|تعداد مشاهده مقاله||111,640,475|
|تعداد دریافت فایل اصل مقاله||86,260,221|
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|
|مقاله 5، دوره 51، شماره 1، شهریور 2018، صفحه 43-52 اصل مقاله (1.09 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/jufgnsm.01.06|
|Mehrdad Mahdavi jafari؛ Gholam Khayati*|
|Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, Kerman, Iran|
|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.|
|Silica Particle؛ Fuzzy inference system؛ Simulated Annealing؛ Artificial Neural Network؛ Process Parameters, Sol-Gel Methods|
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.
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.
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-.
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.
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.
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.
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.
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.
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.
تعداد مشاهده مقاله: 1,021
تعداد دریافت فایل اصل مقاله: 1,079