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Enzymatic characterization of Paenibacillus amylolyticus xylanases GH10 and GH30 for xylan hydrolysis (Paenibacillus amylolyticus 유래 xylanase GH10 및 GH30의 xylan 가수분해 특성)

  • Nam, Gyeong-Hwa;Jang, Myoung-Uoon;Kim, Min-Jeong;Lee, Jung-Min;Lee, Min-Jae;Kim, Tae-Jip
    • Korean Journal of Microbiology
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    • v.52 no.4
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    • pp.463-470
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    • 2016
  • The enzymatic degradation of xylans is the most versatile way to obtain the high value-added functional compounds or the fermentable sugars for renewable energy. The endo-${\beta}$-xylanases are the major enzymes which hydrolyze the internal ${\beta}$-1,4-linkages of xylan backbones to produce the mixtures of xylooligosaccharides including xylobiose and xylotriose. Among them, glucuronoxylanase GH30 can exclusively hydrolyze the internal ${\beta}$-1,4-linkages of xylans decorated with methylglucuronic acid branches. In the present study, two xylanolytic enzyme (PaXN_10 and PaGuXN_30) genes were cloned from Paenibacillus amylolyticus KCTC 3005, and expressed in Escherichia coli, respectively. PaXN_10 (38.7 kDa) belongs to the endo-${\beta}$-xylanases GH10 family, while PaGuXN_30 (58.5 kDa) is a member of glucuronoxylanase GH30. They share the same optimal reaction conditions at $50^{\circ}C$ and pH 7.0. Enzymatic characterization proposed that P. amylolyticus can utilize the hardwood glucuronoarabinoxylans via the cooperative actions of xylanases GH10 and GH30. The extracellular PaGuXN_30 is secreted into the medium and hydrolyzes glucuronoarabinoxylans to release a series of aldouronic acid mixtures with a methylglucuronic acid branch. The resultant products being transported into the microbial cell are successively degraded into the smaller xylooligosaccharides by the intracellular PaXN_10, which will be utilized for the cellular metabolism.

Behavior of Nutrients and Heavy Metals (Cu, Zn) and Applicability Evaluation from Swine Wastewater Treatment Using Microalga Scenedesmus obliquus (미세조류 Scenedesmus obliquus 영양염류와 중금속(Cu, Zn) 거동특성 및 축산 폐수 처리 적용성 평가)

  • Park, Ji-Su;Hwang, In-Sung;Oh, Eun-Ji;Yoo, Jin;Chung, Keun-Yook
    • Applied Chemistry for Engineering
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    • v.30 no.2
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    • pp.226-232
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    • 2019
  • The biological wastewater treatment is more eco-friendly and can be used effectively in wastewater for a variety of purposes than that of the conventional treatment. In particular, the wastewater treatment using microalgae in biological treatment processes has attracted great attention due to its ability to remove economically nutrients from wastewater and have many advantages as a renewable energy source. This study was investigated to establish the optimal growth conditions for microalga Scenedesmus obliquus. Additionally, the removal efficiencies of nutrients (N, P) and heavy metals (Cu, Zn) from the synthetic wastewater were evaluated. As a results, the optimal growth conditions were established at $28^{\circ}C$, pH 7, and light and dark cycle of 14 : 10 h. In the evaluation of nutrient removal efficiencies at each concentrations of 500, 1,000, 5,000, and 10,000 mg/L, the removal rates were 17.6~70% N and 8.4~34% P in the single treatment and 12.0~58.0% N and 3.0~40.3% P in the binary mixture treatment. In addition, the evaluation of heavy metal removal efficiencies at each concentrations of 10, 30 and 50 mg/L, the removal rates were 13.7~40.3% Cu and 10.0~30.0% Zn in the single treatment and 16.0~40.0% Cu and 12.0~20.0% Zn in the binary mixture treatment. Based on the results of the study, it appears that Scenedesmus obliquus can be used for the removal of nutrients and heavy metals from the swine wastewater.

