Home Articles List Article Information
Journal of Ultrafine Grained and Nanostructured Materials
Journal Archive
Volume Volume 51 (2018)
Issue Issue 1
June 2018, Page 1-95
Volume Volume 50 (2017)
Volume Volume 49 (2016)
Volume Volume 48 (2015)
Volume Volume 47 (2014)
Volume Volume 46 (2013)
Volume Volume 45 (2012)
Mahdavi jafari, M., Khayati, G. (2018). Adaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm. Journal of Ultrafine Grained and Nanostructured Materials, 51(1), 43-52. doi: 10.22059/jufgnsm.01.06
Mehrdad Mahdavi jafari; Gholam Khayati. "Adaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm". Journal of Ultrafine Grained and Nanostructured Materials, 51, 1, 2018, 43-52. doi: 10.22059/jufgnsm.01.06
Mahdavi jafari, M., Khayati, G. (2018). 'Adaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm', Journal of Ultrafine Grained and Nanostructured Materials, 51(1), pp. 43-52. doi: 10.22059/jufgnsm.01.06
Mahdavi jafari, M., Khayati, G. Adaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm. Journal of Ultrafine Grained and Nanostructured Materials, 2018; 51(1): 43-52. doi: 10.22059/jufgnsm.01.06

Adaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm

Article 5, Volume 51, Issue 1, June 2018, Page 43-52  XML PDF (1115 K)
Document Type: Research Paper
DOI: 10.22059/jufgnsm.01.06
Authors
Mehrdad Mahdavi jafari; Gholam Khayati
Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
In this study, Back-propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied to estimate the particle size of silica prepared by sol-gel technique. Simulated annealing algorithm (SAA) employed to determine the optimum practical parameters of the silica production. Accordingly, the process parameters, i.e. tetraethyl orthosilicate (TEOS), H2O and NH3 were introduced to BPNN and ANFIS methods. Average mean absolute percentage error (MAPE) and correlation relation (R) indexes were chosen as criteria to estimate the simulation error. Comparison of proposed optimum condition and the experimental data reveal that the ANFIS/SAA strategies are powerful techniques to find the optimal practical conditions with the minimum particles size of silica prepared by sol-gel technique and the accuracy of ANFIS model was higher than the results of ANN. Moreover, sensitivity analysis was employed to determine the effect of each practical parameter on the size of silica nano particles. The results showed that the water content and TEOS have the maximum and minimum effect on the particle size of silica, respectively. Since, water acts as diluent and synthesis of monodisperse silica in diluent solution will decrease the growth probability of nucleate, leading to a the lower silica particle size.
Keywords
Silica Particle; Fuzzy Inference System; simulated annealing; Artificial neural network; Process Parameters, Sol-Gel Methods
References

1. Mansouri I, Kisi O. Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Composites Part B: Engineering. 2015;70:247-55.

2. Vassilopoulos AP, Bedi R. Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates. Computational Materials Science. 2008;43(4):1086-93.

3. Mansouri I, Shariati M, Safa M, Ibrahim Z, Tahir MM, Petković D. Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. Journal of Intelligent Manufacturing. 2017.

4. Xia Y, Gates B, Yin Y, Lu Y. Monodispersed Colloidal Spheres: Old Materials with New Applications. Advanced Materials. 2000;12(10):693-713.

5. Boehm H-P. The Chemistry of Silica. Solubility, Polymerization, Colloid and Surface Properties, and Biochemistry. VonR. K. Iler. John Wiley and Sons, Chichester 1979. XXIV, 886 S., geb. £ 39.50. Angewandte Chemie. 1980;92(4):328-.

6. Stöber W, Fink A, Bohn E. Controlled growth of monodisperse silica spheres in the micron size range. Journal of Colloid and Interface Science. 1968;26(1):62-9.

7. Mozaffari S, Li W, Thompson C, Ivanov S, Seifert S, Lee B, et al. Colloidal nanoparticle size control: experimental and kinetic modeling investigation of the ligand–metal binding role in controlling the nucleation and growth kinetics. Nanoscale. 2017;9(36):13772-85.

8. Mozaffari S, Tchoukov P, Mozaffari A, Atias J, Czarnecki J, Nazemifard N. Capillary driven flow in nanochannels – Application to heavy oil rheology studies. Colloids and Surfaces A: Physicochemical and Engineering Aspects. 2017;513:178-87.

