Normally bast fibres were removed from pineapple web by wrapping the boiled pineapple leaf with nylon web and stamp on it. If using this method it will take 24 hours to stamp on 40 kilograms of boiled leaf in order to obtain clean pineapple web that is suitable for producing paper. Apart from that when pressing the fiber we need to get rid of the water as much as we can because the fiber will be weight before using to produce paper. If the water pressed out each time is not equal there may be an error about paper thickness. Moreover stamping on boiled leaf may be dangerous because the chemical substance used when boiling the leaf may burn the feet and cause wound. Currently there is a Pressing machine to separate water from pineapple fiber Rachadasak Supengcum 2010 to fix the problems mentioned above. However there is a weak point because the pressing tube is small so it can press only 1 kilogram of leaf per one time. Therefore the new machine is designed to have a pressing tube sized 30 centimeters wide and 30 centimeters long. The pressing period is 30 centimeters and the pressure for pressing is 70 bars. It takes 1 minute 15 second for one squeeze which contains 6 kilograms of pineapple leaf. It can decrease the time for pressing from the old machine by 6 times. In addition the machine is designed to have the open-close channel to bring the pressed fiber out from the machine more easily. According to the experiment to remove the bast fibres from pineapple web the most appropriate amount for each pressing is 6 kilograms. The pressing process takes 1 minute 15 second. The remaining pineapple web after the pressing is 3 kilograms

This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life machine type 6 worker group 3 and 4 working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimation

This research proposes reliability analysis in order to find the probability that a machine will work properly the failure rates of a machine and the factors related to the failure rates. The data were collected from 8 types of machines in an autoparts manufacturing factory. They consist of the times measured from the first day of the study until the machine produces defective product workers and production steps. The study duration is 90 days. The results show that the probability that the machine type 4 will work properly more than days is 0.40 machine type 5 more than 68 days is 0.32 machine type 6 more than 72 days is 0.31 machine type 8 more than 85 days is 0.26 machine type 9 more than 109 days is 0.80 machine type 10 at more than 52 days is 0.38 machine type 11 more than 71 days is 0.32 and the probability that the machine type 12 will work properly more than 85 days is 0.26. The failure rate of the machine type 4 at day 51 is 6.61 machine type 5 at day 68 is 1.61 machine type 6 at day 72 is 0.77 machine type 8 at day 85 is 1.26 machine type 9 at day 109 is 1.31 machine type 10 at day 52 is 0.92 machine type 11 at day 71 is 1.24 and the failure rate of the machine type 12 at day 85 is 2.15. The factors influencing on the failure rates are worker group 2 and production step 3 at the significance of 0.05.

This research was aimed to investigate for pineapple leaf paper drying. In this study process of drying the paper as a final step after the other. Paper drying time takes in solar energy to dry naturally for at least 2-3 days or more without natural sunshine. Solar hybrid energy dryer was designed for drying faster than traditional drying. The development of solar hybrid energy dryer ability to operate continuously. The solar dryer receives the heat from sunshine by passing into the solar panel dimension 1 x 2.6 m2 . The panel was installed on the top of the oven which declined 15 degree from the ground. The dimension of the oven is 1 x 1.05 x 1.30 m3 and have added heat to paraphrase another 2 rooms and heater 4 kW with controller and have added heat to paraphrase another 2 rooms. The dryer installed heater 4 kW with temperature controller and blower 0.22 kW serves as the circulation of heat in the oven to constant flow of 0.138 m3 / s. Experiment research test 3 types ; the use of solar energy alone the use of solar heater with a temperature of 50 55 and 60 °C and the heater only at 50 55 and 60 °C with three temperature settings. The variables in the study such as temperature humidity in the dryer humidity and Solar energy affect the pineapple leaf paper drying time. Experimental results from solar heater hybrid with 60 °C temperature setting was dried in minimum period of 3 hours and half at an average initial moisture content 1043.70% db to the final moisture content of 9.12% db. Highest average temperature in the dryer was 64.33 °C with electrical energy consumption 10.5 kWh. The use of only solar drying was dried in maximum period of 5 hours at an average initial moisture content 1070.98% db to the final moisture content of 15.84% db. Highest average temperature in the dryer was 39.89 °C with minimum electrical energy consumption 10.5 kWh.

Research this project invents for design and build a cooking oven Machine to press coconut oil to work continuous and were to create and efficiency validation of Machine for built. Start to scrape continuous bring it to cooking oven set 60 ํC. use cooking oven mean 20 minute per coconut 2 kilograms open it to Machine to press coconut oil to product to work continuous. The result of design and build a cooking oven Machine to press coconut oil to work continuous. There are tools product coconut oil to work continuous 24 hours. By ratio 4 liter per hour.