| 基于改进集合经验模态分解和深度信念网络的出水总磷预测 |
| |
| 引用本文： | 王龙洋,蒙西,乔俊飞.基于改进集合经验模态分解和深度信念网络的出水总磷预测[J].化工学报,2021,72(5):2745-2753. |
| |
| 作者姓名： | 王龙洋 蒙西 乔俊飞 |
| |
| 作者单位： | 1.北京工业大学信息学部，北京 100124;2.计算智能与智能系统北京市重点实验室，北京 100124 |
| |
| 基金项目： | 国家自然科学基金项目(61533002);国家重点研发计划项目(2018YFC1900800-5) |
| |
| 摘 要： | 出水总磷的准确预测对于城市污水处理厂的高效、稳定的运行至关重要。文中针对城市污水处理过程中出水总磷难以预测的问题，提出一种基于改进集合经验模态分解（modified ensemble empirical mode decomposition， MEEMD）和深度信念网络（deep belief network， DBN）的出水总磷预测方法。首先，设计一种MEEMD算法对城市污水处理过程出水总磷数据信号进行分解，获取多个本征模态函数（intrinsic mode function， IMF）组合；然后，建立一种基于模拟退火（simulated annealing， SA）算法的深度信念网络预测模型，通过优化的模型结构对分解后得到的每个IMF分量进行有效预测；最后，通过大气CO2浓度预测和城市污水处理出水总磷预测验证了所提出方法的有效性。
|
| 关 键 词： | 城市污水处理过程 出水总磷 集合经验模态分解 深度信念网络 |
| 收稿时间： | 2020-09-27 |
Prediction of effluent total phosphorus based on modified ensemble empirical mode decomposition and deep belief network |
| |
| WANG Longyang,MENG Xi,QIAO Junfei.Prediction of effluent total phosphorus based on modified ensemble empirical mode decomposition and deep belief network[J].Journal of Chemical Industry and Engineering(China),2021,72(5):2745-2753. |
| |
| Authors: | WANG Longyang MENG Xi QIAO Junfei |
| |
| Affiliation: | 1.Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;2.Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China |
| |
| Abstract: | Accurate prediction of effluent total phosphorus is essential for the stable and efficient operation of urban wastewater treatment plants. Aiming at the problem that effluent total phosphorus is difficult to predict in urban wastewater treatment process, a prediction method of effluent total phosphorus based on modified ensemble empirical mode decomposition(MEEMD) and deep belief network (DBN) is proposed in this paper. First, a MEEMD algorithm is designed to decompose the effluent total phosphorus data signal of the effluent from urban wastewater treatment process. Then, establish a deep belief network prediction model based on simulated annealing (SA) algorithm, and effectively predict each IMF component obtained after decomposition through the optimized model structure. Finally, the effectiveness of the proposed method is verified by the prediction of atmospheric CO2 concentration and the effluent total phosphorus in urban wastewater treatment. |
| |
| Keywords: | urban sewage treatment process effluent total phosphorus ensemble empirical mode decomposition deep belief network |
|
| 点击此处可从《化工学报》浏览原始摘要信息 |
|
点击此处可从《化工学报》下载全文 |
