Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)

In the present work, response surface methodology (RSM) using the miscellaneous design model was performed to optimize thermal properties of Cellulose nonocrystal (CNC) and hybrid of cellulose nanocrystal-copper (II) oxide (CNC-CuO) nanolubricant. Influence of temperature, concentration and type of...

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Main Authors: Sakinah, Hisham, K., Kadirgama, D., Ramasamy, M., Samykano, W. S., Wan Harun, R., Saidur
格式: Conference or Workshop Item
语言:English
出版: IOP Publishing 2019
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在线阅读:http://umpir.ump.edu.my/id/eprint/29819/1/1.%20Statistical%20approach%20for%20prediction%20of%20thermal%20properties%20of%20CNC.pdf
http://umpir.ump.edu.my/id/eprint/29819/
https://doi.org/10.1088/1757-899X/788/1/012016
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spelling my.ump.umpir.298192020-11-13T03:01:42Z http://umpir.ump.edu.my/id/eprint/29819/ Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM) Sakinah, Hisham K., Kadirgama D., Ramasamy M., Samykano W. S., Wan Harun R., Saidur TJ Mechanical engineering and machinery In the present work, response surface methodology (RSM) using the miscellaneous design model was performed to optimize thermal properties of Cellulose nonocrystal (CNC) and hybrid of cellulose nanocrystal-copper (II) oxide (CNC-CuO) nanolubricant. Influence of temperature, concentration and type of nanolubricant is used to develop empirical mathematical model by using Response Surface Methodology (RSM) based on Central Composite Design (CCD) with aid of Minitab 18 statistical analysis software. The significance of the developed empirical mathematical model is validated by using Analysis of variance (ANOVA). In order to produce second-order polynomial equations for target outputs including thermal conductivity and viscosity, 26 experiments were performed. According to the results, the predicted values were in sensible agreement with the experimental data. In other words, more than 80% of thermal conductivity and specific heat capacity variations of the nanolubricant could be predicted by the models, which shows the applied model is precise. The predicted optimized value shown in the optimization plot is 0.1463 for thermal conductivity and 1.6311 for specific heat capacity. The relevant parameters such as concentration, temperature and type of nanolubricant are 81.51, 0.1, and the categorical factor is CNC-CuO. The composite shown in the plot is 0.6531. The validation result wit experimental as shown in indicate that the model can predict the optimal experimental conditions well. IOP Publishing 2019 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/29819/1/1.%20Statistical%20approach%20for%20prediction%20of%20thermal%20properties%20of%20CNC.pdf Sakinah, Hisham and K., Kadirgama and D., Ramasamy and M., Samykano and W. S., Wan Harun and R., Saidur (2019) Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM). In: IOP Conference Series: Materials Science and Engineering, 5th International Conference on Mechanical Engineering Research (ICMER 2019), 30-31 July 2019 , Kuantan, Malaysia. pp. 1-14., 788 (012016). ISSN 1757-899X https://doi.org/10.1088/1757-899X/788/1/012016
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Sakinah, Hisham
K., Kadirgama
D., Ramasamy
M., Samykano
W. S., Wan Harun
R., Saidur
Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)
description In the present work, response surface methodology (RSM) using the miscellaneous design model was performed to optimize thermal properties of Cellulose nonocrystal (CNC) and hybrid of cellulose nanocrystal-copper (II) oxide (CNC-CuO) nanolubricant. Influence of temperature, concentration and type of nanolubricant is used to develop empirical mathematical model by using Response Surface Methodology (RSM) based on Central Composite Design (CCD) with aid of Minitab 18 statistical analysis software. The significance of the developed empirical mathematical model is validated by using Analysis of variance (ANOVA). In order to produce second-order polynomial equations for target outputs including thermal conductivity and viscosity, 26 experiments were performed. According to the results, the predicted values were in sensible agreement with the experimental data. In other words, more than 80% of thermal conductivity and specific heat capacity variations of the nanolubricant could be predicted by the models, which shows the applied model is precise. The predicted optimized value shown in the optimization plot is 0.1463 for thermal conductivity and 1.6311 for specific heat capacity. The relevant parameters such as concentration, temperature and type of nanolubricant are 81.51, 0.1, and the categorical factor is CNC-CuO. The composite shown in the plot is 0.6531. The validation result wit experimental as shown in indicate that the model can predict the optimal experimental conditions well.
format Conference or Workshop Item
author Sakinah, Hisham
K., Kadirgama
D., Ramasamy
M., Samykano
W. S., Wan Harun
R., Saidur
author_facet Sakinah, Hisham
K., Kadirgama
D., Ramasamy
M., Samykano
W. S., Wan Harun
R., Saidur
author_sort Sakinah, Hisham
title Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)
title_short Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)
title_full Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)
title_fullStr Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)
title_full_unstemmed Statistical approach for prediction of thermal properties of CNC and CNC-CuO nanolubricant using Response Surface Methodology (RSM)
title_sort statistical approach for prediction of thermal properties of cnc and cnc-cuo nanolubricant using response surface methodology (rsm)
publisher IOP Publishing
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/29819/1/1.%20Statistical%20approach%20for%20prediction%20of%20thermal%20properties%20of%20CNC.pdf
http://umpir.ump.edu.my/id/eprint/29819/
https://doi.org/10.1088/1757-899X/788/1/012016
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score 13.250246