Jamin Koo

The Estimation of Hazardous Gas Release Rate Using Optical Sensor and Neural Network

Abstract A real-time monitoring and estimation technique is proposed for managing facilities that store hazardous materials. It relies on Gaussian dispersion model, optical sensor and neural networks for the detection and analysis of hazardous (gas) releases. We used an optical sensor network that has already been set surrounding the area where hazardous gas releases can occur. From the real-time sensor data, we detected and analyzed releases of harmful materials and their concentrations. Based on the results, the release rate is estimated using the neural network. This method consists of 14 input variables (sensor data, material properties, process information, and meteorological conditions) and one output (release rate). The dispersion model then performs the simulation of the expected dispersion consequence. The proposed technique provides calculated values that are within acceptable differences from those of commercial software like PHAST and TRACE. In comparing with commercial software, a series of scenarios are developed that involve commonly used hazardous materials such as ammonia and chlorine. The results indicate that the proposed technique can estimate release rates effectively within seconds, with 14 input variables that are far less in number than those required by previous methodologies.

Development of chemical accident database: Considerations, accident trend analysis and suggestions

Traditionally, regulations, standards, codes and management systems were established after major accidents such as Seveso Directive by Seveso accident, Italy, in 1976, etc. Also, several chemical material and process accident databases were developed and implicated in the world. In this paper, we introduce a set of recently developed databases, such as eMARS (EC), HSEES (USA) and PEC-SAFER (Japan), and describe current development of a chemical accident tracking system in Korea. Especially, we intensively focused on general classification codes, simplified and effective database structure, user friendly interface and intuitional searching methods. Additionally, the chemical accident trends were analyzed by using third grade of accident casualties and property’s loss. Finally, suggestions for applicability improvement of database are proposed. To improve the management and operability of hazardous materials and chemical processes, the systemic approaches are essential using accident database. The developed database and suggested issues in this study can perform an important function in the chemical industries.

Optimization of sustainable energy planning with consideration of uncertainties in learning rates and external cost factors

Abstract Traditionally, energy planning has focused on meeting the domestic demand with low total costs. However, issues and concerns about global warming have made the problem of energy planning more complex. Since conventional energy supplies cause environmental problems like greenhouse effect, most governments are now trying to increase investment in developing sustainable energy systems (e.g. hydro, wind, solar, and/or biomass energy). Unfortunately, the expansion of promising sustainable energy technologies in the market or research area is not obvious. The production costs of these are uncertain and much higher than those of fossil fuels. To overcome these problems, the concept of learning rate is adopted in this study. And the learning rate helps evaluate the costs of installing and operating sustainable energy systems. In this paper, economic evaluation of conventional power plants and prospective sustainable energy systems with respect to production cost is studied. The production cost includes capital, fixed, variable and external costs. For case study, the data reported by Korean government is used. Using the proposed methodology, authorities and companies will be able to decide the optimal investment and installation plans for sustainable energy supplies.