• 한국건축시공학회지
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• 제22권6호
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• pp.681-688
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• 2022

본 연구에서는 도면정보나 건축물 정보가 불확실한 노후 지역의 건설폐기물 발생량 예측을 위하여 드론촬영, 자동 체적 산출, 연면적 간접 변환, 폐기물량 예측의 과정을 QGIS 플러그인 형태로 구축하였다. 이를 위해 모의검증을 통하여 드론 활용 자동 산출된 체적과 현장실측 기반 BIM 체적과의 높은 일치성을 확인하였다. 실제 해체예정지를 대상으로 현장검증을 실시하고 드론 기반 체적과 실측-BIM기반 체적의 일치성을 재확인하였다. 층고와 건축물 유형 설정을 통하여 폐기물 발생 원단위를 적용하고 직간접적인 방식으로 폐기물량을 산출하였고, 전체 과정은 드론 촬영과 산출 자동화 과정을 포함하여 면적 1,200,000m² 대상지를 6시간 이내의 작업시간으로 신속하게 처리하였다. 비록 건축물 정보와 드론을 통한 건물 객체의 차이는 가설물, 적재물, 층고 설정에 따라 오차가 발생하였지만, 실제 해체물량도 최초 예측보다 더 크게 발생할 것으로 파악되었으며, 향후 사례조사를 통하여 체적-연면적 변환의 정확도 향상에 대한 개선이 필요할 것이다.

• Environmental Analysis Health and Toxicology
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• 제19권2호
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• pp.227-233
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• 2004

The main objectives of this study were to identify from literature review the potential sources and to provide a preliminary national emission inventory for the unintentionally produced polychlorinated biphenyls (PCBs) (i.e., by - product PCBs). In Korea, fuel combustion, waste combustion, thermal industrial processes, and transportation were identified as potential sources of by -product PCB s. According to the availability of the emission factors and/or activity data, emission inventory could be assessed only for fuel combustion, waste combustion, steel industry, non-ferrous industry, and non-metallurgical industry. The total national emission of by-product PCBs was estimated to be 1087kg for the year 2000. The preliminary estimation further indicated that the steel manufacturing was the single dominant emission category, contributing 93％ to the total emission. Of the steel manufacturing processes, the contribution of the electric arc furnace was about 80％ of the total emission. Due to high uncertainty associated with both the emission factors and activity statistics, the emission estimates in this study are likely to contain significant errors. However, the results of the present work could serve the first step toward future efforts to establish national source and emission inventories of by-product PCBs.

• 방사성폐기물학회지
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• 제4권4호
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• pp.385-391
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• 2006

연구용원자로 해체비용은 해체대상물에 대한 특성 및 제원에 맞게 해체작업을 분류하고 구성요소를 설정하여 단위비용인자를 바탕으로 한 공학적 비용 산정 방법으로 해체비용을 산정한다. 연구용원자로에 대한 해체비용은 크게 인건비, 장비 및 재료비로 구성이 되는데 해체작업에 소요되는 인건비는 해체대상물에 소요되는 작업시간을 바탕으로 계산을 한다. 본 논문에서는 연구용원자로 해체비용 산정 시 인건비 계산에 필요한 단위비용인자 및 작업 난이도 인자를 산출하였다.

• 방사성폐기물학회지
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• 제19권2호
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• pp.243-253
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• 2021

In the dismantling process of a reactor coolant system (RCS) piping, a radiation protection plan should be established to minimize the radiation exposure doses of dismantling workers. Hence, it is necessary to estimate the individual effective dose in the RCS piping dismantling process when decommissioning a nuclear power plant. In this study, the radiation exposure doses of the dismantling workers at different positions was estimated using the MicroShield dose assessment program based on the NUREG/CR-1595 report. The individual effective dose, which is the sum of the effective dose to each tissue considering the working time, was used to estimate the radiation exposure dose. The estimations of the simulation results for all RCS piping dismantling tasks satisfied the dose limits prescribed by the ICRP-60 report. In dismantling the RCS piping of the Kori-1 or Wolsong-1 units in South Korea, the estimation and reduction method for the radiation exposure dose, and the simulated results of this study can be used to implement the radiation safety for optimal dismantling by providing information on the radiation exposure doses of the dismantling workers.

