Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning
Compressor performance is being evaluated based on its power consumption and other operational parameters to meet load demand efficiently while consuming less power. Without proper correlation with other operational data, it is difficult to predict future power consumption that may lead to a low per...
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| Format: | Article |
| Language: | en |
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Italian Association of Chemical Engineering - AIDIC
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29546/2/02190311220251657362857.pdf http://eprints.utem.edu.my/id/eprint/29546/ https://www.cetjournal.it/cet/25/122/040.pdf |
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| author | Zulkafli, Nur Izyan Hashim, Muhammad Fikri Sulaima, Mohamad Fani Jali, Mohd Hafiz Ahmad Izzuddin, Tarmizi Jayiddin, Nur Saleha Md Lasin, Azmi Iskandar, M Tarmidzi |
| author_facet | Zulkafli, Nur Izyan Hashim, Muhammad Fikri Sulaima, Mohamad Fani Jali, Mohd Hafiz Ahmad Izzuddin, Tarmizi Jayiddin, Nur Saleha Md Lasin, Azmi Iskandar, M Tarmidzi |
| author_sort | Zulkafli, Nur Izyan |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Compressor performance is being evaluated based on its power consumption and other operational parameters to meet load demand efficiently while consuming less power. Without proper correlation with other operational data, it is difficult to predict future power consumption that may lead to a low performance of compressors. The Multiple Linear Regression (MLR) analysis in Altair AI Studio software is being used as a model to predict power consumption for four compressors with two different models by considering mass flow rate, suction and discharge temperature, and pressure as its dependent variables. The set of data has been split into two, which are training and testing, at a ratio of 90:10, respectively. This study resulted in a low percentage difference between the predicted and actual power consumption of those four compressors, which are 1.46 %, 1.40 %, 2.00 %, and 2.25 % for Compressor 1, Compressor 2, Compressor 3, and Compressor 4, respectively. The MLR of the compressor power consumption model can be utilized to predict its future power consumption to move towards more sustainable and low-carbon emissions. |
| format | Article |
| id | my.utem.eprints-29546 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Italian Association of Chemical Engineering - AIDIC |
| record_format | eprints |
| spelling | my.utem.eprints-295462026-02-23T02:01:28Z http://eprints.utem.edu.my/id/eprint/29546/ Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning Zulkafli, Nur Izyan Hashim, Muhammad Fikri Sulaima, Mohamad Fani Jali, Mohd Hafiz Ahmad Izzuddin, Tarmizi Jayiddin, Nur Saleha Md Lasin, Azmi Iskandar, M Tarmidzi Compressor performance is being evaluated based on its power consumption and other operational parameters to meet load demand efficiently while consuming less power. Without proper correlation with other operational data, it is difficult to predict future power consumption that may lead to a low performance of compressors. The Multiple Linear Regression (MLR) analysis in Altair AI Studio software is being used as a model to predict power consumption for four compressors with two different models by considering mass flow rate, suction and discharge temperature, and pressure as its dependent variables. The set of data has been split into two, which are training and testing, at a ratio of 90:10, respectively. This study resulted in a low percentage difference between the predicted and actual power consumption of those four compressors, which are 1.46 %, 1.40 %, 2.00 %, and 2.25 % for Compressor 1, Compressor 2, Compressor 3, and Compressor 4, respectively. The MLR of the compressor power consumption model can be utilized to predict its future power consumption to move towards more sustainable and low-carbon emissions. Italian Association of Chemical Engineering - AIDIC 2025 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29546/2/02190311220251657362857.pdf Zulkafli, Nur Izyan and Hashim, Muhammad Fikri and Sulaima, Mohamad Fani and Jali, Mohd Hafiz and Ahmad Izzuddin, Tarmizi and Jayiddin, Nur Saleha and Md Lasin, Azmi and Iskandar, M Tarmidzi (2025) Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning. Chemical Engineering Transactions, 122. pp. 235-240. ISSN 2283-9216 https://www.cetjournal.it/cet/25/122/040.pdf 10.3303/CET25122040 |
| spellingShingle | Zulkafli, Nur Izyan Hashim, Muhammad Fikri Sulaima, Mohamad Fani Jali, Mohd Hafiz Ahmad Izzuddin, Tarmizi Jayiddin, Nur Saleha Md Lasin, Azmi Iskandar, M Tarmidzi Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| title | Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| title_full | Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| title_fullStr | Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| title_full_unstemmed | Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| title_short | Predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| title_sort | predicting power consumption of cryogenic compressors using multiple linear regression in machine learning |
| url | http://eprints.utem.edu.my/id/eprint/29546/2/02190311220251657362857.pdf http://eprints.utem.edu.my/id/eprint/29546/ https://www.cetjournal.it/cet/25/122/040.pdf |
| url_provider | http://eprints.utem.edu.my/ |