State of Health and State of Charge Estimation of Li-ion Battery for Construction Equipment based on Dual Extended Kalman Filter (이중확장칼만필터(DEKF)를 기반한 건설장비용 리튬이온전지의 State of Charge(SOC) 및 State of Health(SOH) 추정)

  • Hong-Ryun Jung;Jun Ho Kim;Seung Woo Kim;Jong Hoon Kim;Eun Jin Kang;Jeong Woo Yun
    • Journal of the Microelectronics and Packaging Society
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    • v.31 no.1
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    • pp.16-22
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    • 2024
  • Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

Assessment of Water Control Model for Tomato and Paprika in the Greenhouse Using the Penman-Monteith Model (Penman-Monteith을 이용한 토마토와 파프리카의 증발산 모델 평가)

  • Somnuek, Siriluk;Hong, Youngsin;Kim, Minyoung;Lee, Sanggyu;Baek, Jeonghyun;Kwak, Kangsu;Lee, Hyondong;Lee, Jaesu
    • Journal of Bio-Environment Control
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    • v.29 no.3
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    • pp.209-218
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    • 2020
  • This paper investigated actual crop evapotranspiration (ETc) of tomato and paprika planted in test beds of the greenhouse. Crop water requirement (CWR) is the amount of water required to compensate ETc loss from the crop. The main objectives of the study are to assess whether the actual crop watering (ACW) was adequate CWR of tomato and paprika and which amount of ACW should be irrigated to each crop. ETc was estimated using the Penman-Monteith model (P-M) for each crop. ACW was calculated from the difference of amount of nutrient supply water and amount of nutrient drainage water. ACW and CWR of each crop were determined, compared and assessed. Results indicated CWR-tomato was around 100 to 1,200 ml/day, while CWR-paprika ranged from 100 to 500 ml/day. Comparison of ACW and CWR of each crop found that the difference of ACW and CWR are fluctuated following day of planting (DAP). However, the differences could divide into two phases, first the amount of ACWs of each crop are less than CWR in the initial phase (60 DAP) around 500 ml/day and 91 ml/day, respectively. Then, ACWs of each crop are greater than the CWR after 60 DAP until the end of cultivation approximately 400 ml/day in tomato and 178 ml/day in paprika. ETc assessment is necessary to correctly quantify crop irrigation water needs and it is an accurate short-term estimation of CWR in greenhouse for optimal irrigation scheduling. Thus, reducing ACW of tomato and paprika in the greenhouse is a recommendation. The amount of ACW of tomato should be applied from 100 to 1,200 ml/day and paprika is 100 to 500 ml/day depend on DAP.

A Study on the Design of the Grid-Cell Assessment System for the Optimal Location of Offshore Wind Farms (해상풍력발전단지의 최적 위치 선정을 위한 Grid-cell 평가 시스템 개념 설계)

  • Lee, Bo-Kyeong;Cho, Ik-Soon;Kim, Dae-Hae
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.24 no.7
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    • pp.848-857
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    • 2018
  • Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

Blue Carbon Resources in the East Sea of Korea and Their Values and Potential Applications (동해안 블루카본 자원의 가치와 활용방안)

  • Yoon, Ho-Sung;Do, Jeong-Mi;Jeon, Byung Hee;Yeo, Hee-Tae;Jang, Hyeong Seok;Yang, Hee Wook;Suh, Ho Seong;Hong, Ji Won
    • Journal of Life Science
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    • v.32 no.7
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    • pp.578-587
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    • 2022
  • Korea, as the world's 7th largest emitter of greenhouse gases, has raised the national greenhouse gas reduction target as international regulations have been strengthened. As it is possible to utilize coastal and marine ecosystems as important nature-based solutions (NbS) for implementing climate change mitigation or adaptation plans, the blue carbon ecosystem is now receiving attention. Blue carbon refers to carbon that is deposited and stored for a long period after carbon dioxide (CO2) is absorbed as biomass by coastal ecosystems or oceanic ecosystems through photosynthesis. Currently, there are only three blue carbon ecosystems officially recognized by the Intergovernmental Panel on Climate Change (IPCC): mangroves, salt marshes, and seagrasses. However, the results of new research on the high CO2 sequestration and storage capacity of various new blue carbon sinks, such as seaweeds, microalgae, coral reefs, and non-vegetated tidal flats, have been continuously reported to the academic community recently. The possibility of IPCC international accreditation is gradually increasing through scientific verification related to calculations. In this review, the current status and potential value of seaweeds, seagrass fields, and non-vegetated tidal flats, which are sources of blue carbon on the east coast, are discussed. This paper confirms that seaweed resources are the most effective NbS in the East Sea of Korea. In addition, we would like to suggest the direction of research and development (R&D) and utilization so that new blue carbon sinks can obtain international IPCC certification in the near future.

