| 引用本文: | 邱微,吴克祥,江进,赫俊国,袁一星.基于GA-ANN模型的A2/O工艺运行参数优化[J].哈尔滨工业大学学报,2017,49(9):117.DOI:10.11918/j.issn.0367-6234.201607057 |
| QIU Wei,WU Kexiang,JIANG Jin,HE Junguo,YUAN Yixing.Optimization of the A2/O technological parameters based on GA-ANN model[J].Journal of Harbin Institute of Technology,2017,49(9):117.DOI:10.11918/j.issn.0367-6234.201607057 |
|
| 本文已被：浏览 3882次 下载 868次 |
码上扫一扫！ |
|
|
| 基于GA-ANN模型的A2/O工艺运行参数优化 |
|
邱微1,2,吴克祥1,2,江进1,2,赫俊国1,2,袁一星1,2
|
|
(1.哈尔滨工业大学 市政环境工程学院,哈尔滨150090;2.城市水资源与水环境国家重点实验室(哈尔滨工业大学),哈尔滨150090)
|
|
| 摘要: |
| 影响A2/O工艺运行的参数有许多，这些因素相互联系、相互作用，影响工艺效率.为了弥补控制单一变量法或者设计正交试验法的不足，综合考察多种运行参数对工艺运行效果的影响，建立了基于遗传算法进行全局寻优的神经网络模型(GA-ANN模型)，并应用于某城市污水处理厂A2/O工艺的运行优化.获得该厂调试运行期间154组有效监测数据后，随机选取2/3的数据用于GA-ANN模型的求解，1/3的数据用于模型的检验，对工艺运行参数进行优化，得到最佳运行参数组合.结果显示, 建立基于遗传算法的神经网络模型用于A2/O工艺运行参数的优化是可行的，可以为污水处理厂运行参数的设置提供理论参考，对调试工作、提高工艺运行效率具有一定的实际指导意义.
|
| 关键词: 城市污水 A2/O 运行参数 GA-ANN模型 |
| DOI：10.11918/j.issn.0367-6234.201607057 |
| 分类号:TU992.3 |
| 文献标识码:A |
| 基金项目:城市水资源与水环境国家重点实验室(哈尔滨工业大学)自主课题(2016TS02); 黑龙江省自然科学基金委面上项目(E201427); 国家留学基金资助 |
|
| Optimization of the A2/O technological parameters based on GA-ANN model |
|
QIU Wei1,2,WU Kexiang1,2,JIANG Jin1,2,HE Junguo1,2,YUAN Yixing1,2
|
|
(1.School of Municipal & Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China; 2.State Key Laboratory of Urban Water Resources and Environment (Harbin Institute of Technology), Harbin 150090, China)
|
| Abstract: |
| A2/O process is one of the major processes in municipal waste water treatment, but many parameters affect the operation effect of A2/O process. And these parameters interact with each other, affecting the efficiency of the process. In order to make up the insufficience of single variable control method or orthogonal designing method, it establishes the neural network model (GA-ANN model) based on genetic algorithm. The model has been applied to an urban waste water treatment plant by A2/O process optimization. During the commissioning operation of the plant, it has obtained 154 effective monitoring data, and 2/3 of the data has been randomly selected for the GA-ANN model, and 1/3 of the data has been used for the model test. The process parameters have been optimized and get the best combination of operating parameters. The results show that it is feasible to establish the neural network model based on the genetic algorithm for the optimization of the A2/O process operation parameters. It can provide the theoretical reference for setting operation parameter of the waste water treatment plant. And it is also helpful to the practical production and application for adjustment and improvement of the operation efficiency.
|
| Key words: municipal sewage A2/O operating parameter GA-ANN model |
