Jinil Kim’s scientific contributions

With the rapid development of ICTs, condition monitoring has been used as a key technology in the plant industries. For reliable condition monitoring, sensors should output same values under same conditions regardless of time, but the sensitivity of sensors is gradually changed due to several factors such as temperature, humidity, contamination, aging, and etc. This type of situation is called as sensor drift problem. To solve this, several methods such as autoassociative neural network, auto-associative support vector regression, and etc. have been developed to detect sensor drifts earlier by estimating new input based on historical data. This study applied the auto-associative kernel regression model into a synthetic natural gas plant which produces synthetic natural gas from coals to detect sensor drifts during operation phase. To validate the auto-associative kernel regression model in the synthetic natural gas plant, a real data collected from an experimental operation are used. Based on the experimental results, the auto-associative kernel regression model can rapidly detect the sensor drift in the synthetic natural gas plant.

Successful implementation of Model-based Systems Engineering(MBSE) obviously needs a model supporting efficient communication among engineers of various domains. The system modeling language standard, SysML is designed to create MBSE supporting models. However, SysML itself is not practical enough to be used for real-world engineering projects. As SysML is designed for generic systems and requires specialized knowledge, a model written in SysML has a limited capability to support communication between a systems engineer and a subsystem engineer. Our research’s main goal is to develop a SysML based plant model integrating most outputs from plant design phases. As mentioned, a standard SysML based plant model is not specific enough to be understood by plant engineers. To make the SysML model more practical, a customized SysML for the plant engineering domain is required. Unfortunately, current researches on SysML Domain Specific Language(DSL) for the plant engineering industry are still on the early stage. So, as a pilot, we have developed our own SysML-based Piping & Instrumentation Diagram (P&ID) creation environment and P&ID itself for a specific plant system, via widely known SysML modeling tool called MagicDraw. P&ID is one of the most important output during the plant design phase, which contains all information for the plant construction phase. So a SysML based P&ID has a great potential to bridge gaps between plant engineers.