| 基于混合智能算法的多目标优化在厌氧氨氧化与反硝化协同脱氮除碳中的应用 |
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| 引用本文： | 谢彬,马邕文,万金泉,王艳,渠艳飞.基于混合智能算法的多目标优化在厌氧氨氧化与反硝化协同脱氮除碳中的应用[J].环境科学学报,2018,38(4):1467-1473. |
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| 作者姓名： | 谢彬 马邕文 万金泉 王艳 渠艳飞 |
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| 作者单位： | 华南理工大学环境与能源学院;华南理工大学教育部工业聚集区域污染控制与修复重点实验室;华南理工大学制浆造纸国家重点实验室; |
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| 基金项目： | 国家自然科学基金（No.31570568，31670585）；制浆造纸工程国家重点实验室项目（No.201535）；广东市科技计划项目（No.201607010079，201607020007）；广东省科技计划项目（No.2016A020221005） |
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| 摘 要： | 针对厌氧氨氧化与反硝化协同实现脱氮除碳优化问题,采用UASB反应器处理不同进水条件下的氨氮废水,基于BP神经网络分别建立NH_4~+-N去除模型和COD去除模型,同时为了提高模型的鲁棒性和运算速度,使用PCA算法降低输入变量维数.仿真结果表明,基于PCA-BP的预测模型具有较好的预测能力,检验样本中模型预测值与实际真实值的相关系数分别为0.9164和0.9987,且两模型的平均预测误差都保持在在10%以内.进一步结合NSGA-II算法建立以去除NH+4-N和COD最大化的优化模型,以优化结果为条件建立的出水效果接近实际真实值,表明该模型给出的优化解决方案有效可行,可为实现厌氧氨氧化与反硝化协同脱氮除碳工艺的设计和操作提供参考和指导.
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| 关 键 词： | 厌氧氨氧化 脱氮除碳 神经网络 多目标优化 |
| 收稿时间： | 2017/8/25 0:00:00 |
| 修稿时间： | 2017/10/17 0:00:00 |
Application research of multi-objective optimization in carbon and nitrogen removal by anaerobic ammonia oxidation and denitrification based on multi-intelligence-algorithm |
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| XIE Bin,MA Yongwen,WAN Jinquan,WANG Yan and QU Yanfei.Application research of multi-objective optimization in carbon and nitrogen removal by anaerobic ammonia oxidation and denitrification based on multi-intelligence-algorithm[J].Acta Scientiae Circumstantiae,2018,38(4):1467-1473. |
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| Authors: | XIE Bin MA Yongwen WAN Jinquan WANG Yan and QU Yanfei |
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| Institution: | College of Environment and Energy, South China University of Technology, Guangzhou 510006,1. College of Environment and Energy, South China University of Technology, Guangzhou 510006;2. The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006;3. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510006,1. College of Environment and Energy, South China University of Technology, Guangzhou 510006;2. The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006;3. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510006,1. College of Environment and Energy, South China University of Technology, Guangzhou 510006;2. The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006;3. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510006 and College of Environment and Energy, South China University of Technology, Guangzhou 510006 |
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| Abstract: | |
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| Keywords: | ANAMMOX carbon and nitrogen removal neural network multi-objective optimization |
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