42.
http://dx.doi.org/10.1016/j.bej.2003.09.007
[16] Pazouki, M., Ganjkhanlou, Y., Tofigh, A., Hosseini, M., Aghaie, E. and Ranjbar, M. (2012) Optimizing of Iron Bioleaching from a Contaminated Kaolin Clay by the Use of Artificial Neural Network. International Journal of Engineering-Transactions B: Applications, 25, 81-88.
[17] Ahmadi, A., Schaffie, M., Manafi, Z. and Ranjbar, M. (2010) Electrochemical Bioleaching of High Grade Chalcopyrite Flotation Concentrates in a Stirred Bioreactor. Hydrometallurgy, 104, 99-105.
http://dx.doi.org/10.1016/j.hydromet.2010.05.001
[18] Mendel, J.M. (1995) Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, 83, 345-377.
http://dx.doi.org/10.1109/5.364485
[19] Kasabov, N.K. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. Marcel Alencar, New York.
[20] Nguyen, H.T., Prasad, N.R., Walker, C.L. and Walker, E.A. (2010) A First Course in Fuzzy and Neural Control. CRC Press, Boca Raton.
[21] Nguyen, H.T. and Walker, E.A. (2005) A First Course in Fuzzy Logic. CRC Press, Boca Raton.    eww150115lx

Mathematical Modeling and Experimental Validation of Mixed Metal Oxide Thin Film Deposition by Spray Pyrolysis

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53136#.VLXWacnQrzE

ABSTRACT

The deposition of metal oxide films using Spray Pyrolysis Technique (SPT) is investigated through mathematical and physical modeling. A comprehensive model is developed in the processes including atomization, spray, evaporation, chemical reaction and deposition. The predicted results including particle size and film thickness are compared with the experimental data obtained in a complementary study. The predicted film thickness is in a good agreement with the measurements when the temperature is high enough for the chemical reaction to proceed. The model also adequately predicts the size distribution when the nanocrystals are well-structured at controlled temperature and concentration.

Cite this paper

Khatami, S. , Ilegbusi, O. and Trakhtenberg, L. (2015) Mathematical Modeling and Experimental Validation of Mixed Metal Oxide Thin Film Deposition by Spray Pyrolysis. Materials Sciences and Applications, 6, 68-77. doi: 10.4236/msa.2015.61009.

References

[1] Sivalingam, D., Gopalakrishnan, J.B. and Rayappan, J.B.B. (2012) Nanostructured Mixed ZnO and CdO Thin Film for Selective Ethanol Sensing. Materials Letters, 77, 117-120.
http://dx.doi.org/10.1016/j.matlet.2012.03.009
[2] Ilican, S., Caglar, Y., Caglar, M. and Yakuphanoglu, F. (2006) Electrical Conductivity, Optical and Structural Properties of Indium-Doped ZnO Nanofiber Thin Film Deposited by Spray Pyrolysis Method. Physica E: Low-Dimensional Systems and Nanostructures, 35, 131-138.
http://dx.doi.org/10.1016/j.physe.2006.07.009
[3] Kalantar-Zadeh, K. and Fry, B. (2007) Nanotechnology-Enabled Sensors. Springer, New York.
[4] Kumar, P. (2013) Magnetism and Magnetotransport in Half and over Doped Manganites Impact of Substrate Induced Strain and Polycrystalline Disorder. Ph.D. Thesis, Jaypee Institute of Information Technology, Noida.
[5] Perednis, D. and Gauckler, L.J. (2005) Thin Film Deposition Using Spray Pyrolysis. Journal of Electroceramics, 14, 103-111.
http://dx.doi.org/10.1007/s10832-005-0870-x
[6] Nakaruk, A.S.C.C. and Sorrell, C.C. (2010) Conceptual Model for Spray Pyrolysis Mechanism: Fabrication and Annealing of Titania Thin Films. Journal of Coatings Technology and Research, 7, 665-676.
http://dx.doi.org/10.1007/s11998-010-9245-6
[7] Jayanthi, G.V., Zhang, S.C. and Messing, G.L. (1993) Modeling of Solid Particle Formation during Solution Aerosol Thermolysis: The Evaporation Stage. Aerosol Science and Technology, 19, 478-490.
http://dx.doi.org/10.1080/02786829308959653
[8] Yu, H.F. and Liao, W.H. (1998) Evaporation of Solution Droplets in Spray Pyrolysis. International Journal of Heat and Mass Transfer, 41, 993-1001.
http://dx.doi.org/10.1016/S0017-9310(97)00226-3
[9] Eslamian, M., Ahmed, M. and Ashgriz, N. (2006) Modelling of Nanoparticle Formation during Spray Pyrolysis. Nanotechnology, 17, 1674.
http://dx.doi.org/10.1088/0957-4484/17/6/023
[10] Reuge, N. and Caussat, B. (2007) A Dimensionless Study of the Evaporation and Drying Stages in Spray Pyrolysis. Computers & Chemical Engineering, 31, 1088-1099.
http://dx.doi.org/10.1016/j.compchemeng.2006.09.011
[11] Huang, L., Kumar, K. and Mujumdar, A.S. (2004) Simulation of a Spray Dryer Fitted with a Rotary Disk Atomizer Using a Three-Dimensional Computational Fluid Dynamic Model. Drying Technology, 22, 1489-1515.
http://dx.doi.org/10.1081/DRT-120038737
[12] Jiang, X., Ward, T.L., Swol, F.V. and Brinker, C.J. (2010) Numerical Simulation of Ethanol-Water-NaCl Droplet Evaporation. Industrial & Engineering Chemistry Research, 49, 5631-5643.
http://dx.doi.org/10.1021/ie902042z
[13] Khatami, S.M.N., Ilegbusi, O.J. and Trakhtenberg, L. (2013) Modeling of Aerosol Spray Characteristics for Synthesis of Sensor Thin Film from Solution. Applied Mathematical Modeling, 37, 6389-6399.
http://dx.doi.org/10.1016/j.apm.2013.01.009
[14] Khatami, S.M.N. and Ilegbusi, O.J. (2012) Droplet Evaporation and Chemical Reaction in a Single Multi-Component Droplet to Synthesis Mixed-Oxide Film Using Spray Pyrolysis Method. Proceedings of the ASME 2012 International Mechanical Engineering Congress and Exposition, Houston, 9-15 November 2012, 633-638.
[15] Widiyastuti, W., Wang, W.N., Lenggoro, I.W., Iskandar, F. and Okuyama, K. (2007) Simulation and Experimental Study of Spray Pyrolysis of Polydispersed Droplets. Journal of Materials Research, 22, 1888-1898.
http://dx.doi.org/10.1557/jmr.2007.0235
[16] Filipovic, L., Selberherr, S., Mutinati, G.C., Brunet, E., Steinhauer, S., Köck, A. and Schrank, F. (2013) Modeling Spray Pyrolysis Deposition. Proceedings of the World Congress on Engineering, 2, 987-992.
[17] Blaker, K.A., Halani, A.T., Vijayakumar, P.S., Wieting, R.D. and Wong, B. (1988) Chemical Vapor Deposition of Zinc Oxide Films and Products. US Patent No. 4751149.
[18] Barnes, T.M., Leaf, J., Fry, C. and Wolden, C.A. (2005) Room Temperature Chemical Vapor Deposition of c-Axis ZnO. Journal of Crystal Growth, 274, 412-417.
http://dx.doi.org/10.1016/j.jcrysgro.2004.10.015
[19] Khatami, S.M.N., Kuruppumullage, D.N. and Ilegbusi, O.J. (2013) Characterization of Metal Oxide Sensor Thin Films Deposited by Spray Pyrolysis. Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition, San Diego, 15-21 November, Article ID: V010T11A044.
[20] Khatami, S.M.N. and Ilegbusi, O.J. 2011) Modeling of Aerosol Spray Characteristics for Synthesis of Mixed-Oxide Nanocomposite Sensor Film. Proceedings of the ASME 2011 International Mechanical Engineering Congress and Exposition, Denver, 11-17 November 2011, 581-589.
[21] Shinde, P.S. (2012) Photoelectrochemical Detoxification of Water Using Spray Deposited Oxide Semiconductor Thin Films. Ph.D. Thesis, Shivaji University, Kolhapur.
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Modeling Operational Parameters of a Reactive Electro-Dialysis Cell for Electro-Refining Anodic Scrap Copper

