• Clean Technology
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• v.27 no.4
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• pp.341-349
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• 2021

In combustion facilities, the nitrogen and sulfur in fossil fuels react with oxygen to generate air pollutants such as nitrogen oxides (NO_X) and sulfur oxides (SO_X), which are harmful to the human body and cause environmental pollution. There are regulations worldwide to reduce NO_X and SO_X, and various technologies are being applied to meet these regulations. There are commercialized methods to reduce NO_X and SO_X emissions such as selective catalytic reduction (SCR), selective non-catalytic reduction (SNCR) and wet flue gas desulfurization (WFGD), but due to the disadvantages of these methods, many studies have been conducted to simultaneously remove NO_X and SO_X. However, even in the NO_X and SO_X simultaneous removal methods, there are problems with wastewater generation due to oxidants and absorbents, costs incurred due to the use of catalysts and electrolysis to activate specific oxidants, and the harmfulness of gas oxidants themselves. Therefore, in this research, microbubbles generated in a high-pressure disperser and reducing agents were used to reduce costs and facilitate wastewater treatment in order to compensate for the shortcomings of the NO_X, SO_X simultaneous treatment method. It was confirmed through image processing and ESR (electron spin resonance) analysis that the disperser generates real microbubbles. NO_X and SO_X removal tests according to temperature were also conducted using only microbubbles. In addition, the removal efficiencies of NO_X and SO_X are about 75% and 99% using a reducing agent and microbubbles to reduce wastewater. When a small amount of oxidizing agent was added to this microbubble system, both NO_X and SO_X removal rates achieved 99% or more. Based on these findings, it is expected that this suggested method will contribute to solving the cost and environmental problems associated with the wet oxidation removal method.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.10-24
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• 2021

Purpose: The purpose of this study is to determine the dangers of biodiesel and general diesel mixtures currently used as alternative fuels by equipment (tag method and penski Marten method) and to determine the difference between flash point and combustion point (closed, open) according to test methods. It is intended to be used as a reference material for identification and evaluation of firecausing substances by confirming the risk of mixtures by comparative analysis and measurement, and establishing a risk assessment method for chemical substances. Method: Flash point test method and result treatment were tested based on ASTM and KS M mode, which are tag sealing and pen schematense test methods used as flash point and combustion point test methods for crude oil and petroleum products. The manufacturer of the equipment used in this experiment was a test equipment that satisfies the test standards of KS M 2010 with equipment produced by TANAKA of Japan. The flash point and combustion point were measured, and the flash point according to the test method of biodiesel and general diesel mixture ( Closed, open), and the ignition point of a mixture of biodiesel and general diesel was compared and analyzed for ignition risk compared with conventional diesel. Results: Looking at the experimental results, first, as an analysis of the risk of flammability of the mixture, the flash point of a substance containing 70% biodiesel was found to be about 92℃ based on general diesel with a flash point of 64.5℃, and gasoline and biodiesel or When the biodiesel mixture was synthesized, it was confirmed that the flash point tends to decrease. In addition, the difference between the flash point and the combustion point was analyzed as about 20 ~ 30℃, and when a small amount of gasoline or methanol was mixed, the flash point was lowered, but it was confirmed that the combustion point was similar to that of the existing mixture. Conclusion: In this study, in order to secure the effectiveness of the details of the criteria for judging dangerous materials in the existing Dangerous Materials Safety Management Act, and to secure the reliability and reproducibility of the judgment of dangerous materials, we confirm the criteria for judging the risk of the mixture through an experimental study on flammable mixtures. It will be able to provide reference data for experimental criteria for flammable liquids that are regulated in the field. In addition, if this study accumulates know-how on experiment by test method, it is expected that it can be used as a basis for research on risk assessment and research on dangerous goods.

• Korean Chemical Engineering Research
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• v.59 no.1
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• pp.127-137
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• 2021

Air emission charge for nitrogen oxide as a precursor of fine dust has been introduced and implemented within the country from 2020. Therefore, the development of economical combustion technology for NOx reduction has got more needed urgently. This study investigated the air-staging effect as a way to reduce the NOx during combustion of domestic unused forest biomass, recently possible to secure REC (Renewable Energy Certification) as a substitute for overseas wood pellets in a 0.1 MWth circulating fluidized bed combustion test-rig. Operating conditions were comparison with and without air-staging, the supply position of tertiary air (6.4 m, 8.1 m, 9.4 m in the combustor) and variation of air-staging ratio (Primary air:Secondary air:Tertiary air=91%:9%:0%, 82%:9%:9%, 73%:9%:18%). NO and CO concentrations in flue gas, profiles of temperature and pressure at the height of the combustion, unburned carbon in sampled fly ash and combustion efficiency on operating conditions were evaluated. As notable results, NO concentration with air-staging application under tertiary air supply at 9.4 m in the combustor reduced 100.7 ppm compared to 148.8 ppm without air-staging while, CO concentration increased from 52.2 ppm without air-staging to 99.8 ppm with air-staging. However, among air-staging runs, when tertiary air supply amount at 6.4 m in the combustor increased by air-staging ratio (Primary air:Secondary air:Tertiary air=73%:9%:18%), NO and CO concentrations decreased the lowest 90.8 ppm and 66.1 ppm, respectively. Furthermore, combustion efficiency at this condition was improved to 99.3%, higher than that (98.3%) of run without air-staging.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.36-43
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• 2024

Fishing net waste (FNW) constitutes over half of all marine plastic waste and is a major contributor to the degradation of marine ecosystems. While current treatment options for FNW include incineration, landfilling, and mechanical recycling, these methods often result in low-value products and pollutant emissions. Importantly, FNWs, comprised of plastic polymers, can be converted into valuable resources like syngas and pyrolysis oil through pyrolysis. Thus, this study presents a process for generating high-purity hydrogen (H₂) by catalytically pyrolyzing FNW in a CO₂ environment. The proposed process comprises of three stages: First, the pretreated FNW undergoes Ni/SiO₂ catalytic pyrolysis under CO₂ conditions to produce syngas and pyrolysis oil. Second, the produced pyrolysis oil is incinerated and repurposed as an energy source for the pyrolysis reaction. Lastly, the syngas is transformed into high-purity H₂ via the Water-Gas-Shift (WGS) reaction and Pressure Swing Adsorption (PSA). This study compares the results of the proposed process with those of traditional pyrolysis conducted under N₂ conditions. Simulation results show that pyrolyzing 500 kg/h of FNW produced 2.933 kmol/h of high-purity H₂ under N₂ conditions and 3.605 kmol/h of high-purity H₂ under CO₂ conditions. Furthermore, pyrolysis under CO₂ conditions improved CO production, increasing H₂ output. Additionally, the CO₂ emissions were reduced by 89.8% compared to N₂ conditions due to the capture and utilization of CO₂ released during the process. Therefore, the proposed process under CO₂ conditions can efficiently recycle FNW and generate eco-friendly hydrogen product.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.