Jae-Wook Ko

Real-time risk monitoring system for chemical plants

This study was performed to develop a Real-Time Risk Monitoring System which helps to do fault detection using the information from plant information systems in a chemical process. In this study, to do fault detection, principal component analysis (PCA) methods of multivariate statistical analysis were used. The fundamental notions are a set of variable combinations, that is, detection of principal components which indicate the tendency of variables and operating data. Besides classical statistic process control, PCA can reduce the dimension of variables with monitoring process. Therefore, they are known as suitable methods to treat enormous data composed of many dimensions. The developed Real-Time Risk Monitoring System can analyze and manage the plant information on-line, diagnose causes of abnormality and so prevent major accidents. It’s useful for operators to treat numerous process faults efficiently.

Diffraction Efficiencies of of SeO 2 / Acrylamide Modified Photopolymer

The highest diffraction efficiency(DE) value after illuminations and post-curing of photopolymer films were obtained at the SeO2 /acrylamide(AA) Ratios of AA 3.0g, SeO2 1.0 g and the DEs were stable values of over 90%. By the addition of SeO2, the maximum DE at the initial stage of illumination reached at 300 seconds, which suggests SeO2 slow down the photopolymerization of DE variation curve for the optimum composition during extended-time illumination of 9,000 seconds resembles sin curve due to the combination of the monomer diffusion and the photopolymerization, and the photopolymer film expanded at about 8% after photopolymerization due to monomer migration.