Steam pipe network prediction system based on Bayesian neural network algorithm

Abstract

The invention discloses a steam pipe network prediction system based on a Bayesian neural network algorithm. The steam pipe network prediction system based on the Bayesian neural network algorithm mainly comprises a relational data base, a data collection module, a data display module and a Bayesian neural network prediction module. The data display module is arranged on an engineer station, the Bayesian neural network prediction module is arranged on an application server, the relational data base is arranged on a relational data base server, and the data collection module is arranged on a real-time data base. The relational data base is a data communication medium between the data display module and the Bayesian neural network prediction module, the Bayesian neural network prediction module writes results of the Bayesian neural network prediction module into the relational data base, and the data display module reads and displays the results from the relational data base. The steam pipe network prediction system based on the Bayesian neural network algorithm has the advantages of achieving rapid solution, ensuring precision of a calculation model and being capable of meeting requirements of a process technology, and the calculation results are in fit with actual operation conditions.

Description

technical field [0001] The invention relates to the field of health care massage equipment, in particular to a Bayesian neural network algorithm and a steam pipe network prediction system based on the algorithm. Background technique [0002] Steam is one of the important energy media for iron and steel enterprises. In a large integrated iron and steel enterprise, the steam system is a complex object with the characteristics of large time delay, large inertia, nonlinearity, multivariable coupling, variable parameters, etc. Low pressure), multi-working conditions changes (seasons, production plans), and the operation and management personnel lack accurate prediction and effective control of the information on the operating conditions of the pipeline network. In the face of such complex operating conditions, most of the management personnel still adopt the "responsive" scheduling management method, relying on the experience accumulated in production over the years to command t...

AI Technical Summary

Problems solved by technology

In a large integrated iron and steel enterprise, the steam system is a complex object with the characteristics of large time delay, large inertia, nonlinearity, multivariable coupling, variable parameters, etc. Low pressure), multi-working conditions changes (seasons, production plans), and the operation and management personnel lack accurate prediction and effective control of the information on the operating conditions of the pipeline network

In the face of such complex operating conditions, most of the management personnel still adopt a "responsive" dispatching management method, relying on the experience accumulated in production over the years to command the operation of the system, and frequent occurrences of emptying, degraded use, etc., resulting in great waste

This will inevitably lead to the lack of effective predictive control methods for pipe network operation.

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