EMD-LSTM-based outlet SO2 concentration prediction method

A concentration prediction, SO2 technology, applied in the field of pollutant monitoring, can solve the problems of large delay, many affected factors, large inertia, etc., to prevent overfitting and improve accuracy.

Pending Publication Date: 2021-09-28
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0003] At present, most of the desulfurization systems in coal-fired power plants are limestone-gypsum wet desulfurization systems. This system has the advantages of high desulfurization efficiency and low cost. Delay, large inertia and other issues

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  • EMD-LSTM-based outlet SO2 concentration prediction method
  • EMD-LSTM-based outlet SO2 concentration prediction method
  • EMD-LSTM-based outlet SO2 concentration prediction method

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Embodiment

[0040] The EMD-LSTM based export SO 2 Concentration prediction method, comprising the steps of:

[0041] Step 1: Collect and export SO 2 Concentration-related variables, get initial variables;

[0042] Step 2: Screen the initial variables through the LASSO algorithm, and obtain the input variables after removing redundant variables;

[0043] Step 3: Compensate the time delay of the input variables through the mutual information algorithm;

[0044] Step 4: Decompose the selected input variables through the EMD algorithm to obtain a stable signal as the model input variable;

[0045] Step 5: Use the LSTM neural network to establish a prediction model, input the model input variables, and obtain the SO 2 Concentration prediction data.

[0046] In step 1 of this embodiment, the initial variables include the pH value of the desulfurization tower, the slurry flow rate of the limestone slurry, the inlet flue gas flow rate, the inlet SO 2 Concentration, unit load, density of slu...

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Abstract

The invention provides an EMD-LSTM-based outlet SO2 concentration prediction method, wherein the method comprises the following steps: step 1, acquiring variables related to outlet SO2 concentration to obtain initial variables; step 2, screening the initial variables through an LASSO algorithm, and after redundant variables are removed, obtaining input variables; step 3, performing time delay compensation on the input variables through a mutual information algorithm; step 4, decomposing the selected input variables through an EMD algorithm to obtain stationary signals as model input variables; and step 5, establishing a prediction model by adopting an LSTM neural network, and inputting the model input variables to obtain prediction data of the SO2 concentration. According to the prediction method, the precision of the model can be effectively improved, the model is prevented from being over-fitted, and the prediction accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of pollutant monitoring, in particular to an EMD-LSTM-based outlet SO 2 Concentration Prediction Methods. Background technique [0002] China is a country where coal is the main energy source, SO produced by coal burning 2 It will cause great pollution to the environment. Coal-fired power plants are SO 2 Large emitters, in recent years, due to my country's SO 2 The emission requirements are becoming increasingly stringent, the control of SO 2 The damage to the environment caused by the reduction of export concentration is the main problem faced by all power companies at present. [0003] At present, most of the desulfurization systems in coal-fired power plants are limestone-gypsum wet desulfurization systems. This system has the advantages of high desulfurization efficiency and low cost. Delay, large inertia and other issues. Measure SO when operating conditions fluctuate 2 The instrument does not r...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B01D53/50G06F30/25G06F30/27G06N3/04G06N3/08B01D53/80
CPCB01D53/501G06F30/27G06F30/25G06N3/08B01D2251/404B01D2251/606G06N3/044
Inventor 金秀章刘岳仝卫国
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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