Fuel characteristics of Yellow Poplar bio-oil by catalytic pyrolysis (촉매열분해를 이용한 백합나무 바이오오일의 연료 특성)

  • Chea, Kwang-Seok;Jeong, Han-Seob;Ahn, Byoung-Jun;Lee, Jae-Jung;Ju, Young-Min;Lee, Soo-Min
    • Journal of the Korean Applied Science and Technology
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    • v.34 no.1
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    • pp.1-11
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    • 2017
  • Bio-oil has attracted considerable interest as one of the promising renewable energy resources because it can be used as a feedstock in conventional petroleum refineries for the production of high value chemicals or next-generation hydrocarbon fuels. Zeolites have been shown to effectively promote cracking reactions during pyrolysis resulting in highly deoxygenated and hydrocarbon-rich compounds and stable pyrolysis oil products. In this study, catalytic pyrolysis was applied to upgrade bio-oil from yellow poplar and then fuel characteristics of upgraded bio-oil was investigated. Yellow Poplar(500 g) which ground 0.3~1.4 mm was processed into bio-oil by catalytic pyrolysis for 1.64 seconds at $465^{\circ}C$ with Control, Blaccoal, Whitecoal, ZeoliteY and ZSM-5. Under the catalyst conditions, bio-oil productions decreased from 54.0%(Control) to 51.4 ~ 53.5%, except 56.2%(Blackcoal). HHV(High heating value) of upgraded bio-oil was more lower than crude bio-oil while the water content increased from 37.4% to 37.4 ~ 45.2%. But the other properties were improved significantly. Under the upgrading conditions, ash and TAN(Total Acid Number) is decrease and particularly important as transportation fuel, the viscosity of bio-oil decreased from 6,933 cP(Control) to 2,578 ~ 4,627 cP. In addition, ZeoliteY was most effective on producing aromatic hydrocarbons and decreasing of from the catalytic pyrolysis.

Temperature and Solar Radiation Prediction Performance of High-resolution KMAPP Model in Agricultural Areas: Clear Sky Case Studies in Cheorwon and Jeonbuk Province (고해상도 규모상세화모델 KMAPP의 농업지역 기온 및 일사량 예측 성능: 맑은 날 철원 및 전북 사례 연구)

  • Shin, Seoleun;Lee, Seung-Jae;Noh, Ilseok;Kim, Soo-Hyun;So, Yun-Young;Lee, Seoyeon;Min, Byung Hoon;Kim, Kyu Rang
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.22 no.4
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    • pp.312-326
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    • 2020
  • Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.

The Heat Transfer Performance of a Heat Pipe for Medium-temperature Solar Thermal Storage System (중온 태양열 축열조용 히트파이프의 열이송 성능)

