• Clean Technology
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• v.12 no.2
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• pp.67-77
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• 2006

A simple dual sludge process, called as $KNR^{(R)}$ (Kwon's Nutrient Removal) system, was developed for small sewage treatment. It is a hybrid system that consists of an UMBR (Upflow multi-layer bioreactor) as anaerobic and anoxic reactor with suspended denitrifier and a post aerobic biofilm reactor, filled with pellet-like media, with attached nitrifier. To evaluate the stability and performance of this system for small sewage treatment, the pilot-scale $KNR^{(R)}$ plant with a treatment capacity of $50m^3/d$ was practically applied to the actual sewage treatment plant, which was under retrofit construction during pilot plant operation, with a capacity of $50m^3/d$ in a small rural community. The HRTs of a UMBR and a post aerobic biofilm reactor were about 4.7 h and 7.2 h, respectively. The temperature in the reactor varied from $18.1^{\circ}C$ to $28.1^{\circ}C$. The pilot plant showed stable performance even though the pilot plant had been the severe fluctuation of influent flow rate and BOD/N ratio. During a whole period of this study, average concentrations of $COD_{cr}$, $COD_{Mn}$, $BOD_5$, TN, and TP in the final effluent obtained from this system were 11.0 mg/L, 8.8 mg/L, 4.2 mg/L, 3.5 mg/L, 9.8 mg/L, and 0.87/0.17 mg/L (with/without poly aluminium chloride(PAC)), which corresponded to a removal efficiency of 95.3%, 87.6%, 96.3%, 96.5%, 68.2%, and 55.4/90.3%, respectively. Excess sludge production rates were $0.026kg-DS/m^3$-sewage and 0.220 kg-DS/kg-BOD lower 1.9 to 3.8 times than those in activated sludge based system such as $A_2O$ and Bardenpho.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.