LAPSE:2020.0968
Published Article
LAPSE:2020.0968
An Artificial Intelligence Approach to Predict the Thermophysical Properties of MWCNT Nanofluids
September 15, 2020
Experimental data of thermal conductivity, thermal stability, specific heat capacity, viscosity, UV−vis (light transmittance) and FTIR (light absorption) of Multiwalled Carbon Nanotubes (MWCNTs) dispersed in glycols, alcohols and water with the addition of sodium dodecylbenzene sulfonate (SDBS) surfactant for 0.5 wt % concentration along a temperature range of 25 °C to 200 °C were verified using Artificial Neural Networks (ANNs). In this research, an ANN approach was proposed using experimental datasets to predict the relative thermophysical properties of the tested nanofluids in the available literature. Throughout the designed network, 65% and 25% of data points were comprehended in the training and testing set while the other 10% was utilized as a validation set. The parameters such as temperature, concentration, size and time were considered as inputs while the thermophysical properties were considered as outputs to develop ANN models of further predictions with unseen datasets. The results found to be satisfactory as the (coefficient of determination) R2 values are close to 1.0. The predicted results of the nanofluids’ thermophysical properties were then validated with experimental dataset values. The validation plots of all individual samples for all properties were graphically generated. A comparison study was conducted for the robustness of the proposed approach. This work may help to reduce the experimental time and cost in the future.
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Keywords
Artificial Neural Networks, experimental data, nanofluids, prediction, thermophysical properties
Subject
Suggested Citation
Bakthavatchalam B, Shaik NB, Hussain PB. An Artificial Intelligence Approach to Predict the Thermophysical Properties of MWCNT Nanofluids. (2020). LAPSE:2020.0968
Author Affiliations
Bakthavatchalam B: Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia
Shaik NB: Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia [ORCID]
Hussain PB: Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia
Shaik NB: Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia [ORCID]
Hussain PB: Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia
Journal Name
Processes
Volume
8
Issue
6
Article Number
E693
Year
2020
Publication Date
2020-06-14
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr8060693, Publication Type: Journal Article
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Published Article
LAPSE:2020.0968
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External Link
doi:10.3390/pr8060693
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Version History
[v1] (Original Submission)
Sep 15, 2020
Verified by curator on
Sep 15, 2020
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v1
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https://psecommunity.org/LAPSE:2020.0968
Original Submitter
Calvin Tsay
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