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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IOP Publishing
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/29819/ http://umpir.ump.edu.my/id/eprint/29819/1/1.%20Statistical%20approach%20for%20prediction%20of%20thermal%20properties%20of%20CNC.pdf |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:56:04Z |
| format | Conference or Workshop Item |
| id | ump-29819 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:56:04Z |
| publishDate | 2019 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1088/1757-899X/788/1/012016 |
| 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) |
| title | 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_short | 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) |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/29819/ http://umpir.ump.edu.my/id/eprint/29819/ http://umpir.ump.edu.my/id/eprint/29819/1/1.%20Statistical%20approach%20for%20prediction%20of%20thermal%20properties%20of%20CNC.pdf |