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잉여슬러지의 열적가용화를 통한 가용화 및 혐기성소화 생분해도 향상
정성엽(Jeong, Seongyeob),정석영(Jung, Sukyoung),장순웅(Chang, Soonwoong) 한국신재생에너지학회 2014 신재생에너지 Vol.10 No.1
The present study investigated the effects of thermal pre-treatment on the enhancement of anaerobic biodegradability of waste activated sludge at varied TS concentration levels. The activated sludges were thermally oxidized for 30 minutes at 80{sim}200?C with varied TS concentrations (2%, 4% and 6%). and then, sludge characteristics, solubilization efficiency and methane production yield of thermally pre-treated sludges were analyzed. The higher the temperature in the thermal pre-treatment, the higher the concentration levels of dissolved matters such as SCOD_{Cr}, NH₄{^+} and VFAs, which indicates that the thermal pre-treatment facilitates the hydrolysis and acid fermentation. Furthermore, the solubilization efficiency was increased in proportion to the temperature rise at all TS concentrations and was reached at 68.9%, 55.6% and 53.1%, respectively, at 200?C. In the BMP test of the pre-treated sludges, higher methane production yields were observed as 0.313. 0.314 and 0.299m³;CH₄/kg;VS_{add} at the condition of TS 2% (160?C), 4% (160?C) and 6% (180?C), respectively, and degradation rate was increased by 84%, 79% and 65% compared with non-pretreated waste activated sludge. These findings suggest the effectiveness of thermal pre-treatment of waste activated sludge for anaerobic biodegradable process.
건설폐기물, 생활폐기물의 용출특성 분석과 BMP test를 통한 최종메탄(CH<SUB>4</SUB>) 및 황화수소(H<SUB>2</SUB>S) 수율 산정
정석영(Jung, Sukyoung),정성엽(Jeong, Seongyeob),장순웅(Chang, Soonwoong) 한국신재생에너지학회 2014 신재생에너지 Vol.10 No.1
The main object of this study was to offer information about incoming waste in landfill and to evaluate biochemical methane and hydrogen sulfide potentials of landfill wastes. We examined brick, soil, mixed waste (C&D waste and MSW) samples for the study. The leaching experiments showed that BOD, COD and sulfate were determined in the range of 0~18,816 mg/kg, 85~21,100 mg/kg and 160~1,205 mg/kg, respectively in 6hr extraction test. An accumulated extraction tests for 140day were determined BOD 226~197,219 mg/kg, COD 436~242,588 mg/kg and Sulfate 1,090~25,140 mg/kg. Also, BMP (biochemical methane potential) tests were carried out to examine methane and hydrogen sulfide yields for the 3 different wastes. As a result, methane yield was determined to 262.68 mL CH₄/g VS of MSW and 0~17.75 mL CH₄/g VS in brick, soil and C&D waste. Higher hydrogen sulfide yield was observed to 0.079mL H₂S/g VS in C&D waste. This result indicate that brick and soil could be sources of sulfate, and higher production of hydrogen sulfide could be odor problem and inhibitor of methane production.
Shin, Jaekwon,Kim, Jintae,Lee, Beomhee,Lee, Junghoon,Lee, Jisung,Jeong, Seongyeob,Chang, Soonwoong The Institute of Internet 2018 International journal of advanced smart convergenc Vol.7 No.1
Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.
Jaekwon Shin,Jintae Kim,Beomhee Lee,Junghoon Lee,Jisung Lee,Seongyeob Jeong,Soonwoong Chang 한국인터넷방송통신학회 2018 Journal of Advanced Smart Convergence Vol.7 No.1
Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.