Sewage chemical oxygen demand soft measuring method based on support vector machine and neural network

Abstract

The invention relates to a sewage chemical oxygen demand soft measuring method based on a support vector machine and a neural network, belonging to the technical field of sewage treatment. In the invention, the water quality parameters pH, dissolved oxygen, redox potential, a pH rate of change, a dissolved oxygen rate of change and a redox potential rate of change are selected for carrying out soft measurement on the water quality parameter chemical oxygen demand. By adopting the support vector machine, the input data are classified according to all water quality parameters and the parameter rates of change, and the proper neutral network is selected for respectively training to realize the real-time effective estimate of the water quality parameters. The test on the testing system shows that the method has good precision and general applicability.

Description

technical field [0001] The invention belongs to the field of sewage treatment, and in particular relates to a soft measurement method for chemical oxygen demand in sewage. Background technique [0002] Chemical Oxygen Demand (COD, Chemical Oxygen Demand) is the amount of oxidant consumed when a certain strong oxidant is used to treat water samples under the condition of strong acid and heating, expressed in terms of oxygen concentration (mg / L). A comprehensive indicator of total organic matter. The content of organic matter in water is one of the important indicators for grading the environmental quality of natural water bodies. It is the fundamental factor that causes the water body to turn black and smelly, and it is also the basis for judging whether the water body is polluted by domestic sewage and industrial wastewater. Many countries have stipulated the maximum value of COD, a water quality parameter that can be discharged into natural water bodies. [0003] At prese...

AI Technical Summary

Problems solved by technology

The second problem lies in the construction method of the neural network

Due to the diversity of sewage status and the limitation of the selection of auxiliary variables, this requirement is often not fully satisfied, often resulting in the failure to achieve the ideal accuracy when estimating the leading variable with the analog output of the soft sensor

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