scholarly journals Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Parvaneh Shabanzadeh ◽  
Norazak Senu ◽  
Kamyar Shameli ◽  
Maryam Mohaghegh Tabar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silver Nanoparticles ◽  
Reaction Temperature ◽  
Chemical Reduction ◽  
Size Control ◽  
Design Parameters ◽  
Ag Nps ◽  
Factors Affecting ◽  
Artificial Neural

Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

Artificial Neural Network Modelling of Green Synthesised Silver Nanoparticles in Bentonite/Starch Bio-Nanocomposite

2018 ◽  
Vol 14 (3) ◽  
pp. 239-251 ◽  
Author(s):  
Anupama Thapliyal ◽  
Roop Krishen Khar ◽  
Amrish Chandra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silver Nanoparticles ◽  
Mean Square Error ◽  
Reduction Method ◽  
Mean Square ◽  
Ann Model ◽  
Ag Nps ◽  
Feed Forward ◽  
Artificial Neural

Background: In this study, computational Artificial Neural Network (ANN) model is applied for optimisation and evaluation of silver nanoparticles (AgNPs) size in the bionanocomposite matrix. The primary purpose of this study is used a feed-forward ANN model to create a connection between the output as the size of Ag–NPs, with four inputs variables, including AgNO3 concentration, the weight percentage of starch, Bentonite amount and Gallic acid concentration. Method: Silver nanoparticles were synthesised via biogenic green reduction method. The fast Levenberg– Marquardt (LM) backpropagation algorithm applied for the training of ANN model in this research. The optimised ANN is a multilayer perceptron (MLP) which is a kind of feed forward (4- 10-1) network has an input layer with 4 nodes, hidden layers with 10 neurones, and an output layer with 1 node found a fitness function. Results: The output results of developed computational ANN model were compared to its predictive values of the size of silver nanoparticles regarding two statistical parameters, the coefficient of determination (R2) and mean square error (MSE) of data set. It observed that ANN predicted values are close to the actual values and well fitted to the data. The mean square error(MSE) is 0.03, and a regression is about 1. Conclusion: AgNO3 concentration has the most likely factor affecting the size of silver nanoparticles (Ag–NPs) and this makes possible to develop a green reduction method for the preparation of silver nanoparticles. This study confirms that employing ANN method with LM feed forward (4-10-1) network is a useful tool with cost-effective for predicting the results of analysis and modelling of the chemical reactions.

Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network

RSC Advances ◽  
2015 ◽  
Vol 5 (106) ◽  
pp. 87277-87285 ◽  
Author(s):  
Parvaneh Shabanzadeh ◽  
Rubiyah Yusof ◽  
Kamyar Shameli
Keyword(s):  
Neural Network ◽  
Aqueous Solution ◽  
Artificial Neural Network ◽  
Silver Nanoparticles ◽  
Silver Nitrate ◽  
Water Extract ◽  
Vitex Negundo ◽  
Ag Nps ◽  
Artificial Neural

In this study silver nanoparticles (Ag-NPs) are biosynthesized from silver nitrate aqueous solution through a simple and eco-friendly route using water extract ofVitex negundoL. (V. negundo) which acted as a reductant and stabilizer simultaneously.

scholarly journals Size Control of Synthesized Silver Nanoparticles by Simultaneous Chemical Reduction and Laser Fragmentation in Origanum majorana Extract: Antibacterial Application

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2326
Author(s):  
Entesar Ali Ganash ◽  
Reem Mohammad Altuwirqi
Keyword(s):  
Silver Nanoparticles ◽  
Irradiation Time ◽  
Chemical Reduction ◽  
Size Control ◽  
Reduction Process ◽  
Laser Wavelength ◽  
X Ray Diffraction ◽  
Ag Nps ◽  
Transmission Electron ◽  
Origanum Majorana

In this work, silver nanoparticles (Ag NPs) were synthesized using a chemical reduction approach and a pulsed laser fragmentation in liquid (PLFL) technique, simultaneously. A laser wavelength of 532 nm was focused on the as produced Ag NPs, suspended in an Origanum majorana extract solution, with the aim of controlling their size. The effect of liquid medium concentration and irradiation time on the properties of the fabricated NPs was studied. While the X-ray diffraction (XRD) pattern confirmed the existence of Ag NPs, the UV–Vis spectrophotometry showed a significant absorption peak at about 420 nm, which is attributed to the characteristic surface plasmon resonance (SPR) peak of the obtained Ag NPs. By increasing the irradiation time and the Origanum majora extract concentration, the SPR peak shifted toward a shorter wavelength. This shift indicates a reduction in the NPs’ size. The effect of PLFL on size reduction was clearly revealed from the transmission electron microscopy images. The PLFL technique, depending on experimental parameters, reduced the size of the obtained Ag NPs to less than 10 nm. The mean zeta potential of the fabricated Ag NPs was found to be greater than −30 mV, signifying their stability. The Ag NPs were also found to effectively inhibit bacterial activity. The PLFL technique has proved to be a powerful method for controlling the size of NPs when it is simultaneously associated with a chemical reduction process.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2020 ◽  
pp. 1632-1649
Author(s):  
Veronica Chan ◽  
Christine W. Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

scholarly journals A critical evaluation of the methods for the determination of required volumes for detention tank

2018 ◽  
Vol 45 ◽  
pp. 00088 ◽  
Author(s):  
Mariusz Starzec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Catchment Area ◽  
Critical Evaluation ◽  
Design Parameters ◽  
Method Of Calculation ◽  
Rainfall Duration ◽  
Detention Tank ◽  
Artificial Neural

Simplified methods allow a straightforward and quick determination of parameters of interest. A simplified method of calculation to be used must provide sufficiently accurate simulation results. This paper presents the results of tests completed to evaluate the effects of the parameters which describe a sewer catchment area and network on the value of Tp, a parameter applied in the Dziopak method [18]. The results of 2997 hydrodynamic simulations allowed to formulate an artificial neural network the application of which enabled the determination of the value of Tp dependent on the design parameters of a sewer catchment area and network. The artificial neural network had a very low error R2 = 0.9972 between the expected and determined values of Tp. The completed tests indicated a relationship by which an increase of the rainfall duration, a parameter used in the dimensioning of detention tank, is concomitant to an increase in the value of Tp. The calculations made so far included an assumption that the Tp value is constant irrespective of the design rainfall duration for the dimensioning of detention tank; this assumption has led to gross calculation errors. The paper also provides proof that the inclusion of these relationships allows a more precise determination of the service volume required for a multi-chamber detention tank.

CFD-based blade shape optimization of MGT-70(3)axial flow compressor

2019 ◽  
Vol 30 (6) ◽  
pp. 3307-3321 ◽  
Author(s):  
Mohammad Reza Pakatchian ◽  
Hossein Saeidi ◽  
Alireza Ziamolki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Optimization ◽  
Fluid Dynamic ◽  
Axial Compressor ◽  
Axial Flow ◽  
Design Parameters ◽  
Content Type ◽  
Wide Range ◽  
Artificial Neural

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

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