A real-time optimization decision-making method for automatic combustion in waste incineration process

Abstract

The invention relates to the technical field of urban solid waste incinerator optimal control, in particular to an automatic combustion optimized decision method for the waste incineration process. Bymeans of the optimized decision of key manipulated variables (feeder speed and fire grate speed), an automatic combustion system operates within an ideal range, and the method mainly comprises the following steps that (1) a decision case library is set up according to historical data of the incineration process; (2) a training pattern pool is constructed; (3) a stochastic allocation network is trained according to a study type pseudometric measuring criterion, and therefore a stochastic allocation network retrieval model is obtained; (4) a target case is input into the stochastic allocation network retrieval model, and K similar case solutions are obtained; (5) the average value of the K similar case solutions is obtained through case reusing, and therefore the decision value of a targetcase solution (feeder speed and fire grate speed) is obtained and output to an incineration process control system; and (6) the fourth step and the fifth step are repeated to achieve the automatic combustion real-time optimized decision process of the incineration process.

Description

technical field [0001] The invention relates to the technical field of optimization decision-making for municipal solid waste incinerators, more specifically, the invention is a real-time optimization decision-making method for automatic combustion in the garbage incineration process. Background technique [0002] In recent years, with the continuous acceleration of my country's urbanization process, the output of municipal solid waste has increased sharply, but the harmless treatment capacity is still insufficient. The National Development and Reform Commission has affirmed and promoted the role and status of municipal solid waste incineration in its opinions on municipal solid waste incineration. Municipal solid waste incineration treatment technology has been widely used in developed countries and regions. my country's current waste incineration treatment technology and equipment are becoming more and more mature, and it has become an important way of urban domestic waste...

AI Technical Summary

Problems solved by technology

When using neural network modeling, there is a lack of effective methods to determine the number of hidden layers and nodes; the support vector machine modeling method is slow in training for large data sets, so the application of these methods is not effective

The modeling method based on neural network requires a large number of representative samples, the number of hidden layers and the number of neurons depend on experience, and the convergence speed is slow, and it is easy to fall into the local minimum; the support vector machine modeling method is suitable for small The sample data has a good effect, but for large sample data, the training time is long, the ability of self-learning is lacking, and the selection of parameters is uncertain; the selection of feature variables by multiple linear regression is a difficult problem

Therefore, the application of these optimization decision-making methods is ineffective

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