| 采用支持向量机算法优化电化学处理油田污水的工艺参数 |
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| 引用本文： | 尹先清,陈文娟,靖波,刘倩,杨航.采用支持向量机算法优化电化学处理油田污水的工艺参数[J].化工环保,2017,37(4):377-382. |
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| 作者姓名： | 尹先清 陈文娟 靖波 刘倩 杨航 |
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| 作者单位： | 1. 海洋石油高效开发国家重点实验室,北京 100027；; 2. 中海油研究总院,北京 100027；; 3. 长江大学 化学与环境工程学院石油石化污染控制与处理国家重点实验室长江大学研究室,湖北 荆州 434023 |
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| 基金项目： | 海洋石油高效开发国家重点实验室开放基金项目（CCL2015RCPS0221RNN）; “十三·五”国家科技重大专项项目（2016ZX05025-003） |
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| 摘 要： | 采用支持向量机(SVM)算法,将Box-Behnken设计法与支持向量回归算法(SVR)实验参数优化软件相结合,优化电化学去除油田污水COD的工艺参数。通过量子粒子群算法对SVM算法参数进行优化,从建立的回归模型中找到工艺参数的全局最佳点:电解时间60 min,电解电流3 A,三维电极填充料中石英砂质量695 g。模型得到的COD理论最优去除率为92.48%,验证实验得到的COD去除率为91.43%。
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| 关 键 词： | 油田污水 支持向量机（SVM）算法 量子粒子群算法 电化学处理 过程控制参数 |
| 收稿时间： | 2016-11-25 |
Optimization of process parameters for electrochemical treatment of oilfield wastewater by support vector machine algorithm |
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| Yin Xianqing,Chen Wenjuan,Jing Bo,Liu Qian,Yang Hang.Optimization of process parameters for electrochemical treatment of oilfield wastewater by support vector machine algorithm[J].Environmental Protection of Chemical Industry,2017,37(4):377-382. |
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| Authors: | Yin Xianqing Chen Wenjuan Jing Bo Liu Qian Yang Hang |
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| Institution: | 1. State Key Laboratory of Offshore Oil Exploitation,Beijing 100027,China;2. CNOOC Research Institute,Beijing 100027,China;3. State Key Laboratory of Petroleum Pollution Control in Yangtze University,College of Chemical and Environmental Engineering,Yangtze University,Jingzhou Hubei 434023,China |
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| Abstract: | Combining the Box-Behnken design method with support vector regression algorithm （SVR,a software for experimental parameter optimization）,the process parameters for electrochemical removal of oilfield wastewater COD were optimized using the support vector machine （SVM） algorithm. The SVM algorithm parameters were optimized by quantum particle swarm algorithm,the global optimal point of the parameters were found out from the regression model,such as：electrolysis time 60 min,electrolytic current 3 A,mass of quartz sand in the three dimensional electrode filler 695 g. The optimal COD removal rate from the model was 92.48%,while that from the experiment was 91.43%. |
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| Keywords: | oilfield wastewater support vector machine （SVM） algorithm quantum behaved particle swarm optimization electrochemistry treatment process control parameter |
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