Neural network estimation of kinetic parameters in distributed activation energy model (DAEM) without a priori assumptions for parallel reaction system

Shinji Wakimoto, Yoshiya Matsukawa, Yui Numazawa, Yohsuke Matsushita, Hideyuki Aoki

Research output: Contribution to journalArticlepeer-review


In this study, a new estimation method for the kinetic parameters in a distributed activation energy model (DAEM) was designed and developed. In the proposed method, the conversion estimation by the DAEM is regarded as a feedforward computation of a three-layer neural network, and the kinetic parameters of the DAEM are estimated by optimization of the neural network. The proposed method does not require an a priori assumption of the kinetic parameters or mechanism of a parallel reaction system. First, we created reaction data using numerical simulations, and a kinetic analysis using the neural network was performed. The neural network predicted conversion X very accurately; however, reactions with low contributions to the parallel reaction system also appeared. Next, we carried out a kinetic analysis using the neural network with the lower limit on the contribution of the ith reaction Vi*/V*. The lower limit on Vi*/V* did not influence the prediction accuracy of X and had a significant effect on reducing the reactions with low Vi*/V* and the estimation accuracies of the kinetic parameters in the DAEM. The optimal value of the lower limit on Vi*/V* was determined to be 1.0×10−5–1.0×10−4 when using the neural network with 64 hidden layer nodes. Moreover, the prediction accuracy of the proposed method was compared with those of conventional methods — the single-Gaussian method (1-DAEM), double-Gaussian method (2-DAEM), and Miura and Maki method. The kinetic parameters estimated using the proposed method were closer to the true values than those obtained using conventional methods. Moreover, the X value predicted by the proposed method was more accurate than that predicted by conventional methods. The influence of approximation on training data creation was also examined. The estimation accuracy of the neural network was still high but slightly deteriorated when the neural network was optimized using the reaction data created without the approximation.

Original languageEnglish
Article number127836
Publication statusPublished - 2023 Jul 1


  • DAEM
  • Kinetic analysis
  • Neural network
  • RF5
  • Solid-fuel reaction

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Organic Chemistry


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