The difficulty that exists in accurately monitoring the performance of air conditioners has made performance prediction an arduous task. Nevertheless, the performance still needs to be monitored and predicted as it helps solve a lot of problems resulting from this technology like effect of the technology on the grid, energy consumption, water utilisation and GHGs emission. With the introduction of regression modelling as a means of system monitoring and prediction some years ago, the accuracy was still a call for concern. It is worth realising that increasing the number of predictors will enhance this method’s accuracy. As such, this document intends to increase the accuracy of this method’s monitoring and predicting ability by increasing the number of predictors to cut across system thermal, environmental and human behavioural variation. These predictors experimentally gotten are used to build an environ-behavioural model that monitors the coefficient of performance and energy consumption of a domestic split-type air conditioner with higher accuracy. Refrigerants have undergone evolution in the past decades in a bid to come up with a refrigerant that has zero ODP, lower – than – R22 GWP and much better than R22 thermodynamic performance. No pure refrigerant has been found to possess these qualities as such mixtures or blends are the best shot at the moment. R410A could stand the test of time to be the long term R22 replacement but for the fact that besides R410A’s higher GWP than that of R22, the former’s system performance is lower than that of the latter’s due to the lower thermodynamic performance of the former. This means the search continues. In this document, a combination of carefully chosen refrigerant components are carefully blended to come up with a simulation based R22 long term replacement, which will be referred to in this document as BTEP.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ufh/vital:28537 |
| Date | January 2017 |
| Creators | Bantan, Mafor Glory |
| Publisher | University of Fort Hare, Faculty of Science & Agriculture |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Masters, MSc |
| Format | 88 leaves, pdf |
| Rights | University of Fort Hare |
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