Study on prediction fly ash generation using statistical method
This study present of fly ash generation at generated one the power plant in Malaysia. The main purpose of this research to predict the generation of fly ash in future years by a method. This prediction is important so that fly ash generated could be used in a beneficial way. Prediction of fly ash w...
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American Institute of Physics Inc.
2023
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my.uniten.dspace-235362023-05-29T14:50:08Z Study on prediction fly ash generation using statistical method Zahari N.M. Mohamad D. Arenandan V. Beddu S. Sadon S.N. Syamsir A. Kamal N.L.M. Zainoodin M.M. Nadhirah A. 54891672300 57200335404 57209317359 55812080500 57200334298 57195320482 56239107300 57202388764 56353119500 This study present of fly ash generation at generated one the power plant in Malaysia. The main purpose of this research to predict the generation of fly ash in future years by a method. This prediction is important so that fly ash generated could be used in a beneficial way. Prediction of fly ash was done by using two types of software which are Neural Network Toolbox, MATLAB and IBM SPSS Statistics 23, Linear Regression. Among these two methods, IBM SPSS Statistics 23, Linear Regression is found to be the most effective way to predict the generation of fly ash in the future by using five year's best fit linear regression equation compared to Neural Network Toolbox, MATLAB. � 2018 Author(s). Final 2023-05-29T06:50:08Z 2023-05-29T06:50:08Z 2018 Conference Paper 10.1063/1.5066994 2-s2.0-85057225486 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057225486&doi=10.1063%2f1.5066994&partnerID=40&md5=b07e2b62cbb1a968e9cc5b17ff10d9bb https://irepository.uniten.edu.my/handle/123456789/23536 2031 20038 All Open Access, Bronze American Institute of Physics Inc. Scopus |
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This study present of fly ash generation at generated one the power plant in Malaysia. The main purpose of this research to predict the generation of fly ash in future years by a method. This prediction is important so that fly ash generated could be used in a beneficial way. Prediction of fly ash was done by using two types of software which are Neural Network Toolbox, MATLAB and IBM SPSS Statistics 23, Linear Regression. Among these two methods, IBM SPSS Statistics 23, Linear Regression is found to be the most effective way to predict the generation of fly ash in the future by using five year's best fit linear regression equation compared to Neural Network Toolbox, MATLAB. � 2018 Author(s). |
| author2 |
54891672300 |
| author_facet |
54891672300 Zahari N.M. Mohamad D. Arenandan V. Beddu S. Sadon S.N. Syamsir A. Kamal N.L.M. Zainoodin M.M. Nadhirah A. |
| format |
Conference Paper |
| author |
Zahari N.M. Mohamad D. Arenandan V. Beddu S. Sadon S.N. Syamsir A. Kamal N.L.M. Zainoodin M.M. Nadhirah A. |
| spellingShingle |
Zahari N.M. Mohamad D. Arenandan V. Beddu S. Sadon S.N. Syamsir A. Kamal N.L.M. Zainoodin M.M. Nadhirah A. Study on prediction fly ash generation using statistical method |
| author_sort |
Zahari N.M. |
| title |
Study on prediction fly ash generation using statistical method |
| title_short |
Study on prediction fly ash generation using statistical method |
| title_full |
Study on prediction fly ash generation using statistical method |
| title_fullStr |
Study on prediction fly ash generation using statistical method |
| title_full_unstemmed |
Study on prediction fly ash generation using statistical method |
| title_sort |
study on prediction fly ash generation using statistical method |
| publisher |
American Institute of Physics Inc. |
| publishDate |
2023 |
| _version_ |
1806426137338839040 |
| score |
13.211869 |