An Intelligent prediction model for UCG state based on dual-source LSTM

Yuteng Xiao, Hongsheng Yin*, Tianhong Duan, Honggang Qi*, Yudong Zhang, Alireza Jolfaei, Kaijian Xia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Underground coal gasification (UCG) is a serious attempt to clean and efficient use of coal, but it has not been able to solve the problem of stable production. Predicting UCG can provide effective guidance for control, which effectively solves this problem. Existing UCG prediction models are not accurate, and most of them can only predict a single variable, and cannot adequately predict the UCG state. The paper proposes the concept of combustible gas equivalents that can characterize the concentration of mixed gas through stoichiometry and material balance equations. The equivalent gradient is introduced to characterize the trends in equivalent, and the UCG state discrimination standard is established to evaluate the UCG state. Eventually, a dual-source long short-term memory (LSTM) prediction model is proposed for predicting UCG state. The experimental results show that compared with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) prediction model, the model can make a better prediction of equivalent value and the accuracy of predicting trends in equivalent reaches 90.99%.

Original languageEnglish
Pages (from-to)3169-3178
Number of pages10
JournalInternational Journal of Machine Learning and Cybernetics
Issue number11
Early online date1 Oct 2020
Publication statusPublished - Nov 2021


  • Equivalent
  • LSTM
  • Prediction
  • Underground coal gasification


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