In this research, a composite of ZrO2-TiO2 was used as a photocatalyst in the degradation of dye wastewater. The dye waste water is a single Methylene Blue, MB, waste water from Batik industry. The ZrO2 was prepared from zircon sand founded from Bangka Island, Indonesia. The composite was prepared at various weight ratios and heat treated at 500 o C. The result shows that the purity of ZrO2 from zircon sand is only 66.46 %. However, the addition of ZrO2 into TiO2 is able to increase the photocatalytic activity which proven by 88.75 % degradation of MB at a ZrO2-TiO2 weight ratio of 1:1. The degradation result is higher than that with anatase TiO2; that is only 62.67 %. The kinetics study found that the photocatalytic degradation of MB with single TiO2 has the rate constant of 1.85×10 -2 minutes -1 . Meanwhile, the rate constant of the MB degradation with the composite ZrO2-TiO2 is 16.73×10 -2 minutes -1 .
This research aims to address the problems that are facing a largest steel manufacturing in Indonesia in producing steel is production failure that is caused by process so that it produces defect. The steel manufacturing produces 3 types of products, the named are Hot Rolled Plate, As Rolled and Full Hard. For Full Hard products there are 3 types of popular sizes that are often produced, the sizes are 0.2 x 914 mm, 0.25 x 914 mm and 0.7 x 1219 mm. When compared to the two popular sizes, Full Hard 0.2 x 914 mm products have the highest number of defective products. The percentage of these non-achievements is indicated by defects as in the year 2018 of January until December occurred a defective average of 16% or 1,617 tons This research will focus on improving the Shearing process and the Tandem Cold Mill that causes Saw Tooth Edge, Pick Up and Friction Pick Up defects. This research is done by using Six Sigma methodology (DMAIC) is used to minimize the occurrence of problems in the Shearing process and the Tandem Cold Mill. To find out the cause of a defect in a problematic process, an analysis using fishbone diagrams and 5 why's, then determines the defect repair priority using FMEA.
In the effort to hold up the case spread of COVID-19’s growth rate by implementing health protocols such as the use of masks, supervision is needed especially for the people who have not or still have problems to wearing masks. In this research, the system utilizes the robotic power to identify visitors whether they are wearing masks or not, and automatically distribute masks if the user is detected as not wearing a mask. The user face detection process uses the Haar Cascade Classifier algorithm and SVM (Support Vector Machine) to classify users who wear masks or not. For the user who is detected as not wearing masks, myCobot-Pi with the support of suction pump will distribute masks to users. The use of myCobot-Pi as a raspberry pi based robotic arm allows the application of the system on devices that are minimal in terms of specifications and size. Through trials by taking 41 examples of detection cases, 29 cases were found that managed to detect the correct use of masks. In addition, in this study we use PP sheet plastic protector to replace the packaging of the mask because it can be carried by the suction pump properly.
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