Biochemical oxygen demand BOD soft measurement method based on elastic radical basis function neural network

Abstract

The invention discloses a biochemical oxygen demand BOD soft measurement method based on elastic radical basis function neural network, belonging to the technical field of detection. The sewage processing process has severe production condition and serious random disturbance, has the characteristics of strong nonlinearity, large time varying and serious lag and is hard to build a precise mathematical model by mechanism analysis. The invention utilizes the liveness function of an RBF neuron to judge the activeness of the neuron, and divides the neuron with strong activeness; then, joint strength between the hidden layer neuron and the output layer neuron of an RBF neuron network is analyzed by calculating a mutual information dependency function so as to revise the neural network structureaccording to the mutual information intensity; and finally, the parameter of the neural network is adjusted until the network structure satisfies the requirement on processing information. The invention improves the quality and the efficiency of sewage processing, lowers sewage processing cost and provides in-time water quality and relevant parameter monitoring for realizing closed loop control for the sewage processing process so as to accelerate sewage treatment plants to efficiently and stably operate.

Description

technical field [0001] The soft measurement method is one of the main development trends of detection technology and instrumentation research, and is an important branch of the advanced manufacturing technology field. The present invention relates to a soft measurement method for the key water quality indicator biochemical oxygen demand (BOD) in the sewage treatment process; the soft measurement method is based on A certain optimal criterion selects a group of auxiliary variables that are closely related to the main variable and easy to measure. By constructing a certain mathematical model, the estimation of the main variable is realized by relying on prior learning and memory; the accuracy of the soft measurement depends on the actual measurement. The effect of data learning, memory and association, and the ability to continuously re-learn; the application of soft sensing methods to sewage treatment systems can not only save investment and operating costs, but also monitor the...

AI Technical Summary

Problems solved by technology

The main problems in the process of urban sewage treatment are: ① Excessive power consumption and high operating costs; ② Abnormal working conditions frequently occur, and the effluent quality exceeds the standard

Among them, the water quality parameter BOD refers to the amount of oxygen required to decompose a unit of organic matter within a specified time. At present, most sewage treatment plants use the dilution inoculation method and the rapid measurement method of microbial sensors to measure the biochemical oxygen demand BOD in different types of water. The BOD analysis and measurement cycle Generally, it is 5 days, which cannot reflect the actual situation of sewage treatment in time, and cannot realize real-time measurement of BOD, which directly makes it difficult to realize closed-loop control in the sewage treatment process

In addition, the number and content of pollutants in sewage are large and varied, which is a big challenge for detection

Although the development of process measuring instruments in the form of new hardware can directly solve the detection problems of various sewage treatment process variables and water quality parameters, due to the complexity of organic matter in sewage, the development of these sensors will be a costly and long-term project.

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