| Project/Area Number |
12450331
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
生物・生体工学
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| Research Institution | Nagoya University |
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Principal Investigator |
KOBAYASHI Takeshi Graduate School of Engineering, Nagoya University Professor, 工学研究科, 教授 (10043324)
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| Co-Investigator(Kenkyū-buntansha) |
IRITANI Eiji Graduate School of Engineering, Nagoya University Professor, 工学研究科, 教授 (60144119)
HONDA Hiroyuki Graduate School of Engineering, Nagoya University Associate Professor, 工学研究科, 助教授 (70209328)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥15,000,000 (Direct Cost: ¥15,000,000)
Fiscal Year 2001: ¥10,400,000 (Direct Cost: ¥10,400,000)
Fiscal Year 2000: ¥4,600,000 (Direct Cost: ¥4,600,000)
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| Keywords | activated sludge process / prediction model / nitrification / bulking microorcanisms / artificial neural network / red tide / sedimentation agents / 硝化・脱窒 / バルキング原因微生物 / 廃水処理 / ニューラルネットワーク / COD / 磁性微粒子 / 磁気分離 / 硝化菌 |
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| Research Abstract |
Activated sludge process is often applied for domestic waste water treatment/ but many problems are remaining to be solved for improvement of the activated sludge process. This research combined knowledge engineering techniques with biotechnology techniques. At first, a prediction model for effluent COD from an activated sludge process was constructed by applying recursive fuzzy neural network and the operating condition was estimated accurately by applying genetic algorithm. Chitosan magnetite aggregates were made and applied for enhancement of nitrification activity in activated sludge process. The aggregates enriched specifically the nitrifying bacteria such as Nitrosomonas europaea and the removal rate for nitrous compounds was enhanced dramatically. Denitrification activity was also enhanced by the immobilization of typical denitrification bacteria within alginate gels. Occurrence of red tide in a semi-closed aqua-ecosystem was predicted very accurately by applying artificial neural network. This model can prevent a severe damage for fishermen and also be applied for optimization of water treatment system in recycle use.
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