Wastewater treatment intelligent monitoring method and system based on GA-DBN network
A wastewater treatment system and wastewater treatment technology, applied in the general control system, control/regulation system, adaptive control, etc., can solve the problems that affect the algorithm implementation and optimization effect, and it is difficult to ensure the optimal network
Active Publication Date: 2020-02-21
SOUTH CHINA NORMAL UNIVERSITY +1
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The deep belief network DBN model is a typical deep learning algorithm, which can more accurately perceive the internal laws of object evolution, but it is difficult to guarantee the optimal network, and there are parameter selection and optimization in the process of wastewater treatment prediction, diagnosis and optimization control. Many problems, such as algorithm implementation and optimization effect
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[0089] Intelligent monitoring method of wastewater treatment based on GA-DBN network, such as figure 1 As shown, this method is based on the relationship between the effluent quality of the wastewater treatment system and the influent indicators and model control parameters, making full use of the ability of the genetic algorithm GA to search for the optimal solution macroscopically and the ability of the deep belief network DBN to accurately perceive the internal laws of object evolution. Create a water quality soft-sensing model based on GA-DBN, predict the COD and SS concentration of the wastewater treatment effluent, intelligently control the wastewater treatment system, adjust the effluent water quality in real time, and promote the efficient and stable operation of the wastewater treatment system. In addition, because the GA-DBN fusion model not only has a strong feature learning ability, but also can use adaptive learning rules to design the entire network and complete t...
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The invention discloses a wastewater treatment intelligent monitoring method and a wastewater treatment intelligent monitoring system based on a GA-DBN network. The wastewater treatment intelligent monitoring method comprises the steps of: selecting proper parameters of a wastewater treatment system as input independent variables and output variables; acquiring an optimal hidden layer node numberof each layer of network of a deep belief network DBN by adopting iterative optimization calculation; screening out an optimal input independent variable by utilizing a genetic algorithm GA; constructing a GA-DBN fusion model based on the genetic algorithm-deep belief network; and training the model, carrying out real-time soft measurement on the output variables of the wastewater treatment systemby means of the trained model to obtain a diagnosis result, and guiding optimization of the wastewater treatment process. The genetic-deep belief network GA-DBN fusion model and the wastewater treatment intelligent monitoring system constructed by the invention can be used for predicting water quality parameters such as effluent COD and SS concentration of the wastewater treatment system, realizing intelligent monitoring and diagnosis of water quality of the wastewater treatment system, and promoting efficient and stable operation of the wastewater treatment system.
Description
technical field [0001] The invention relates to the field of wastewater treatment and control research, in particular to a GA-DBN network-based intelligent monitoring method and system for wastewater treatment. Background technique [0002] At present, wastewater treatment generally goes through primary physical and chemical and secondary biochemical processes. The primary physical and chemical treatment is mainly used to remove SS and a small part of COD and BOD; the secondary biochemical treatment is used to remove most of COD and BOD. In order to monitor the stability of the wastewater treatment system and improve the compliance rate of wastewater discharge, real-time monitoring of indicators such as effluent COD and SS is required. The wastewater treatment process is complex, and the mechanism is not yet fully understood. It is difficult to effectively predict and regulate the effluent quality with accurate mathematical models. [0003] In order to solve the problem tha...
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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 黄明智牛国强易晓辉李小勇应光国石青松
Owner SOUTH CHINA NORMAL UNIVERSITY