Coke oven gas generation prediction model integrating multi-time scale characteristics
XIE Lin-rong
HU Jie
CHEN Lue-feng
REN Yi
WU Min
Abstract：Accurate real-time prediction of coke oven gas generation is an important reference for real-time monitoring of coke oven production status and gas scheduling.In this paper,a coke oven gas generation prediction model incorporating multi-time scale features is developed to achieve effective prediction of coke oven gas generation.Firstly,the coke oven gas generation process is characterized,then a sliding window step-by-step decomposition model is established,based on which the coke oven gas generation is decomposed by empirical wavelet transform,and the components are reconstructed according to the sample entropy,and the reconstructed components are reconstructed by using a long and short-term memo-ry network to build a prediction model,and then experiments are carried out by using the actual field data.The experimental results show that the prediction accuracy of the proposed method reaches 0.29％for the average absolute percentage error,which is 0.3％higher than that of the single short-term memory network model,and 0.22％higher than that of the stepwise decomposition model.The results verify the feasibility and effectiveness of the proposed method.
Keywords：coke oven gas generation predictionmultiple time scalessliding window stepwise decompositionempir-ical wavelet transformlong short-term memory network
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 299-310 )