• Journal of the Korean Wood Science and Technology
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• 제47권3호
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• pp.344-359
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• 2019

Effects of various types of zeolite on the catalytic performance of hydrodeoxygenation (HDO) of bio-oil obtained from waste larch wood pyrolysis were investigated herein. Bifunctional catalysts were prepared via wet impregnation. The catalysts were characterized through XRD, BET, and SEM. Experimental results demonstrated that HDO enhanced the fuel properties of waste wood bio-oil, such as higher heating values (HHV) (20.4-28.3 MJ/kg) than bio-oil (13.7 MJ/kg). Water content (from 19.3 in bio-oil to 3.1-16.6 wt% in heavy oils), the total acid number (from 150 in bio-oil to 28-77 mg KOH/g oil in heavy oils), and viscosity (from 103 in bio-oil to $40-69mm^2/s$ in heavy oils) also improved post HDO. In our experiments, depending on the zeolite support, NiFe/HBeta exhibited a high Si/Al ratio of 38 with a high specific surface area ($545.1m^2/g$), and, based on the yield of heavy oil (18.3-18.9 wt%) and HHV (22.4-25.2 MJ/kg), its performance was not significantly affected by temperature and solvent concentration variations. In contrast, NiFe/zeolite Y, which had a low Si/Al ratio of 5.2, exhibited the highest improved quality for heavy oil at high temperature, with an HHV of 28.3 MJ/kg at $350^{\circ}C$ with 25 wt% of solvent.

• 신재생에너지
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• 제18권2호
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• pp.1-8
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• 2022

This study aimed to determine the optimal conditions for fabricating pitch pine (PCP) and Mongolian oak (MOK) pellets using chestnut-shell tea waste (CSW) and castor oil (CSO) as additives. For pellets fabricated using a pilot-scale flat-die pellet mill, all moisture content (MC) was in line with A1 wood pellet standards for residential and small-scale commercial uses designated by the National Institute of Forest Science at the Republic of Korea (NIFOS), regardless of fabricating conditions; the durability of PCP pellets prepared using PCP particles with 10% MC, and CSW addition also satisfied these criteria. The moisture tolerance of PCP pellets improved with combination of 2 wt% CSW and 2-6 wt% CSO. Overall, use of 20 mesh CSW as an additive, PCP with 10% MC, and MOK with 12% MC was found to be optimal. Moreover, using CSO as an additive, high-quality PCP and MOK pellets can be fabricated by adjusting the particles to 12% MC. However, the durability of PCP and MOK pellets prepared using these conditions did not meet the wood pellet standards for residential and small-scale commercial use. Therefore, further research is needed to improve the durability of these pellets.

• Journal of Microbiology and Biotechnology
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• 제32권10호
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• pp.1325-1334
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• 2022

Global warming has accelerated in recent decades due to the continuous consumption of petroleum-based fuels. Cyanobacteria-derived biofuels are a promising carbon-neutral alternative to fossil fuels that may help achieve a cleaner environment. Here, we propose an effective strategy based on the large-scale cultivation of a newly isolated cyanobacterial strain to produce phycobiliprotein and biodiesel, thus demonstrating the potential commercial applicability of the isolated microalgal strain. A native cyanobacterium was isolated from Goryeong, Korea, and identified as Pseudanabaena mucicola GO0704 through 16s RNA analysis. The potential exploitation of P. mucicola GO0704 was explored by analyzing several parameters for mixotrophic culture, and optimal growth was achieved through the addition of sodium acetate (1 g/l) to the BG-11 medium. Next, the cultures were scaled up to a stirred-tank bioreactor in mixotrophic conditions to maximize the productivity of biomass and metabolites. The biomass, phycobiliprotein, and fatty acids concentrations in sodium acetate-treated cells were enhanced, and the highest biodiesel productivity (8.1 mg/l/d) was achieved at 96 h. Finally, the properties of the fuel derived from P. mucicola GO0704 were estimated with converted biodiesels according to the composition of fatty acids. Most of the characteristics of the final product, except for the cloud point, were compliant with international biodiesel standards [ASTM 6761 (US) and EN 14214 (Europe)].

• 한국분말재료학회지
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• 제29권1호
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• pp.22-27
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• 2022

Conventionally, metal materials are produced by subtractive manufacturing followed by melting. However, there has been an increasing interest in additive manufacturing, especially metal 3D printing technology, which is relatively inexpensive because of the absence of complicated processing steps. In this study, we focus on the effect of varying powder size on the synthesis quality, and suggest optimum process conditions for the preparation of AlCrFeNi high-entropy alloy powder. The SEM image of the as-fabricated specimens show countless, fine, as-synthesized powders. Furthermore, we have examined the phase and microstructure before and after 3D printing, and found that there are no noticeable changes in the phase or microstructure. However, it was determined that the larger the powder size, the better the Vickers hardness of the material. This study sheds light on the optimization of process conditions in the metal 3D printing field.

