Prediction of effluent total phosphorus based on modified ensemble empirical mode decomposition and deep belief network

doi:10.11949/0438-1157.20201365

Abstract

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.

Key words: urban sewage treatment process, effluent total phosphorus, ensemble empirical mode decomposition, deep belief network

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

CLC Number:

TP 183

WANG Longyang, MENG Xi, QIAO Junfei. Prediction of effluent total phosphorus based on modified ensemble empirical mode decomposition and deep belief network[J]. CIESC Journal, 2021, 72(5): 2745-2753.

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.

Figures/Tables 8

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序号	预测算法	RMSE	APE	Time/s
1	BP	1.3000~6.6000		15.8
2	ALRDBN	1.1671		17.6
3	CDBN	1.1507		11.5
4	DBN	1.2830	0.0049	4.24
5	MEEMD-DBN	1.0051	0.0026	5.01
6	MEEMD-DBN-SA	0.3725	0.0008	10.56

序号	预测算法	RMSE	APE	Time/s
1	BP	1.3000~6.6000		15.8
2	ALRDBN	1.1671		17.6
3	CDBN	1.1507		11.5
4	DBN	1.2830	0.0049	4.24
5	MEEMD-DBN	1.0051	0.0026	5.01
6	MEEMD-DBN-SA	0.3725	0.0008	10.56

序号	预测算法	RMSE	APE	Time/s
1	ILM-SVDFNN	0.1061	0.1161	22.15
2	LM-SVDFNN	0.1078	0.1167	40.05
3	ILM-FNN	0.1296	0.1412	34.38
4	EBP-SVDFNN	0.1766	0.1690	85.76
5	EBP-FNN	0.1944	0.1840	114.37
6	DFNN	0.1763	0.1644	66.87
7	GDFNN	0.1539	0.1628	46.83
8	MPCA-NN	0.2668	—	68.93
9	RBF	0.0697	0.0969	12.19
10	GAP-RBF	0.1482	0.1843	100.00
11	IELM	0.0783	0.1105	91.17
12	APSO-RBF	0.0711	0.1168	222.63
13	DBN	0.0851	0.2428	0.70
14	MEEMD-DBN	0.0362	0.1564	36.29
15	MEEMD-DBN-SA	0.0269	0.1151	68.11

序号	预测算法	RMSE	APE	Time/s
1	ILM-SVDFNN	0.1061	0.1161	22.15
2	LM-SVDFNN	0.1078	0.1167	40.05
3	ILM-FNN	0.1296	0.1412	34.38
4	EBP-SVDFNN	0.1766	0.1690	85.76
5	EBP-FNN	0.1944	0.1840	114.37
6	DFNN	0.1763	0.1644	66.87
7	GDFNN	0.1539	0.1628	46.83
8	MPCA-NN	0.2668	—	68.93
9	RBF	0.0697	0.0969	12.19
10	GAP-RBF	0.1482	0.1843	100.00
11	IELM	0.0783	0.1105	91.17
12	APSO-RBF	0.0711	0.1168	222.63
13	DBN	0.0851	0.2428	0.70
14	MEEMD-DBN	0.0362	0.1564	36.29
15	MEEMD-DBN-SA	0.0269	0.1151	68.11

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