A method for predicting effluent water quality based on an improved online sequence extreme learning machine

Abstract

The invention discloses a method for predicting effluent water quality based on an improved online sequence extreme learning machine. On the basis of the machine learning method extreme learning machine, the method considers the influence of dissatisfied rank matrix inversion and random parameters on the neural network, The ridge regression and ensemble method are introduced and an improved online sequence extreme learning machine method is proposed. This method is applied to the water quality prediction of sewage treatment. It has the characteristics of fast training speed, real-time online prediction and good prediction effect, and effectively solves the problems caused by The delay problem of sewage treatment control caused by insufficient sensor hardware has important practical significance and economic value for the improvement of sewage treatment process.

Description

technical field [0001] The invention relates to the fields of control science and engineering, environmental science and engineering, in particular to a method for predicting effluent quality based on an improved online sequence extreme learning machine. Background technique [0002] As an indispensable strategic resource for the development of human life, water is one of the elements necessary for the existence of life. In the world, water resources are generally abundant, but the water resources available to humans are very scarce, and the distribution of water resources is uneven. It also makes the problem of water shortage in some areas highlighted. In addition to natural factors, problems such as water pollution and irrational use caused by human factors have further exacerbated the shortage of water resources. How to effectively and rationally utilize the existing water resources, so that in the process of rapid social development, there are not only mountains of gold...

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Problems solved by technology

[0004] However, as a complex industrial system, the sewage treatment system has problems such as nonlinearity, strong coupling, large hysteresis, and multiple disturbances, and it is difficult to build an accurate mathematical model based on the mechanism

In the actual industrial site, due to the large hysteresis of the system, the control of sewage treatment based on the results of sensor detection often cannot guarantee the quality of effluent water. At the same time, some sensor detection values ​​cannot be obtained in real time, and experimental methods need to be used to obtain offline , there is a large time lag in the detection results of the implementation method, which cannot provide effective reference values ​​for real-time control

Therefore, the effect of control based on detection and sensing results is often difficult to be effectively improved.

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