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51367#.VGVVbWfHRK0

ABSTRACT

This work will create an electro-dialysis cell model that has the purpose of refining anodic scrap copper—an element that currently must be returned to the copper conversion process. The cell modeling is based on Ohm’s Law, while the resulting copper deposit morphology is studied through the thickness of the layer deposited on the surface and the electric current lines traced from the anode to the cathode. The use of the model demonstrated that it is possible to effectively predict the specific energy consumption required for the refinement of the anodic scrap copper, and the morphology of the cathode obtained, with a margin of error of 9%.

Cite this paper

Cifuentes, G. , Hernández, J. , Manríquez, J. and Guajardo, N. (2014) Modeling Operational Parameters of a Reactive Electro-Dialysis Cell for Electro-Refining Anodic Scrap Copper. American Journal of Analytical Chemistry, 5, 1011-1019. doi: 10.4236/ajac.2014.515107.

References

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http://dx.doi.org/10.4067/S0717-97072009000400002
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Modeling of Deposition Process of Particulate Organic Matter (POM) with Sand on Vegetated Area in a River

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=50616#.VEcFXVfHRK0

ABSTRACT

The transport and deposition of particulate organic matter (POM) in river streams has recently received much attention as one of important ecological processes in rivers. We focused on interacted behaviors of sand particles in bed load and POM in vegetated area on sand bars. The purpose of this study is to clarify the characteristics of deposition of POM with bed load on sandbars with the riparian vegetation. A basic experiment on POM transport and deposition with vegetation is conducted in a laboratory flume. It demonstrates that several issues still remain to be future investigated. In particular, the shear due to the bed roughness in the vegetated area and the transport and deposition process of sand particles and POM are required to be described by the proper modeling which will be introduced into a simulation model of various fluvial processes. The main results of this study are that ripples are formed by bed load in riparian vegetation and POM deposition is promoted by ripple behavior. Based on these results, the POM deposition with ripples in vegetated area is described by a conceptual model which will affect various aspects in ecosystem management based on fluvial processes.

Cite this paper

Obana, M. , Jeon, H. and Tsujimoto, T. (2014) Modeling of Deposition Process of Particulate Organic Matter (POM) with Sand on Vegetated Area in a River. Journal of Water Resource and Protection, 6, 1290-1296. doi: 10.4236/jwarp.2014.614119.

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http://dx.doi.org/10.4236/jwarp.2014.610082
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