| 基于支持向量机的污水处理软测量算法的研究 |
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| 引用本文： | 苏书惠,张绍德,谭敬辉.基于支持向量机的污水处理软测量算法的研究[J].自动化与仪器仪表,2009(6):6-9. |
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| 作者姓名： | 苏书惠 张绍德 谭敬辉 |
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| 作者单位： | 安徽工业大学电气信息学院,安徽马鞍山,243002 |
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| 摘 要： | 针对污水处理过程中生化需氧量BOD难以实时在线测量的问题，建立了用于预估BOD的支持向量机（SVM）的软测量模型。考虑到该支持向量机模型的测量精度取决于其两个参数C、σ能否获得最优值，采用遗传算法和粒子群优化算法，实现对这两个参数的寻优。仿真结果表明：该软测量模型的测量精度较高，可用于污水处理厂对BOD进行在线测量。
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| 关 键 词： | 软测量 支持向量机 污水处理 遗传算法 粒子群算法 |
Study on soft-sensing method based on support vector machine for sewage disposal |
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| SU Shu-hui,ZHANG Shao-de,TAN Jing-hui.Study on soft-sensing method based on support vector machine for sewage disposal[J].Automation & Instrumentation,2009(6):6-9. |
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| Authors: | SU Shu-hui ZHANG Shao-de TAN Jing-hui |
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| Abstract: | In view of the hardship to get real-time and on-line of Biochemical Oxygen Demand （BOD） in sewage disposal process. A soft-sensing model based on support vector machine is established for estimating the Biochemical Oxygen Demand. The measurement accuracy of the support vector machine model depend on a good setting of the two parametres C, σ. Genetic algorithm and Particle Swarm Optimization （PSO） algorithm am applied to optimize the two parameters synchronously.The simulation results indicate that the soft-sensing model can be used for sewage disposal plant on-line measurement of BOD. |
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| Keywords: | Soft-sensing Support vector machine Sewage disposal Genetic algotithm Particle swarm optimization |
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