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.

Heatwaves are one of the most common phenomena originating from changes in the urban thermal environment. They are caused mainly by the evapotranspiration decrease of surface impermeable areas from increases in temperature and reflected heat, leading to a dry urban environment that can deteriorate aspects of everyday life. This study aimed to calculate daily maximum ground surface temperature affecting heatwaves, to quantify the effects of urban thermal environment control through water cycle restoration while validating its feasibility. The maximum surface temperature regression equation according to the impermeable area ratios of urban land cover types was derived. The estimated values from daily maximum ground surface temperature regression equation were compared with actual measured values to validate the calculation method’s feasibility. The land cover classification and derivation of specific parameters were conducted by classifying land cover into buildings, roads, rivers, and lands. Detailed parameters were classified by the river area ratio, land impermeable area ratio, and green area ratio of each land-cover type, with the exception of the rivers, to derive the maximum surface temperature regression equation of each land cover type. The regression equation feasibility assessment showed that the estimated maximum surface temperature values were within the level of significance. The maximum surface temperature decreased by 0.0450˚C when the green area ratio increased by 1% and increased by 0.0321˚C when the impermeable area ratio increased by 1%. It was determined that the surface reduction effect through increases in the green area ratio was 29% higher than the increasing effect of surface temperature due to the impermeable land ratio.

The effects of temperature and salinity on egg development and settlement of the ascidian Herdmania momus were investigated. Adult specimens were collected from the Dodu Yacht facility in Jeju Island, Korea (33°30′30.54″N, 126°27′55.46″E) in August 2018. Egg development and larval settlement were observed and recorded at 8 h intervals using a stereomicroscope, under nine temperature (10, 13, 16, 19, 22, 25, 28, 31, and 34°C), and four salinity regimens (28, 30, 32, and 34 psu). The highest hatching rate (82.8±7%) was observed at 32 psu and 25°C and the lowest hatching rate (1.0±2%) was at 34 psu and 13°C. The developmental rate (0.222±0.0994) was highest at 28 psu and 28°C, and lowest (0.016±0.008) at 30 psu and 13°C. The highest settlement success rate (77.1±5%) was at 32 psu and 25°C and the lowest (0.1±1.0%) was at 30 psu, and 13°C. The rate of settlement (0.080±0.000) was highest at 28 psu and 28°C, and lowest (0.013±0.000) at 30 psu and 13°C. Both hatching and settlement success rates increased as temperature increased and tended to decrease beyond an optimal temperature range. Herdmania momus preferred 30-34 psu salinity and 22-25°C temperature. This study provides baseline information about the life history of H. momus, and important data to control the damage caused by the increase in number and distribution of this invasive ascidian.

본 연구는 대륜 국화 ‘백마’의 생육 특성인 생체중, 건물중, 엽면적을 조사하여, 생장 및 기후요소에 따른 생장 예측모델 개발을 위하여 수행되었다. 정식후 일수 및 누적온도에 따른 국화의 건물중 및 엽면적 분석에 기반한 ‘백마’의 생장예측을 위한 시그모이드 회귀모델을 개발하였다. ‘백마’의 건물중 상대 생장률(RGR)은 재배기간 평균 0.084 g·g-1·d-1이었다. 정식 후 재배 기간에 따른 건물중에 대한 상대 생장률은 정식 초기부터 단일처리 전까지 높았으며 최고 0.133 g·g-1·d-1까지 증가하였고, 63일째 단일처리가 시작된 후 수확 시기에서는 0.030 g·g-1·d-1으로 감소하는 경향을 보였다. 누적온도에 따른 국화의 건물중, 엽면적에 대한 생장 모델(sigmoid 곡선)을 개발하였다. 정식 후 일수와 누적온도에 따른 ‘백마’의 건물중 및 엽면적은 지수함수적으로 증가하였으며, 건물중의 경우 63일(누적온도 1601℃)까지 평균 39.1%씩 증가하였고, 이후 평균 7.4%씩 증가하였다. 엽면적의 경우 정식 후 28일차 까지 평균 63.3%씩 증가하였고, 화아분화가 발생하기 전인 84일차까지 평균 6.5%씩 증가하였으며 화아 분화가 발생하 기 전 84일까지 평균 6.5%로 증가했고, 이후 수확 전까지 평균 10.6%씩 증가하는 경향을 보였다. 본 실험은 충남지역에 서 대륜 국화 ‘백마’의 재배관리 체계와 계획적 연중 생산 체계를 구축하는데 유용한 자료로 활용될 수 있다. 보다 정밀한 생육 예측 모델을 만들기 위해서는 누적 일사량을 포함한 다양한 기상자료를 바탕으로 하여 교정 및 검증이 필요하다.

Temperature-dependent development models for Hydrochara affinis were built to estimate the ecological parameters as fundamental research for monitoring the impact of climate change on rice paddy ecosystems in South Korea. The models predicted the number of lifecycles of H. affinis using the daily mean temperature data collected from four regions (Cheorwon, Dangjin, Buan, Haenam) in different latitudes. The developmental rate of each life stage linearly increased as the temperature rose from 18°C to 30°C. The goodness-of-fit did not significantly differ between the models of each life stage. Unlike the optimal temperature, the estimated thermal limits of development were considerably different among the models. The number of generations of H. affinis was predicted to be 3.6 in a high-latitude region (Cheorwon), while the models predicted this species to have 4.3 generations in other regions. The results of this study can be useful to provide essential information for estimating climate change effects on lifecycle variations of H. affinis and studies on biodiversity conservation in rice fields.