Modeling and optimization the effective parameters of nanofluid heat transfer performance using artificial neural network and genetic algorithm method

Pourpasha, Hadi; Farshad, Pedram; Heris, Saeed Zeinali

doi:10.1016/j.egyr.2021.10.121

Cited by 28 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

z www.nature.com/scientificreports/ catalysis, electrochemical treatment, and Fenton's method 8,9 . Among the mentioned methods, catalysis is the greater way to eliminate pollution due to its properties such as time-saving, high satisfaction, and economical.Recently, nanoparticles have been used in various applications such as heat transfer [10][11][12][13][14] , lubrication [15][16][17] , CO 2 capture [18][19][20] , textile manufacturing [21][22][23][24][25] , photocatalyst, water treatment systems 26 , and catalysis 27 . Magnetic ferrites are used in fundamental research fields due to their electrical, magnetic, chemical/thermal stability properties and spinal properties (MFe 2 O 4 ; M = Ni, Co, Fe, Cu, Zn, etc.)

…”

mentioning

confidence: 99%

“…Recently, nanoparticles have been used in various applications such as heat transfer [10][11][12][13][14] , lubrication [15][16][17] , CO 2 capture [18][19][20] , textile manufacturing [21][22][23][24][25] , photocatalyst, water treatment systems 26 , and catalysis 27 . Magnetic ferrites are used in fundamental research fields due to their electrical, magnetic, chemical/thermal stability properties and spinal properties (MFe 2 O 4 ; M = Ni, Co, Fe, Cu, Zn, etc.)…”

mentioning

confidence: 99%

See 1 more Smart Citation

The catalytic performance of CuFe2O4@CQD nanocomposite as a high-perform heterogeneous nanocatalyst in nitroaniline group reduction

Naghash‐Hamed¹,

Arsalani

Mousavi

2023

Sci Rep

View full text Add to dashboard Cite

In this study, we fabricated an economical, non-toxic, and convenient magnetic nanocomposite of CuFe2O4 nanoparticles (NPs)/carbon quantum dots (CQDs) of citric acid via the co-precipitation method. Afterward, obtained magnetic nanocomposite was used as a nanocatalyst to reduce the ortho-nitroaniline (o-NA) and para-nitroaniline (p-NA) using a reducer agent of sodium borohydride (NaBH4). To investigate the functional groups, crystallite, structure, morphology, and nanoparticle size of the prepared nanocomposite, FT-IR, XRD, TEM, BET, and SEM were employed. The catalytic performance of the nanocatalyst was experimentally evaluated based on the ultraviolet–visible absorbance to assess the reduction of o-NA and p-NA. The acquired outcomes illustrated that the prepared heterogeneous catalyst significantly enhanced the reduction of o-NA and p-NA substrates. The analysis of the absorption showed a remarkable decrease for ortho-NA and para-NA at λmax = 415 nm in 27 s and λmax = 380 nm in 8 s, respectively. The constant rate (kapp) of ortho-NA and para-NA at the stated λmax was 8.39 × 10–2 s−1 and 5.48 × 10–1 s−1. The most highlighted result of this work was that the CuFe2O4@CQD nanocomposite fabricated from citric acid performed better than absolute CuFe2O4 NPs, since nanocomposite containing CQDs had a more significant impact than copper ferrite NPs.

show abstract

“…

…”

mentioning

confidence: 99%

mentioning

confidence: 99%

The catalytic performance of CuFe2O4@CQD nanocomposite as a high-perform heterogeneous nanocatalyst in nitroaniline group reduction

Naghash‐Hamed¹,

Arsalani

Mousavi

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…erefore, ANN is especially used in classification, prediction, and especially handling complex and noisy data [38]. Moreover, a neural network with one hidden layer is sufficient and more common than multiple hidden layers-based networks [39,40].…”

Section: Feedforward Backpropagation Processmentioning

confidence: 99%

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Sushmi

Subbulekshmi

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Sun is the sustainable and abundantly available alternative resource on the planet Earth. The uncertain nature of the source caused due to various environmental factors increases the need to quantify the irradiation potential at the targeted location, especially for power smoothing processes, solar-fed electrical applications, and the utility grid. The irradiation measuring devices lead to appropriate maintenance and more expenses and are ineffective under dynamically varying irradiation conditions. Hence, a robust ML-based forecasting technique is sufficient to predict GHI under dynamically varying irradiation profiles caused by various environmental factors. Therefore, this study focuses on short-term (hourly) GHI forecasting using the FFBP-LM-ANN approach. In most of the related articles, the selection of independent variables and designing the network is being a challenge and obstacle in attaining fast and efficient prediction. Such a scenario increases the overall computation complexity and understanding. Hence, this study was intended to use a simple process to crucially select 5 environmental factors and to optimally choose the number of neurons for designing the network for better irradiation prediction. Thus, the network is designed with an input layer of 5 selected environmental variables and one hidden layer with 20 neurons, and hourly GHI in the output layer is performed. The approach is trained using the Levenberg-Marquardt BP algorithm in MATLAB toolbox, with the help of a 4-year dataset received for the rooftop panels of VIT University, Chennai, from NREL. The performance of the model during training and testing was validated and analyzed using 9 performance matrices. As a result, FFBP-LM-ANN satisfactorily predicts hourly GHI for the targeted location based on rRMSE of 7.21%, MAE of 0.042, MBE of 0.000492, R of 0.96, MAPE of 44.4%, MRE of 9.5%, and NSE of 94% obtained under testing process. Moreover, the model has performed much better when compared with 9 related models that exist in literature based on the input variables used, network design, epoch, and RMSE. Subsequently, such a predictor will be adaptable and more suitable for the explicit prediction of hourly GHI for different regions across the world having varying climatic conditions, since the study model is designed for locations facing robust climatic nature. More importantly, the designed model is superior with only environmental variables, which are rarely found in the article, rather than geographical variables, which are predominantly used in most of the related literature.

show abstract

“…In the three-layer network, the following approximate relationship was found between the number of neurons in the hidden layer N and the number of neurons in the input layer M (Eq. (15) ) ( Pourpasha et al, 2021 ; Abilov and Zeybek, 2000 ; Liu et al, 2001 ):

21

…”

Section: Development Of the Mathematical Modelmentioning

confidence: 99%

Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization

Zhao

Jin

et al. 2022

Heliyon

View full text Add to dashboard Cite

Modeling and optimization the effective parameters of nanofluid heat transfer performance using artificial neural network and genetic algorithm method

Cited by 28 publications

References 41 publications

The catalytic performance of CuFe2O4@CQD nanocomposite as a high-perform heterogeneous nanocatalyst in nitroaniline group reduction

The catalytic performance of CuFe2O4@CQD nanocomposite as a high-perform heterogeneous nanocatalyst in nitroaniline group reduction

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization

Contact Info

Product

Resources

About