A Bayesian statistical traceability method for discharging industrial waste water exceeding the standard of sewage pipe network

Abstract

The invention discloses a Bayesian statistical traceability method for discharging industrial waste water exceeding the standard of sewage pipe network, It includes: 1. Random generation of the initial point (img file = 'DEST_PATH_IMAGE002. TIF' wi= '17' he= '19' / ) in the range of the prior information of the unknown parameters; 2, simulate that time series of pollutant concentration of the current parameter (img file = 'DEST_PATH_IMAGE004. TIF' wi= '16' he= '18' / ) correspond to the monitoring point, The posterior probability density of unknown parameters (img file = 'DEST_PATH_IMAGE006. TIF'wi= '45' he= '20' / ) was obtained by comparing with the actual monitoring data. 3, generate candidate parameters accord to that suggested distribution (img file= 'DEST_PATH_IMAGE008. TIF' wi= '17' he='15' / ), (img file= '928204DEST_PATH_IMAGE008. TIF' wi= '17' he= '15' / ), The posterior probability density of unknown parameters (img file = 'DEST_PATH_IMAGE010. TIF' wi= '47' he= '19' / ) is obtained bycomparing the likelihood degree with the actual monitoring data, 4, extract a random number (img file = 'DEST_PATH_IMAGE012. TIF' wi= '11' he= '13' / ), jud whether that candidate value is accepted ornot, outputting an accepted value and a posterior probability density; 5, repeat steps 3 and 4 until that iteration is complete. The invention has the advantages of effectively narrowing the value range of unknown parameters, utilizing the characteristics of the MCMC sampling method, reducing the workload and the sampling time under the condition of ensuring the rationality of the sampling, and improving the traceability efficiency.

Description

technical field [0001] The invention belongs to municipal engineering information technology, and in particular relates to a method for tracing the source of industrial wastewater discharged beyond the standard in a sewage pipe network integrated with a Bayesian statistical reasoning algorithm, a SWMM model and Matlab programming. Background technique [0002] In the sewage pipe network, industrial wastewater containing high concentrations of heavy metals and other toxic substances is secretly discharged, which usually has a serious impact on the activated sludge process of sewage treatment plants, and even leads to the death of activated sludge poisoning, and the effluent quality is not up to standard. However, the traceability problem is highly uncertain, and conventional mathematical model traceability methods are not suitable. Usually, the result of traceability and inversion is a unique value. Once the inversion result deviates from the actual value, the traceability res...

AI Technical Summary

Problems solved by technology

[0005] 2. The patent sampling calculation workload is heavy and takes a long time

Assuming that the total number of nodes that need to be inferred in the pipeline network is NX, the traceability calculation of 3 unknown pollution source information parameters is performed. The enumeration method in this patent needs to solve NX forward. 3 The convective transport equation of secondary pollutants requires a large amount of calculation, a long time for reverse deduction, and slow traceability feedback

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