A neural network hybrid optimization method for cement denitrification
A neural network and optimization method technology, applied in the field of automation industry, can solve the problems of emissions that do not meet national standards, and it is difficult for cement enterprises to accurately control the amount of ammonia injection, and achieve the effect of improving prediction accuracy.
Active Publication Date: 2022-03-01
HANGZHOU DIANZI UNIV +1
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[0003] The present invention is aimed at the fact that it is difficult for cement enterprises to precisely control the amount of ammonia sprayed, resulting in NO x Disadvantages such as emissions not meeting national standards, a neural network hybrid optimization method for cement denitrification was proposed
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[0052] Take the cement denitrification reactor system as an example:
[0053] There is an ammonia injection grid at the entrance of the reactor. The ammonia gas from the liquid ammonia evaporation system passes through an ammonia supply adjustment door, mixes with the diluted air from the dilution fan, and is sprayed out through the nozzle. x Under the action of the catalyst, a selective catalytic reduction reaction occurs to generate water and ammonia. In the process of this system, the amount of ammonia injection is a key control indicator. By establishing a prediction model for the cement denitrification reactor, the unit load, ammonia injection amount, SCR inlet smoke temperature, inlet NO x Concentration, inlet flue gas oxygen content, outlet flue gas oxygen content, and denitrification efficiency are 7 pairs of variables as the input of the model, and SCR outlet NO x Concentration is used as the output of the model.
[0054] Step 1. Collection of cement denitrification...
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The invention relates to a neural network mixing optimization method for cement denitrification. The present invention first collects multiple variables that affect the outlet concentration in the cement denitrification process, uses principal component analysis to preprocess the data, reduces the data dimension, and then uses the genetic algorithm and the LM algorithm to optimize the center, width and weight of the neural network to obtain Neural network prediction model, the final preprocessed data is input into the model to improve the accuracy of model prediction. Different from the traditional neural network prediction method, the present invention combines data processing and neural network parameter optimization to improve prediction accuracy.
Description
technical field [0001] The invention belongs to the technical field of automation industry and relates to a neural network mixing optimization method for cement denitrification. Background technique [0002] NO x The emission is an unavoidable process in cement production, as the environmental protection department x Increasingly stringent emission limits, low NO x Combustion technology and denitrification technology are widely used in cement enterprises, but at this stage, most cement enterprises are difficult to achieve precise control of the amount of ammonia injection, or even automatic control, resulting in NO x The emissions cannot meet the environmental protection standards required by the state, so this invention proposes a neural network mixing optimization method for cement denitrification, which controls a reasonable amount of ammonia injection and ensures that NO x Emissions meet national standards to avoid secondary pollution. Contents of the invention [...
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IPC IPC(8): B01D53/86G06K9/62G06N3/04G06N3/08G06N3/12B01D53/56
CPCB01D53/8625G06N3/086G06N3/126B01D2251/2062G06N3/045G06F18/2135G06F18/23213
Inventor 于征张日东袁亦斌吴胜王璟琳
Owner HANGZHOU DIANZI UNIV