Improvement of waste water treatment using functional magnetites and knowledge engineering techniques

使用功能性磁铁矿和知识工程技术改进废水处理

• 批准号: 12450331
• 负责人: KOBAYASHI Takeshi
• 金额: $ 9.6万
• 依托单位: Nagoya University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-12450331/
• 关键词: 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

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.

活性污泥法常用于生活废水处理，但活性污泥法的改进仍有许多问题有待解决。这项研究将知识工程技术与生物技术技术相结合。首先，应用递归模糊神经网络构建了活性污泥法出水COD的预测模型，并应用遗传算法对运行工况进行了准确估计。制备了壳聚糖磁铁矿聚集体，并将其用于增强活性污泥工艺中的硝化活性。这些聚集体专门富集了硝化细菌，如欧洲亚硝化细菌，并且亚硝化合物的去除率显着提高。将典型反硝化细菌固定在藻酸盐凝胶内也增强了反硝化活性。通过应用人工神经网络，可以非常准确地预测半封闭水生生态系统中赤潮的发生。该模型可以防止渔民遭受严重损害，也可用于优化循环利用的水处理系统。