  • Park, Min Kyu;Lee, Jung Ryun;Boo, Joon Hong
    • 한국신재생에너지학회:학술대회논문집
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    • 2011.05a
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    • pp.69-69
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    • 2011
  • 태양열 발전 플랜트에 사용되는 중고온 범위의 축열조에 고체-액체간 상변화를 수행하는 용융염을 축열물질로 사용하면 액체상 또는 고체상만으로 된 열저장 매체에 비해 축열조의 규모를 축소함과 동시에 축열온도의 균일성 향상에 기여할 수 있다. 중온인 $250{\sim}400^{\circ}C$ 범위에서 이용 가능한 용융염으로는 질산칼륨($KNO_3$), 질산리튬($LiNO_3$)등이 있다. 그러나 이러한 용융염의 가장 큰 단점은 열전도율이 매우 낮다는 것이며, 이로 인해 요구되는 열전달률을 성취하기 위해서는 많은 열접촉면적이 필요하다는 것이다. 이러한 단점을 극복하는 방법을 도입하지 않고서는 축열시스템의 소규화를 성취하는데 큰 효과를 가져올 수 없다. 한편 열수송 성능이 탁월한 히트파이프를 사용하면 열원 및 열침과 축열물질 사이의 열전달 효율을 증가시켜 시스템의 성능 향상과 동시에 소규모화에 기여할 수 있다. 중온 범위 히트파이프의 작동유체로서 다우섬-A(Dowtherm-A)는 $150^{\circ}C$이상 $400^{\circ}C$까지의 범위에서 소수에 불과한 선택적 대안 중 하나이다. 따라서 본 연구에서는 용융염을 사용하는 중온 태양열축열조에 적용 가능한 다우섬-A 히트파이프의 성능을 파악하여 기술적 자료를 제시하고자 하였다. 열원으로는 고온 고압의 과열증기, 그리고 열침으로는 중온의 포화증기를 고려하였다. 용융염 축열조를 수직으로 관통하는 히트파이프는 하단부에서 열원 증기와 열교환 가능하며, 중앙부에서 축열물질과 열교환하고, 상단부에서는 중온 증기와 접촉할 수 있도록 배치하였다. 축열모드에서는 히트파이프의 하단부가 증발부로 작동하고, 중앙부가 응축부로 작동하여 용융염으로 열을 방출하면 용융염의 온도가 상승하고 용융점에 도달하면 액상으로의 상변화가 진행되면서 축열이 활성화된다. 축열모드에서 히트파이프의 상단부는 단열부로 작동한다. 방열과정에서는 히트파이프의 하단부가 단열된 상태이고, 중앙부는 용융염으로부터 열을 받아 증발부로 작동하며, 상단부는 중온 증기로 열을 방출하므로 응축부로 작동한다. 즉, 축열시스템의 작동모드에 따라 하나의 히트파이프에서 증발부, 응축부, 단열부의 위치가 변하게 된다. 특히, 히트파이프의 중앙 부분이 응축부에서 증발부로 전환될 때에도 작동이 보장되려면 내부 작동유체의 연속적인 재순환이 가능해야 하므로, 일반 히트파이프에서와는 달리 초기 작동액체의 충전량을 증발부 전체의 체적보다 더 많이 과충전해야 한다. 이러한 히트파이프의 성능 파악을 위한 실험에서 고려한 변수들은 열부하, 작동액체의 충전률, 작동온도 등이며, 열수송 성능의 지표로서는 유효열전도율과 열저항을 이용하였다. 중온범위에서 적정한 작동온도를 성취하기 위해 실험에서는 전압 조절기로 열부하를 조절하는 동시에 항온조로 응축부의 냉각수 입구 온도를 제어하였다. 하나의 히트파이프에 대해서 최대 1 kW까지의 열부하에서 냉각수 입구 온도를 $40^{\circ}C$에서 $80^{\circ}C$ 범위로 변화시키면 히트파이프 작동온도를 약 $250^{\circ}C$ 내외로 조절 가능하였다. 히트파이프 작동액체 충전률은 윅구조물의 공극 체적을 기준으로 372%에서 420%까지 변화 시켰다. 실험 결과를 토대로 열저항과 유효 열전도율을 각각 입력 열유속, 작동온도, 작동액체 충전률 등의 함수로 제시했다. 동일한 냉각수 온도에서는 충전률이 높을수록 히트파이프의 작동온도가 감소하였다. 열저항 값의 범위는 최소 $0.12^{\circ}C/W$에서 최대 $0.15^{\circ}C/W$까지로 나타났으며 유효 열전도율의 값은 최소 $7,703W/m{\cdot}K$에서 최대 $8,890W/m{\cdot}K$까지 변화했다. 최소 열저항은 충전률 420%인 경우에 나타났는데 이때의 작동온도는 약 $262^{\circ}C$이었다. 히트파이프의 작동한계로서 드라이아웃(dry-out)은 충전률 372%의 경우에 열부하 950 W에서 발생하였으나, 그 이상의 충전률에서는 열부하 1060 W까지 작동한계 발생이 관찰되지 않았다. 실험 결과 본 연구에서의 히트파이프는 중온 태양열 축열조에 적용되어 개당 약 1 kW의 열부하를 이송하면서 축열물질 및 축방열 대상 유동매체와 열교환을 하는데 사용하는데 충분할 것이라 판단된다.

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Basic Research on the Possibility of Developing a Landscape Perceptual Response Prediction Model Using Artificial Intelligence - Focusing on Machine Learning Techniques - (인공지능을 활용한 경관 지각반응 예측모델 개발 가능성 기초연구 - 머신러닝 기법을 중심으로 -)

  • Kim, Jin-Pyo;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.51 no.3
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    • pp.70-82
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    • 2023
  • The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.