Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

Microstructural characteristics of directionally solidified René 80 superalloy are investigated with optical microscope and scanning electron microscope; solidification velocity is found to change from 25 to 200 μm/s under the condition of constant thermal gradient (G) and constant alloy composition (Co). Based on differential scanning calorimetry (DSC) measurement, γ phase (1,322 oC), MC carbide (1,278 oC), γ/γ' eutectic phase (1,202 oC), and γ' precipitate (1,136 oC) are formed sequentially during cooling process. The size of the MC carbide and γ/γ' eutectic phases gradually decrease with increasing solidification velocity, whereas the area fractions of MC carbide and γ/γ' eutectic phase are nearly constant as a function of solidification velocity. It is estimated that the area fractions of MC carbide and γ/γ' eutectic phase are determined not by the solidification velocity but by the alloy composition. Microstructural characteristics of René 80 superalloy after solid solution heat-treatment and primary aging heat-treatment are such that the size and the area fraction of γ' precipitate are nearly constant with solidification velocity and the area fraction of γ/γ' eutectic phase decreases from 1.7 % to 0.955 %, which is also constant regardless of the solidification velocity. However, the size of carbide solely decreases with increasing solidification velocity, which influences the tensile properties at room temperature.

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies’ attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

The γ/γ´ two-phases, commonly known as a eutectic structure, are observed in the γ interdendritic region of a Nibase superalloy. However, the growth behavior of the γ/γ´ two-phases, whether it is of eutectic or peritectic nature, has not been decidedly established. Directional solidifications were, thus, performed with the planar interface at a low growth rate of 0.5 μm/s in order to promote macro segregation. Directional solidification started with the γ planar interface and the γ´ phase nucleated on the γ planar interface at the solidification fraction of 0.75. The γ/γ´ two-phases showed the γ´ rod structure as major phase and the γ minor phase between γ´ rods, and the volume fraction of the γ phase changed continuously with an increasing solidification fraction. The two-phase γ/γ´ is seen as the coupled peritectic.

The O&M (Operation and Maintenance) phase of offshore plants with a long life cycle requires heavy charges and more efforts than the construction phase, and the occurrence of an accident of an offshore plant causes catastrophic damage. So previous studies have focused on the development of advanced maintenance system to avoid unexpected failures. Nowadays due to the emerging ICTs (Information Communication Technologies) and sensor technologies, it is possible to gather the status data of equipment and send health monitoring data to administrator of an offshore plant in a real time way, which leads to having much concern on the condition based maintenance policy. In this study, we have reviewed previous studies associated with CBM (Condition-Based Maintenance) of offshore plants, and introduced an algorithm predicting the next failure time of the compressor which is one of essential mechanical devices in LNG FPSO (Liquefied Natural Gas Floating Production Storage and Offloading vessel). To develop the algorithm, continuous time Markov model is applied based on gathered vibration data.

In this study, mechanical tests and microstructural analyses including TEM analyses with EDX of precipitates in modified 9Cr-1Mo steel were carried out to determine the cause of embrittlement observed after heat-treatment, which limits the usage of the alloy for power plants. Mod. 9Cr-1Mo steel specimens at austenite temperature were quenched to the molten salt baths at 760˚C and 700˚C, in which the specimens were kept for 10 min ~ 10 hr with subsequent air-cooling. Impact tests showed that the impact value dropped abruptly when the specimens were kept longer than 30 min at ~760˚C reaching to minima in about 1 hr, and then increasing at further retention. The tensile strength of the specimens reached the minimum value without much change afterward, whereas the values of elongation showed the same trend as that of the impact value. The isothermally heat-treated steel at 700˚C also showed a minimum impact value in about 1 hr. These results suggest that the isothermal heattreatment at 760 and 700˚C for about 1 hr induces temporal embrittlement in Mod. 9Cr-1Mo steel. The microstructural examination of all the specimens with extraction replica of the carbides revealed that the specimens with temporal embrittlement had Cr2C, indicating that the cause of the embrittlement was the precipitation of the Cr2C. In addition, TEM/EDX results showed that the Fe/Cr ratio was 0.033 to 0.055 for Cr2C, whereas it was 0.48 to 0.75 for Cr23C6, making the distinction of the Cr2C and Cr23C6 possible even without direct electron diffraction analyses.

It is well known that modified 9Cr-1Mo steel has a low thermal expansion and high thermal conductivity with excellent high temperature properties compared to austenitic stainless steel. For these advantages, the steel is very popular for the boiler tube of thermal power plants. Normalizing is commonly utilized to obtain martensite in this steel, which shows an unusual toughness for martensite. However, some accidents related to this steel have been reported recently, opening the necessity for further study. As a particular behavior of the steel, an abrupt drop of the impact value has been identified upon tempering at 750˚C for about 1 hour. It is well known that Fe3C forms during autotempering and turns to Cr2C at an early stage and then transforms to Cr23C6. In this study, the cause of the abrupt drop of the impact value was investigated with an impact test, microstructural observation, nanodiffraction and phase analyses using instruments such as optical and transmission electron microscopes (TEM) with an extraction carbon replica of the carbides. The analyses revealed that the M2C that formed when retained for about 1 hour at 750˚C causes a drastic decrease in the mechanical properties. The sharp drop in mechanical properties, however, disappeared as the M2C transformed into M23C6 with longer retention.

The FIMS(Far-ultraviolet IMaging Spectrograph), the main payload onboard the first Korean Science Technology SATellite, STSAT-1, has performed various astronomical observations, including the Cygnus Loop, Vela supernova remnants, LMC(Large Magellanic Cloud), since its launch on September 2003. It has been found that the attitude information provided by spacecraft bus system has the errors of more than about 10-15 arcmins due to the time offset problem and errors in attitude knowledge. We develop an algorithm for correction of position errors in FIMS data. The aspect for the FIMS data is determined by comparing the positions of observed bright stars with the Tycho-II and TD-1 catalogs. The position errors of the bright stars along the scanning ( γ) and spatial (δ ) directions were considered as functions of δ, ignoring errors in position angle. The corrected positions of the bright stars coincided very well to their Tycho-II and TD-I positions. The correction algorithm is essential for the FIMS data analysis, and is being used for the FIMS data analysis.