In the context of the Ministry of Environment’s 2022 Climate Change Adaptation Plan for Public Institutions, public sewage treatment plants are one of the important targets for climate change response aimed at sustainable water management. In this study, it is applied a modified methodology to four water regeneration centers (public sewage treatment facilities) in charge of sewage treatment in Seoul to analyze the impacts and risks of climate change and discuss priorities for adaptation measures. The results of the study showed that heavy rains, heat waves, and droughts will be the key impacts of climate change, and highlighted the need for measures to mitigate these risks, especially for facility managers.
In this study, an evaluation system that can be used to evaluate the feasibility of developing and supplying hydrothermal energy for the operation of large-scale complex facilities was developed. To this end, this study derived factors to be considered when selecting a location for the use of hydrothermal energy using raw water from multi-purpose dams and regional water supply systems through literature survey and expert interviews. The evaluation indicators derived from this study are divided into four sectors: hydrothermal energy utilization factors, location factors, planning factors, and disaster safety factors, and are composed of 10 mid-level indicators and 34 detailed planning indicators. The relative importance of all factors was derived using the Analytic Hierarchy Process (AHP) technique, and the developed evaluation indicators and relative importance were applied to four multi-purpose dam regions in the country. As a result, it was found that in the development and use of hydrothermal energy utilizing regional raw water supply line the urban planning conditions of the supply site can have a greater impact on the location selection results than the hydrothermal energy development itself. Due to the characteristics of the evaluation indicators developed in this study and their nature as comprehensive indicators, it is believed that the results should be applied to determine the overall adequacy of site selection in the early stages of hydrothermal energy development. In the future, it is believed that it will be necessary to analyze the problems in supplying and operating hydrothermal energy using raw water from multi-purpose dams and regional water resources. Based on the analysis the evaluation system developed in this study is expected to be improved and supplemented.
In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.
This study proposes the use of a cobalt-based Prussian blue analogue (Co-PBA; potassium cobalt hexacyanoferrate), as an adsorbent for the cost-effective recovery of aqueous ammonium ions. The characterization of Co-PBA involved various techniques, including Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, nitrogen adsorption-desorption analysis, and zeta potential. The prepared Co-PBA reached an adsorption equilibrium for ammonium ions within approximately 480 min, which involved both surface adsorption and subsequent diffusion into the interior. The isotherm experiment revealed a maximum adsorption capacity of 37.29 mg/g, with the Langmuir model indicating a predominance of chemical monolayer adsorption. Furthermore, the material consistently demonstrated adsorption efficiency across a range of pH conditions. Notably, adsorption was observed even when competing cations were present. Co-PBA emerges as a readily synthesized adsorbent, underscoring its efficacy in ammonium removal and selectivity toward ammonium.
In this study, various pre-treatment methods were evaluated for microalgae separation. These methods aimed to facilitate safe, rapid, and cost-effective online imaging for real-time observation and cell counting. As pre-treatment techniques, heating, chemical hydrolysis, heating combined with chemical hydrolysis, and sonication were employed. The effectiveness of these methods was evaluated in the context of online imaging quality through experimentation on cultivated microalgae (Chlorella vulgaris and Scenedesmus quadricauda). The chemical treatment method was found to be inappropriate for improving image acquisition. The heating pre-treatment method exhibited a drawback of prolonged cell dispersion time. Additionally, the heating combined with chemical hydrolysis method was confirmed to have the lowest dispersion effect for Chlorella vulgaris. Conversely, ultrasonication emerged as a promising technique for microalgae separation in terms of repeatability and reproducibility. This study suggests the potential for selecting optimal pre-treatment methods to effectively operate real-time online monitoring devices, paving the way for future research and applications in microalgae cultivation and imaging.