The aim of this research is to design and create the sorting machine within the engineering knowledge by using the three-layer sieve set cylindrical sorting machine under the various conditions of experimental. Furthermore the Specific Energy Consumption [SEC] of Robusta coffee bean separates the standard size of Robusta coffee bean the total of product separated are regarded. For the experiment condition rotation speed adjustable to 9.6 and 14.4 rpm. The raw material flow through to sieve and determination the standard size for calculating the [SEC]. The result of experiment found that the average of total of product separated was 165.1 kg/hr. and 164.7 kg/hr or 99.57% from that were passed the criteria of standard according to the ministry of agriculture cooperative. In order to the [SEC] the cylindrical sorting machine was 0.01631 MJ/kg at 14.4 rpm of rotation speed. However the values of [SEC] may be change from the effect of rotation speed of sieve set.

Diode high voltage the equipment assembles. That have necessity and important for a room practices for test experiment research and for the instruction. But diode high voltage to bring an element in a room practices something still must buy from the foreign countries and expensive. This problem corrects by designing and establish by oneself. This project has designed and build to diode high voltage for a laboratory by diode high voltage 100 kV. That can bring assemble all circuit get the circuit voltage multiplier etc. Building from the inventory at can seek can buy in house diode high voltage. That design and build that is the series diode on print circuit and electric oil insulator. Base principle of diode data technical of the components for applies in designing. The way builds to diode high voltage diagnose of designing builds consider the test diode high voltage the office designs. Which test has followed the objective is pass the power frequency withstand voltage test 120 kV 22 mA

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 work was aimed to improve the carbon black from waste tires for printing ink using coupling agent to disperse carbon black particle for making an oil-based offset ink. This experiment used 2 coupling agent these were silane coupling and derivative fatty acid and used to produce offset inks for the 7 formulas. Formula 1 Commercail carbon blackFormula 2 carbon black was not washed by Nitric acid formula 3 carbon black was not washed by Nitric acid and was added silane coupling agent formula 4 carbon black was not washed by Nitric acid and was added fatty acid modified emulsifier formula 5 carbon black was washed by nitric acid formula 6 carbon black was washed by nitric acid and was added silane coupling formula 7 carbon black was washed by nitric acid and was added fatty acid modified emulsifier. Bring all 6 formulas to test property to compare with offset ink commercial. First find the smallest particle of carbon black for 90 minutes. It was found that formula 4 took 20 minutes to make particles 12 micron and it is smalles particle of all. Then measured ink flow by Spread-O-Meter and measured diameter after 1 minute. It was found that formula 3 and 3 were 77 mm. and approximate commercial ink. Then measured tack of ink with Ink-O-Meter it was found that formula 2 3 4 6 and 7 were 2.4 and formula 5 was 2. Then measured viscosity and yield value of ink by Lary viscometer. It was found that formula 2 had 77 poise of ink viscosity and 670 dyne.s/cm2 of ink yield value that was approximate commercial ink. Finally measured Emulsification capacity (%) by Kershaw Water Pick up Tester. It was found that formula 6 had 74% and it was approximate commercial ink.

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.