| 神经网络预测催化超临界水氧化废水效果的研究 |
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| 引用本文： | 刘威,柴涛.神经网络预测催化超临界水氧化废水效果的研究[J].工业安全与环保,2010,36(1). |
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| 作者姓名： | 刘威 柴涛 |
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| 作者单位： | 中北大学化工与环境学院,太原,030051 |
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| 摘 要： | 在间歇式反应器中进行催化超临界水氧化DDNP废水试验,考察催化剂浓度、反应温度、压力、停留时间对氧化效果的影响。在实验基础上采用BP神经网络算法,建立以催化剂浓度、反应温度、压力、停留时间作为网络模型的输入层,COD去除率作为输出层的双隐层BP神经网络预测模型,预测催化超临界水氧化废水的效果,仿真结果表明模型预测效果较好。
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| 关 键 词： | BP神经网络 催化超临界水氧化 废水处理 |
Study on the Neural Network in Predicting the Degradation Efficiency of Wastewater by Catalytic Supercritcial Water Oxidation |
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| LIU Wei,CHAI Tao.Study on the Neural Network in Predicting the Degradation Efficiency of Wastewater by Catalytic Supercritcial Water Oxidation[J].Industrial Safety and Dust Control,2010,36(1). |
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| Authors: | LIU Wei CHAI Tao |
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| Institution: | College of Chemical Engineering and Environment;North University of China Taiyuan 030051 |
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| Abstract: | The catalytic supercritical water oxidation(SCWO) of DDNP wastewater is performed in an intermittent reactor,to investigate the oxidation decomposition efficiency of organic compounds.The decomposition efficiency is influenced by the concentration of catalyst,reaction temperature,pressure,residence time.Based on the experimental results,a BP Elman network prediction model with two hidden-layer is established using the concentration of catalyst,reaction pressure,temperature,residence time as input variables,... |
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| Keywords: | Elman neural network catalytic supercritical-water oxidation wastewater treatment |
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