Soft measuring method of air particulate matter 2.5 (PM2.5) based on self-organizing fuzzy neural network

Abstract

The invention discloses a soft measuring method of an air particulate matter 2.5 (PM2.5) based on a self-organizing fuzzy neural network, and belongs to both the field of environmental engineering and the field of detection techniques. An atmospheric environmental system has the characteristics of multivariable, nonlinearity, a complicated internal mechanism, incomplete information and the like; the mathematic model of the atmospheric environmental system is difficult to establish through mechanism analysis; however, a neural network has better processing capacity to a highly nonlinear and seriously uncertain system. According to the soft measuring method of the air particulate matter 2.5 (PM2.5) based on the self-organizing fuzzy neural network, aiming at the problem that the PM2.5 is difficult to predict, an auxiliary variable relative to the PM2.5 is selected based on partial least square, then a soft measuring model between a relative variable and the PM2.5 is established by utilizing the self-organizing fuzzy neural network based on a sensitivity analytical method; the PM2.5 is predicted, a better effect is obtained; the in-time and accurate quality information of an atmospheric environment can be provided for an environmental management decision maker and people; the pollution control of the atmospheric environment is beneficially enhanced; the occurrence of serious pollution is prevented.

Description

technical field [0001] The present invention relates to fine particulate matter PM in the air 2.5 soft-measurement method. Soft measurement is to select a group of auxiliary variables that are closely related to the main variable and easy to measure according to an optimal criterion, and estimate the main variable by constructing a certain mathematical model and relying on prior learning and memory. PM 2.5 The prediction of is of great significance in the prevention and control of air pollution, and the soft-sensing method is applied to PM 2.5 In the prediction of air pollution, it can not only save the cost of air pollution monitoring, but also provide timely PM 2.5 It is an important branch of the advanced manufacturing technology field, which belongs to both the field of environmental engineering and the field of detection technology. Background technique [0002] The Air Pollution Prevention and Control Action Plan issued by the State Council clearly stated that by 2...

AI Technical Summary

Problems solved by technology

The gravimetric method requires manual weighing, and the operation is cumbersome and time-consuming

The latter two are automatic monitoring methods, the required instruments and meters are expensive, difficult to maintain, and the measurement range is limited

Commonly used PM 2.5 Forecasting methods Deterministic chemical modeling methods require model resolution, meteorological initial conditions, temporal and spatial distribution of emission sources and other parameters are difficult to determine, complex calculations, and low precision

The linear regression modeling method is not suitable for modeling the inherently nonlinear atmospheric environment system, the artificial neural network has poor interpretability, and the general fuzzy neural network has a fixed structure

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