• Yiduo Wang
  • Bin Chen
  • Zhengqiu Zhu
  • Rongxiao Wang
  • Feiran Chen
  • Yong Zhao
  • Laobing Zhang

Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency.

Original languageEnglish
Pages (from-to)27-38
Number of pages12
JournalProcess Safety and Environmental Protection
Volume138
DOIs
Publication statusPublished - 1 Jun 2020

    Research areas

  • Gas emission, Hybrid strategy, Neural network, Optimization algorithms, Source term estimation

ID: 71363263