Conventionally, the study of convection heat transfer merely focuses on the behavior of air flow without considering the conductive effect of the horizontal flat plate. However, it is expected that the conductive effect of the horizontal plate somewhat affects the air flow temperature across the flat plate. Therefore, it is motivated to study the variation of air flow temperature across different materials of flat plate in various time frame. The materials used in this study are aluminium, stainless steel and cast iron. Infrared camera and FloEFD simulation software are used to measure the upper surface temperature of the flat plate. For forced convection, the study is carried out within the range of 103 £ Re £ 104 and within the range of 1 × 107 £ Ra < 2.2 × 107 for natural convection. Flow velocity of 2.3 m/s, 4.1 m/s and 5.2 m/s are used for the forced convection. The results showed that aluminium plate cools down faster than the other two metal plates used in all scenarios. Stainless steel’s temperature goes down faster compared to cast iron. These results were supported by the fact that aluminium has higher heat transfer rate of other metals. For forced convection, the discrepancies of temperatures between experimental and simulation studies are below 10%, which demonstrates that the results are reasonably acceptable. For natural convection, even though the discrepancies between simulation and experimental results on temperature variations are relatively large, the temperatures varied in similar pattern. This indicates that the results are reliable.
Population growth has resulted in a decrease in readily available sources of potable water. Desalination is one of many approaches that has been studied and proposed as a way out of this predicament. In this study, multistage Reverse Osmosis desalination process is used in the model, since it has the potential to achieve a higher purity percentage than the single-stage RO desalination process. Some researchers have studied the distinctive tools of AI, specifically Artificial Neural Network as regression model and the genetic Algorithms as an optimization technique in the process of desalination and water treatments. This paper aims to examine multistage RO desalination by employing various artificial intelligence (AI) techniques, including Artificial Neural Network (ANN) and Support Vector Machine (SVM). Both training methods used for this research come under the category of regression algorithms, which are used to establish a predictive link between variables and labels. The main finding of this study was the noticeable decrease of Mean Square Error (MSE) in second stage when data was trained using the ANN. While on the other hand the MSE increased in second stage when the data was trained using the SVM. It can be concluded that the results of this research indicate that applying ANN and SVM to RO desalination process modelling would yield substantial improvements. Future work will be focusing on predicting and improving the performance of ANN and SVM prediction with other function variables.
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