Research in the training simulation sector to improve the realism and immersive experience of operator training simulators (OTSs) entails combining cutting-edge technologies such as virtual reality (VR) and augmented reality (AR). Although most of the existing studies has been about troubleshooting training, research into the response to chemical accidents through mutual cooperation between the participants has been insufficient. Therefore, we developed an immersive OTS that can facilitate mutual cooperation. Training processes to educate trainees in general chemical facilities were selected, while changes that can occur in facilities during an accident and the corresponding responses in various scenarios were used as the training content. A communication system that relays information between the worksite and the control room was implemented using a distributed control system (DCS) and AR technology. We installed a pilot plant and developed a DCS, thereby establishing an infrastructure that allows the boardman and field operator to cooperate during accident scenarios. Furthermore, we developed an OTS that allowed trainees to learn prompt and accurate responses to chemical accidents through operation of the actual equipment. The training effect of the OTS was found to be approximately 4.5 times better than traditional training methods. It is, therefore, anticipated that the developed OTS will minimize losses or damage caused by chemical accidents.
Abstract In the chemical industry, when a fire occurs, a significant amount of energy is generated due to combustion, impacting other facilities within the plant and potentially leading to severe consequences through a domino effect. For decades, thermal radiation caused by flames has been calculated and predicted through simplified fire modeling. However, with advancements in computing technology, numerical model‐based calculations have greatly improved, allowing for a more realistic implementation that considers actual phenomena. In this study, accident data and 3D modeling information were utilized to conduct fire modeling and simulation based on actual incidents in chemical plants. Through the analysis of simulation results, the initial emergency evacuation distance was provided to minimize the damage caused by thermal radiation, and the final evacuation distance was presented using the probit function. In addition, the study evaluated the impact of generated thermal radiation and overpressure on structures and equipment, providing evidence regarding the potential for secondary incidents. Moreover, the research revealed that the impact of thermal radiation and overpressure decreases due to obstacles, offering insights into the selection of emergency evacuation routes. This study can contribute to supporting effective emergency evacuation strategies in chemical facilities.
There have been studies recently on bubble-column scrubbers with low cost and high efficiency for the absorption and treatment of hazardous gases in the event of a chemical spill. Bubble columns are vulnerable to freezing at temperatures below zero because the absorbents generally do not circulate. To address this issue, this study focused on the applicability, absorbed amount, and performance of brine as an absorbent. Under three different temperatures, i.e., −5 °C, −8 °C and −10 °C we examined brine (NaCl, CaCl2, and MgCl2) by varying the concentration required at each temperature. Following the experiments, CaCl2 brine was determined as the optimal brine for its absorption performance and affordability. Based on the experimental results, the absorption performance for ammonia, ethylene oxide, and methylamine, which are hazardous and water-soluble gases among accident preparedness substances (APS), was tested by using ASEPN PLUS. Our results suggested although the efficiency dropped by about 5% to 25% when brine was used as an absorbent, it can be used at the low temperatures because the gas solubility increased with decreasing temperature. Therefore, if brine, as an alternative, is used at temperatures about 15 °C, it can operate efficiently and stably without deterioration in the absorption performance. Given our experimental results and design data on the absorbed amount and absorbent replacement period for major hazardous gases are utilized to prevent bubble columns from freezing, it can be commercially used for small and medium-sized enterprises because it can help reduce installation and operation costs.
Hydrogen, an environmentally friendly and highly regarded future energy source, can form flammable vapor clouds upon leakage, which may transition into explosion. Predicting the dispersion behavior of hydrogen is crucial for preventing such incidents. This study aims to develop a quantitative property-consequence relationship (QPCR) model using the response surface method (RSM) and artificial neural network (ANN) to swiftly and accurately predict dispersion behavior. Initially, 8 variables were defined from source and dispersion models, constructing a data set through 6,561 PHAST simulations. Subsequently, the RSM-BBD (Box-Behnken design) and ANN-BPNN (Backpropagation neural network) models were developed, alongside a hybrid model incorporating BPNN after excluding four low-influence variables based on analysis of variance (ANOVA). All models achieved an R
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