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Sewage treatment effluent parameter prediction method based on grey neural network composition model

A technology of gray neural network and combined model is applied in the field of effluent parameter prediction of sewage treatment based on gray neural network combined model, which can solve the problems of low prediction accuracy, and achieve the goal of improving prediction accuracy, good practical application value and good scalability. Effect

Inactive Publication Date: 2017-07-28
ZHEJIANG GONGSHANG UNIVERSITY
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Problems solved by technology

[0004] In order to overcome the problem of low prediction accuracy of the existing singleness prediction method, the present invention provides a method for prediction of sewage treatment effluent parameters based on gray neural network combination model with high prediction accuracy

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  • Sewage treatment effluent parameter prediction method based on grey neural network composition model
  • Sewage treatment effluent parameter prediction method based on grey neural network composition model
  • Sewage treatment effluent parameter prediction method based on grey neural network composition model

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Embodiment Construction

[0048] Such as figure 1 As shown, the gray neural network combination model-based prediction method for sewage treatment effluent parameters provided in this embodiment includes:

[0049] Step S1. Obtain the historical water inflow parameter sequence x(t) and perform cumulative processing to form the cumulative sequence y(t). The influent parameters include parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD) and pH value.

[0050] Step S2, establishing a gray neural network according to the cumulative sequence y(t) and using the gray neural network to predict historical water discharge parameters; during the prediction process, an improved particle swarm optimization algorithm is used to optimize the gray neural network.

[0051] In this step, the gray system and the neural network are first fused, and the large sample size, nonlinear processing ability and learning ability of the neural network model are used to make up for the gray model's poor ...

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Abstract

The invention provides a sewage treatment effluent parameter prediction method based on a grey neural network composition model, the method comprises the steps: a historical influent parameter sequence x (t) is acquired and is accumulated to form an accumulation sequence y(t); according to the accumulation sequence y(t), a grey neural network is established and is utilized to predict historical effluent parameters, and in the prediction process, an improved particle swarm optimization is adopted to optimize the grey neural network; according to the optimized neural network, present effluent parameter prediction is performed; a Markov chain is adopted to correct obtained present effluent parameters.

Description

technical field [0001] The invention relates to the field of sewage treatment, and in particular to a method for predicting effluent parameters of sewage treatment based on a gray neural network combination model. Background technique [0002] Sewage treatment is a key link in controlling water pollution. Due to the high price of relevant sewage monitoring equipment, some small and medium-sized sewage treatment plants have unsatisfactory monitoring of relevant parameters due to problems such as cost issues or difficult maintenance of effluent monitoring equipment. However, the inaccuracy of some parameters not only has an important impact on the quality of effluent water, but also increases the cost of sewage treatment. [0003] At present, there are many methods for predicting the water quality of sewage treatment plants, but most of them are based on neural network prediction. The change of water quality in sewage treatment plants is composed of many influencing factors....

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/02
CPCG06N3/02G06Q10/04
Inventor 汪磊鲍福光王学成陈冠宇琚春华
Owner ZHEJIANG GONGSHANG UNIVERSITY
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