An Analysis of Energy Mix in Peninsular Malaysia in Line with the Malaysia's Existing Energy Policy

This paper considers dynamic changes of energy-mix available in Peninsular Malaysia with respect to the Malaysia’s energy policies and evaluates these on experimental basis. This research applied a Data Mining approach; Self Organizing Map (SOM) Algorithm for trend cluster analysis time series data. The approach can provide a number of capabilities to uncover relationships between data attributes, uncover relationships between observations, predict the outcome of future observations and learn how to best react to situations through trial and error by using reinforcement learning. Based on the experiment, the test results have shown that the application is able to accommodate large sets of data and produced the trend lines graphs thus at the same time, a clearer picture of scenarios and the latest trend of energy mix applied in Peninsular Malaysia were successfully obtained; it is shown that Malaysian government should increase the execution and improvement in the realization and implementation of energy policy in Malaysia. Besides, Malaysia still has a lot of potential in order to fully utilise renewable energy resources.

MULTI-RESPONSE OPTIMIZATION OF PLASTIC INJECTION MOULDING PROCESS USING GREY RELATIONAL ANALYSIS BASED IN TAGUCHI METHOD

This project investigates the multi-response optimization using grey relational analysis based in Taguchi method of plastic injection mould. Four input process parameters selected are mould temperature, melting temperature, injection time and cooling time. The responses investigated were part weight, shrinkage, warpage, ultimate tensile strength, tensile modulus and percentage of elongation. It is found that the optimum setting parameter generated from multi-response optimization is at run number 4 that are mould temperature at 56oC, melting temperature at 250oC, injection time at 0.7s and cooling time at 15.4s. Result of run number 4 for multi-response optimization for part weight, warpage, shrinkage, tensile ultimate strength, tensile modulus and percentage of elongation are 6.9807g, 0.087mm, 1.73%, 24.732MPa, 981.76MPa and 31.37%, respectively. Multi-response optimization results show that all response results are not higher or lower than experimental results. This is because multi-response optimization normalized all response value. Thus, by implemented multi-response optimization process, the materials characteristics value of plastic part can be predicted