In general for the refrigeration the cooling coil is almost used to be the device to decrease the temperature. Thus in order to design heat exchange we should study the heat transfer into the cooling coil. This research proposes the numerical using finite element method for calculating the temperature at any position of the cooling point for considering to porous media. After that to create an algebraic equation fmite element for writing the program to search the solution by estimating the temperature. The research demonstrates that the changing of temperature will be decreased to saturated condition that is the temperature around the region of the cooling coil model edge will be decreased. The longer time the lower temperature around each position will decrease respectively until the temperature at each position inside the model are the same as region of the cooling coil model edge. Therefore this search result can be the basic for the comparison with the similar problem of other

Blends of R22/R152a/R124 have been proposed to replace R12 in refrigeration systems.In this research work a technique to simply evaluate thermodynamic properties of refrigerant mixtures has been developed from Raoults Law.the method has been approached to evaluate the properties of the refrigerant blends avalable in the market such as MP39( R22/R152/R124 53/13/34)MP52 (R22/R152a/R124 33/15/52)and MP66 (R22/R152/R124 61/11/28) .The results agree well with those given by the manufacture within 5 % error The method has also been used to evaluate the properties of the ( R32/R125/R134a 23/25/52)refrigerant agree wall with those given from the manufacture with in 3 % error The thermodynamic property dlagrams of these refrigerant blends are also proposed.

This research offers. The system dynamics study of supply chain processes in the manufacturing industry. The production is made-up of make to order from the building stock and flow with the fourth cycle and parameters define to test the behavior by using Vensim program to simulation situation by the time 50 weeks showed that the response to increases in the order of 20% in the production process made the amplification behavior. The process resulted in levels of inventory bog down. The reduction of inventory conflicts with the desire to keep the inventory caused by delays of customer change. The organizational behavior is not balance the products backlog and made the level of order fulfillment ratio lower than 100%

The problem of this research is automotive part companies do not have order because of the world economic crisis and effect to automotive industry. Automotive and automotive parts demand in the world decrease. Automotive Industry of Thailand received affect. Automotive part companies cannot export to foreign markets. The companies in Thailand need the forecasting of automotive part quantity demanded by foreign markets by selecting the most accurate model. This paper presents the modeling for forecasting demand for automotive part of foreign markets to Thailand with time series forecasting techniques. In this study the automotive part are wheels and including parts and accessories because Thailand exported the most in group of automotive parts. These five countries have consistently demanded the largest number of wheels and including parts and accessories are Japan China South Korea Germany and Indonesia. The research involves historical collecting data for the period of 1997 to 2008. The evaluation function of forecast error is used with MAPE. The results reveal that each country suits for each model in term of the chosen dimension. The government can use the forecasting demand for effective production planning by informing manufactures.

This research aimed to design and construct a forecasting support system on demand of fresh durian of Thailand by applying 2 mathematical models which were Time Series model and Artificial Neural Network model in order to find an accurate model of forecasting support system on demand of durian yield to forecast durian yield quantity of Thailand in advance and use the forecast results to plan and determine quantity of fresh durian and durian product conforming with domestic consumption and export in order not to be over demand and cheap durian sold price. The forecast models were applied with Time Series model by using Moving Average Weighted Moving Average Single Exponential Smoothing and Holt’s Linear Exponential Smoothing techniques and Back Propagation of Artificial Neural Network model which used 4 input variables relating to durian yield. All of the forecast models were used to forecast total durian yield quantity in 26 provinces of durian cultivated area all around Thailand. Durian yield quantity data used was from 1996 to 2008. After that the forecast results were used to find the errors of MAE MSE RMSE and MAPE. Then the errors of each forecast model were compared. The forecast model which had the least error was the most accurate forecast model. The research result is following. Back Propagation with 4-8-1 structure of Artificial Neural Network model had the least error so that it was the most accurate forecast model to forecast fresh durian yield quantity in advance.