• Steel and Composite Structures
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• 제49권6호
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.614-616
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• 2004

It is waste of time in industrial plant that the PI controller gain tuning. The PI controller has many trial-and-error steps for gain design. This paper proposes the optimum gain design of PI controller using a speed estimation in sensorless vector-control. In this method, a degree of stability and Hurwitz theory are applied and the controller gain is expressed by system parameters.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• 한국유기농업학회지
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• 제20권4호
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• pp.447-458
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• 2012

This study was carried out to evaluation of biomass generation mechanisms and to propose the estimation method of biomass generation. Agricultural by-product biomass is generated during crops cultivation and after harvest. However these are not uniformly generated yearly and these depending on the seasons. For planning of biomass utilization, accurate information of the biomass resources is needed, especially characteristic and productivity of biomass are necessary. Agricultural by-product biomass are generated in a wide area being scattered and it is one of the major reason why agricultural biomass utilization is not activated compared with other waste biomass. In this study, estimation and evaluation biomass generation is achieved in specific spatial and temporal boundary, A-city in Gyeongi-do and september to November respectively. Quantity and quality of by-product biomass show big difference depending on the crop species and cultivation periods and these difference bring up that accurate biomass estimation should be considered during planning of biomass utilization and technology selecting for biomass converting to energy and other forms.

• 한국대기환경학회지
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• 제26권6호
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• pp.657-665
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• 2010

Carbon dioxide ($CO_2$) is known to be a major greenhouse gas partially emitted from waste combustion facilities. According to the greenhouse gas emission inventory in Korea, the quantity of the gas emitted from waste sector in 2005 represents approximately 2.5 percent of all domestic greenhouse gas emission. Currently, the emission rate of greenhouse gas from the waste sector is relatively constant partly because of both the reduced waste disposal in landfills and the increased amounts of waste materials for recycling. However, the greenhouse gas emission rate in waste sectors is anticipated to continually increase, mainly due to increased incineration of solid waste. The objective of this study was to analyze the property of Municipal Solid Waste (MSW) and estimate $CO_2$ emissions from domestic MSW incineration facilities. The $CO_2$ emission rates obtained from the facilities were surveyed, along with other two methods, including Tier 2a based on 2006 IPCC Guideline default emission factor and Tier 3 based on facility specific value. The $CO_2$ emission rates were calculated by using $CO_2$ concentrations and gas flows measured from the stacks. Other parameters such as waste composition, dry matter content, carbon content, oxidation coefficient of waste were included for the calculation. The $CO_2$ average emission rate by the Tier 2a was 34,545 ton/y, while Tier 3 was 31,066 ton/y. Based on this study, we conclude that Tier 2a was overestimated by 11.2 percent for the $CO_2$ emission observed by Tier 3. Further study is still needed to determine accurate $CO_2$ emission rates from municipal solid waste incineration facilities and other various combustion facilities by obtaining country-specific emission factor, rather than relying on IPCC default emission factor.

• 한국폐기물자원순환학회지
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• 제35권7호
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• pp.606-615
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• 2018

Over the past two decades, the options for solid waste management have been changing from land disposal to recycling, waste-to-energy, and incineration due to growing attention for resource and energy recovery. In addition, the reduction of greenhouse gas (GHG) emission has become an issue of concern in the waste sector because such gases often released into the atmosphere during the waste management processes (e.g., biodegradation in landfills and combustion by incineration) can contribute to climate change. In this study, the emission and reduction rates of GHGs by the municipal solid waste (MSW) management options in D city have been studied for the years 1996-2016. The emissions and reduction rates were calculated according to the Intergovernmental Panel on Climate Change guidelines and the EU Prognos method, respectively. A dramatic decrease in the waste landfilled was observed between 1996 and 2004, after which its amount has been relatively constant. Waste recycling and incineration have been increased over the decades, leading to a peak in the GHG emissions from landfills of approximately $63,323tCO_2\;eq/yr$ in 2005, while the lowest value of $35,962tCO_2\;eq/yr$ was observed in 2016. In 2016, the estimated emission rate of GHGs from incineration was $59,199tCO_2\;eq/yr$. The reduction rate by material recycling was the highest ($-164,487tCO_2\;eq/yr$) in 2016, followed by the rates by heat recovery with incineration ($-59,242tCO_2\;eq/yr$) and landfill gas recovery ($-23,922tCO_2\;eq/yr$). Moreover, the cumulative GHG reduction rate between 1996 and 2016 was $-3.46MtCO_2\;eq$, implying a very positive impact on future $CO_2$ reduction achieved by waste recycling as well as heat recovery of incineration and landfill gas recovery. This study clearly demonstrates that improved MSW management systems are positive for GHGs reduction and energy savings. These results could help the waste management decision-makers supporting the MSW recycling and energy recovery policies as well as the climate change mitigation efforts at local government level.