Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine
In this report, the topics on multi-evaporator refrigeration system and extreme learning machine were extensively elaborated. Experiments were performed on the variable ejector in multi-evaporator refrigeration system so as to understand its operation further by expanding its system database. With t...
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sg-ntu-dr.10356-608752023-07-07T17:48:06Z Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine Shahrir Iman Bin Mohamad Sawal Cai Wenjian School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering In this report, the topics on multi-evaporator refrigeration system and extreme learning machine were extensively elaborated. Experiments were performed on the variable ejector in multi-evaporator refrigeration system so as to understand its operation further by expanding its system database. With the newly acquired data obtained from the experiments, the results show that its area distribution does increase. Subsequent tests were carried out to evaluate the system pressure recovery ratio with the addition of new data. The results were able to validate that the system’s PRR does improve as the cross-validation and test errors were reduced. An increase of 0.914% of PRR model accuracy was obtained. Thus, the conducted investigation supports previous researches and claims that the variable ejector is indeed a promising application in the multi-evaporator refrigeration system. Bachelor of Engineering 2014-06-02T06:40:33Z 2014-06-02T06:40:33Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/60875 en Nanyang Technological University 53 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Shahrir Iman Bin Mohamad Sawal Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
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In this report, the topics on multi-evaporator refrigeration system and extreme learning machine were extensively elaborated. Experiments were performed on the variable ejector in multi-evaporator refrigeration system so as to understand its operation further by expanding its system database. With the newly acquired data obtained from the experiments, the results show that its area distribution does increase. Subsequent tests were carried out to evaluate the system pressure recovery ratio with the addition of new data. The results were able to validate that the system’s PRR does improve as the cross-validation and test errors were reduced. An increase of 0.914% of PRR model accuracy was obtained. Thus, the conducted investigation supports previous researches and claims that the variable ejector is indeed a promising application in the multi-evaporator refrigeration system. |
author2 |
Cai Wenjian |
author_facet |
Cai Wenjian Shahrir Iman Bin Mohamad Sawal |
format |
Final Year Project |
author |
Shahrir Iman Bin Mohamad Sawal |
author_sort |
Shahrir Iman Bin Mohamad Sawal |
title |
Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
title_short |
Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
title_full |
Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
title_fullStr |
Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
title_full_unstemmed |
Experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
title_sort |
experimental investigation on variable ejector in multi-evaporator refrigeration system using extreme learning machine |
publishDate |
2014 |
url |
http://hdl.handle.net/10356/60875 |
_version_ |
1772826105110069248 |