9. Mozaffari S, Tchoukov P, Atias J, Czarnecki J, Nazemifard N. Effect of Asphaltene Aggregation on Rheological Properties of Diluted Athabasca Bitumen. Energy & Fuels. 2015;29(9):5595-9.

10. Reza Khayati G, Dalvand H, Darezereshki E, Irannejad A. A facile method to synthesis of CdO nanoparticles from spent Ni–Cd batteries. Materials Letters. 2014;115:272-4.

11. Dalvand H, Reza Khayati G, Darezereshki E, Irannejad A. A facile fabrication of NiO nanoparticles from spent Ni–Cd batteries. Materials Letters. 2014;130:54-6.

12. Hoshyar R, Khayati GR, Poorgholami M, Kaykhaii M. A novel green one-step synthesis of gold nanoparticles using crocin and their anti-cancer activities. Journal of Photochemistry and Photobiology B: Biology. 2016;159:237-42.

13. Bogush GH, Tracy MA, Zukoski CF. Preparation of monodisperse silica particles: Control of size and mass fraction. Journal of Non-Crystalline Solids. 1988;104(1):95-106.

14. Mansouri I, Gholampour A, Kisi O, Ozbakkaloglu T. Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques. Neural Computing and Applications. 2016;29(3):873-88.

15. Lin YC, Zhang J, Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel. Computational Materials Science. 2008;43(4):752-8.

16. Mousavi Anijdan SH, Bahrami A. A new method in prediction of TCP phases formation in superalloys. Materials Science and Engineering: A. 2005;396(1-2):138-42.

17. Artificial Neural Network Based Prediction Hardness of Al2024-Multiwall Carbon Nanotube Composite Prepared by Mechanical Alloying. International Journal of Engineering. 2016;29(12).

18. Mansouri I, Ozbakkaloglu T, Kisi O, Xie T. Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques. Materials and Structures. 2016;49(10):4319-34.

19. Sargolzaei J, Ahangari B. Thermal Behavior Prediction of MDPE Nanocomposite/Cloisite Na[sup +] Using Artificial Neural Network and Neuro-Fuzzy Tools. Journal of Nanotechnology in Engineering and Medicine. 2010;1(4):041012.

20. Jorjani E, Chehreh Chelgani S, Mesroghli S. Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel. 2008;87(12):2727-34.

21. Modeling and Optimization of Roll-bonding Parameters for Bond Strength of Ti/Cu/Ti Clad Composites by Artificial Neural Networks and Genetic Algorithm. International Journal of Engineering. 2017;30(12).

22. M. Mahdavi Jafari, S. Soroushian, G.R. Khayati, Hardness Optimization for Al6061-MWCNT Nanocomposite Prepared by Mechanical Alloying Using Artificial Neural Networks and Genetic Algorithm, Journal of Ultrafine Grained and Nanostructured Materials (2017) ; 50(1):23-32.

23. Khalifehzadeh R, Forouzan S, Arami H, Sadrnezhaad SK. Prediction of the effect of vacuum sintering conditions on porosity and hardness of porous NiTi shape memory alloy using ANFIS. Computational Materials Science. 2007;40(3):359-65.

24. Buragohain M, Mahanta C. A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing. 2008;8(1):609-25.

25. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies. 1975;7(1):1-13.

26. Abraham A. Adaptation of Fuzzy Inference System Using Neural Learning. Fuzzy Systems Engineering: Springer Berlin Heidelberg; 2005. p. 53-83.

27. Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. 1993;23(3):665-85.

28. Bard J. A Review of: “Engineering Optimization: Theory and Practice, Third Edition”Singiresu S. Rao John Wiley & Sons, Inc., 1996, 903 pp., $95.00, ISBN 0471550345. IIE Transactions. 1997;29(9):802-3.

29. Zare M, Vahdati Khaki J. Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models. Materials & Design. 2012;38:26-31.

30. Satoh T, Akitaya M, Konno M, Saito S. Particle Size Distributions Produced by Hydrolysis and Condensation of Tetraethylorthosilicate. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN. 1997;30(4):759-62.

Statistics
Article View: 132
PDF Download: 105