• 한국전자통신학회논문지
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• 제17권4호
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• pp.625-632
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• 2022

국내 전력계통에 부하추종운전 중인 가스터빈 발전기들은 정격 주파수 유지에 큰 역할을 하고 있지만 종종 주파수 제어가 불량한 경우도 발생한다. 이에 가스터빈 속도제어 장치인 조속기의 제어 특성을 살펴보고 고장 유형을 분석한 후 사안 별로 대책을 제시하였다. 그리고, 가스터빈은 연료제어의 빠르기에 따라 주파수 회복에는 도움을 되지만 연소실 온도가 급격하게 변동하고 연소진동이 발생하는 등 안정운전을 저해하는 요소로 작용하기도 한다는 것을 조속기 응동시험을 통해 확인하였다. 따라서, 안정적인 전력품질을 유지하기 위해서는 철저한 설비관리를 수행하고 비례 적분 미분 제어방식에 4차산업의 핵심분야인 기계학습 알고리즘이 융합된 조속기 제어방식의 연구 필요성이 대두되었다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• 현장농수산연구지
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• 제10권1호
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• pp.169-181
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• 2008

The purpose of this study was to produce Pleurotus ostreatus using fermenter with bag cultivation. These results are as follows. 1. While mushroom composts were being fermented in a fermenter, the physical property of the fermented composts was getting better when there isn't any screw or revolving flies in the fermenter and the strength of pressing the composts was getting less. 2. The composts were fermented well as slaked lime of 1% density added to the composts. 3. According to the result of examining our fermenting ways, composts were in the best condition after being fermented for 48 hours since the temperature in a fermenter has come to 60℃, which could be reached by heating the fermenter by 40℃ after putting compost materials and water into it. 4. The good condition of fermenting could be maintained by controlling the speed of revolving flies, therefore the speed be down when the temperature is above 60℃ and up bellow 60℃. 5. Since the composts had been added with 1.5~2% of cottonseed meal or rice bran, the fermented composts were in good condition and also the quantity and quality of the mushroom produced on the fermented composts were satisfied. 6. There were needed 7 hours of labour for 3days from the first day of putting composts into a fermenter for fermenting 3.5M/T(10,000~12,000bags of 750~800g per bag) of composts to the third day of finishing the fermenting work, and also the cost was 112,066￦(130$) including 52,066￦(60$) of electric charge and fuel expense.

• 한국산업보건학회지
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• 제33권4호
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• pp.419-426
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• 2023

Objectives: This study aims to develop an Occupational Safety and Health (OSH) guide for the safe cleaning of contaminated machinery, equipment, and parts used in the electronics manufacturing process. Methods: A literature review, field investigations, and discussions were conducted. An initial draft of an OSH guide was developed and reviewed by experts with significant experience in maintenance work in the electronics manufacturing process in order to refine the guide. Results: Workers involved in cleaning processes with chemicals, solvents, and abrasive blasting can face exposure to a wide range of chemicals, abrasives, and noise. Identifying potential risks associated with each cleaning technique was an essential first step toward enhancing safety measures. The OSH guide comprises approximately eleven to twelve sections spanning 20-25 pages. It includes engineering and administrative protocols systematically organized to address the necessary actions before, during, and after cleaning tasks, depending on the technique. It is recommended that airline respirator masks be used in conjunction with an air purification system to ensure adherence to air quality standard "D" for atmosphere level. The use of an oil-free air compressor is advised, preferably a stationary model that does not rely on fuel sources like diesel. Conclusions: This OSH guide is designed to protect workers involved in maintenance activity in the electronics industry and aligns with global standards, such as those from the International Organization for Standardization (ISO) and Semiconductor Equipment and Material International, ensuring a higher level of safety and compliance.

• 한국결정성장학회지
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• 제33권6호
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• pp.210-215
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• 2023

조합 원료의 소성성은 시멘트 제조 기술에서 매우 중요한 요소이며, 각 원료들의 물리적, 화학적 및 광물학적 특성에 의해 좌우된다. 본 연구에서는 국내산 석탄재와 일본산 석탄재를 시멘트 원료로 사용했을 경우, Lafarge 평가 방법과 Polysius 평가 방법을 이용하여 소성성을 비교하였다. 석탄재의 종류와 투입량에 관계없이 석탄재를 사용하였을 경우가 석탄재를 사용하지 않았을 때 보다 소성성이 향상됨을 알 수 있었다. 모든 종류의 석탄재에서 투입량이 증가할수록 소성성이 향상되었으나, 3 %까지 소성성의 향상이 뚜렷하였고 4 % 첨가 시에는 향상 폭이 상대적으로 크지 않았다. 결론적으로 시멘트의 품질이 허용하는 범위 내에서 석탄재의 투입량이 증가할수록 소성성이 향상되며, 따라서 클링커의 소성에 필요한 연료비의 절감도 기대할 수 있다.