Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms

Ahmadi, Mohammad Hossein; Mohseni-Gharyehsafa, Behnam; Farzaneh–Gord, Mahmood; Jilte, Ravindra; Kumar, Ravinder; Chau, Kwok Wing

doi:10.1080/19942060.2019.1571442

Cited by 42 publications

(24 citation statements)

References 45 publications

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“…Hence applying an exact method for the identification of the relationships between related parameters to predict the thermal conductivity is a regularly problematic issue. To that end, utilization of computational methods and artificial intelligence methods such as the support vector machines, fuzzy inference systems and artificial neural networks (ANNs) which usually give us high accurate results, has been recommended by numerous researchers in different issues (Ahmadi, Ghahremannezhad, et al, 2019;Ahmadi, Mohseni-Gharyehsafa, et al, 2019;Ahmadi, Sadeghzadeh, Raffiee, & Chau, 2019;Ali Ghorbani, Kazempour, Chau, Shamshirband, & Taherei Ghazvinei, 2018;Baghban, Ahmadi, & Shahraki, 2015;Baghban, Ahmadi, Pouladi, & Amanna, 2015;Baghban, Bahadori, Mohammadi, & Behbahaninia, 2017;Baghban, Mohammadi, & Taleghani, 2017;Baghban et al, 2016;Bahadori et al, 2016;Chau, 2017;Cheng & Chau, 2002;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Wu & Chau, 2011;Yaseen, Sulaiman, Deo, & Chau, 2019). ANN is a great satisfactory approach to reach optimal solutions for difficult problems, especially in chemical fields and can help us to reduce the time and cost considerably (Abdi-Khanghah, Bemani, Naserzadeh, & Zhang, 2018).…”

Section: Introductionmentioning

confidence: 99%

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Razavi

Sabaghmoghadam

Bemani

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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An extensive variety of chemical engineering processes include the transfer of heat energy. Since increasing the effective contact surface is known as one of the popular manners to improve the efficiency of heat transfer, the attention to the nanofluids has been attracted. Due to the difficulty and high cost of an experimental study, researchers have been attracted to fast computational methods. In this work, Adaptive neuro-fuzzy inference system and least square support vector machine algorithms have been applied as a comprehensive predictive tool to forecast the nanofluids thermal conductivity in terms of diameter, temperature, the thermal conductivity of the base fluid, the thermal conductivity of nanoparticle and volume fraction. To this end, a large and comprehensive experimental databank contains 1109 data points have been collected from reliable sources. The particle swarm optimization is utilized to reach the best structures of the proposed algorithms. A comprehensive statistical and graphical investigations are carried out to prove the accuracy and ability of proposed models. In addition, the comparisons outputs indicate that the least square support vector machine algorithm has the best performance among the existing correlations and Adaptive neuro-fuzzy inference system algorithms for forecasting thermal conductivity of different nanofluids.

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Section: Introductionmentioning

confidence: 99%

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Razavi

Sabaghmoghadam

Bemani

et al. 2019

Engineering Applications of Computational Fluid Mechanics

Self Cite

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“…ANN is characterized by its structure representing the pattern of connection between nodes, connection weights, and activation function. This technique has been widely used for the prediction of nanomaterial properties (Alizadeh et al, 2017;Ahmadi et al, 2019;Razavi et al, 2019). In the present study, neural network was developed using the tool box of Visual Gene Developer software (1.9).…”

Section: Prediction Modelling Using Neural Network Architecturementioning

confidence: 99%

Use of adsorption-influencing parameters for designing the batch adsorber and neural network–based prediction modelling for the aqueous arsenate removal using combustion synthesised nano-alumina

Prabhakar

Samadder

2020

Environ Sci Pollut Res

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Removal of arsenic from water is of utmost priorities on a global scenario due to its ill effects. Therefore, in the present study, aluminium oxide nano-particles (nano-alumina) were synthesised via solution combustion method, which is self-propagating and eco-friendly in nature. Synthesised nano-alumina was further employed for arsenate removal from water. Usually, pre-oxidation of arsenite is performed for better removal of arsenic in its pentavalent form. Thus, arsenate removal as a function of influencing parameters such as initial concentration, dose, pH, temperature, and competing anions was the prime objective of the present study. The speciation analysis showed that H 2 AsO4and HAsO 4 2− were co-existing anions between pH 6 and 8, as a result of which higher removal was observed. Freundlich isotherm model was well suited for data on adsorption. At optimal temperature of 298 K, maximum monolayer adsorption capacity was found as 1401.90 μg/g. The kinetic data showed film diffusion step was the controlling mechanism. In addition, competing anions like nitrate, bicarbonate, and chloride had no major effect on arsenate removal efficiency, while phosphate and sulphate significantly reduced the removal efficiency. The negative values of thermodynamic parameters ΔH°(− 23.15 kJ/mol) established the exothermic nature of adsorption, whereas the negative values of ΔG°(

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“…Since most of the data we get is single image, it is very difficult to make an accurate calculation for camera perspective. Thus, some algorithms utilize different filters with different sizes to generate density maps [27,38]. However, different filters do not work together well, so information blending between different density maps is insufficient.…”

Section: The Processing Of Camera Perspectivementioning

confidence: 99%

One Shot Crowd Counting with Deep Scale Adaptive Neural Network

et al. 2019

Electronics

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This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly. For a crowd image with any size and resolution, our algorithm can output the density map of the crowd image by end-to-end method and finally estimate the number of the crowd in the image. The proposed DSA-CNN consists of two parts: the seven layers CNN network structure and DSA modules. In order to ensure the proposed method is robust to camera perspective effect, DSA-CNN has adopted different sizes of filters in the network and combines them ingeniously. In order to reduce the depth of the data to increase the speed of training, the proposed method utilized 1 × 1 filter in DSA module. To validate the effectiveness of the proposed model, we conducted comparative experiments on four popular public datasets (ShanghiTech dataset, UCF_CC_50 dataset, WorldExpo’10 dataset and UCSD dataset). We compare the proposed method with other well-known algorithms on the MAE and MSE indicators, such as MCNN, Switching-CNN, CSRNet, CP-CNN and Cascaded-MTL. Experimental results show that the proposed method has excellent performance. In addition, we found that the proposed model is easily trained, which further increases the usability of the proposed model.

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Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms

Abstract: wing Chau (2019) Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP,

Cited by 42 publications

References 45 publications

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Use of adsorption-influencing parameters for designing the batch adsorber and neural network–based prediction modelling for the aqueous arsenate removal using combustion synthesised nano-alumina

One Shot Crowd Counting with Deep Scale Adaptive Neural Network

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