The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

현재 기후변화 문제 해결을 위한 국제적 목표 및 국내 에너지 정책에 부합하는 LNG 발전의 효율성을 극대화하는 방안이 요구 되고 있다. 2050년 탄소중립 목표 달성을 위해 각국은 2030년까지 온실가스 감축 목표를 설정하였고, 국내에서도 석탄 발전 시설을 단계 적으로 폐쇄하고 LNG 발전 시설을 확대하려는 노력이 진행되고 있다. 이 연구에서는 LNG의 재기화 과정에서 발생하는 냉열을 회수하여 Carbon Capture and Storage, Ammonia-water Rankine Cycle / Kalina Cycle, Data Center Cooling, Direct Expansion 공정에 활용할 수 있는 시스템을 제안하였다. 연구 결과 제안된 시스템의 3E 분석 결과 Energy 효율 51.52%, Exergy 효율 42.74%, 환경적 측면에서 2,145.8 gCO2 / kgLNG의 탄 소 배출량을 보여 가장 우수한 성능을 확인하였다. 이를통해 본 연구에서 새롭게 제시한 시스템은 Energy, Exergy, 환경성 측면에서 강점을 가지며, 향후 기후변화 대응에 크게 기여할 것으로 판단된다.

This study aimed to develop an efficient recycling process for wastewater generated from soil-washing used to remediate uranium (U(VI))-contaminated soil. Under acidic conditions, U(VI) ions leached from the soil were precipitated and separated through neutralization using hydrazine (N2H4). N2H4, employed as a pH adjuster, was decomposed into nitrogen gas (N2), water (H2O), and hydrogen ions (H+) by hydrogen peroxide (H2O2). The residual N2H4 was precipitated when the pH was adjusted using sulfuric acid (H2SO4) to recycle the wastewater in the soil-washing process. This purified wastewater was reused in the soil-washing process for a total of ten cycles. The results confirmed that the soil-washing performance for U(VI)-contaminated soil was maintained when using recycled wastewater. All in all, this study proposes an efficient recycling process for wastewater generated during the remediation of U(VI)-contaminated soil.

As environmental concerns escalate, the increase in recycling of aluminum scrap is notable within the aluminum alloy production sector. Precise control of essential components such as Al, Cu, and Si is crucial in aluminum alloy production. However, recycled metal products comprise various metal components, leading to inherent uncertainty in component concentrations. Thus, meticulous determination of input quantities of recycled metal products is necessary to adjust the composition ratio of components. This study proposes a stable input determination heuristic algorithm considering the uncertainty arising from utilizing recycled metal products. The objective is to minimize total costs while satisfying the desired component ratio in aluminum manufacturing processes. The proposed algorithm is designed to handle increased complexity due to introduced uncertainty. Validation of the proposed heuristic algorithm's effectiveness is conducted by comparing its performance with an algorithm mimicking the input determination method used in the field. The proposed heuristic algorithm demonstrates superior results compared to the field-mimicking algorithm and is anticipated to serve as a useful tool for decision-making in realistic scenarios.

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

Aluminum alloys, known for their high strength-to-weight ratios and impressive electrical and thermal conductivities, are extensively used in numerous engineering sectors, such as aerospace, automotive, and construction. Recently, significant efforts have been made to develop novel aluminum alloys specifically tailored for additive manufacturing. These new alloys aim to provide an optimal balance between mechanical properties and thermal/ electrical conductivities. In this study, nine combinatorial samples with various alloy compositions were fabricated using direct energy deposition (DED) additive manufacturing by adjusting the feeding speeds of Al6061 alloy and Al-12Si alloy powders. The effects of the alloying elements on the microstructure, electrical conductivity, and hardness were investigated. Generally, as the Si and Cu contents decreased, electrical conductivity increased and hardness decreased, exhibiting trade-off characteristics. However, electrical conductivity and hardness showed an optimal combination when the Si content was adjusted to below 4.5 wt%, which can sufficiently suppress the grain boundary segregation of the α- Si precipitates, and the Cu content was controlled to induce the formation of Al2Cu precipitates.