Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method

Denglong Ma; Wei Tan; Zaoxiao Zhang; Jun Hu

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Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method
- Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method
- 中国化学工程学报（英文版） 2018年26卷第2期页码：356-363
- Affiliations：
  
  1. 2 State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University,Xi'an,China,710049
  2. 3 School of Chemical Engineering and Technology, Xi'an Jiaotong University,Xi'an,China,710049
  3. 4 School of Chemical Engineering, Northwest University,Xi'an,China,710069
- Author bio：
- Funds：
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  中图分类号：
- 纸质出版：2018
- Accepted：
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Denglong Ma, Wei Tan, Zaoxiao Zhang, 等. Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method[J]. 中国化学工程学报（英文版）, 2018,26(2):356-363.

Denglong Ma, Wei Tan, Zaoxiao Zhang, Jun Hu. Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method[J]. Chin.J.Chem.Eng., 2018, 26(2): 356-363.
Denglong Ma, Wei Tan, Zaoxiao Zhang, 等. Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method[J]. 中国化学工程学报（英文版）, 2018,26(2):356-363. DOI：

Denglong Ma, Wei Tan, Zaoxiao Zhang, Jun Hu. Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method[J]. Chin.J.Chem.Eng., 2018, 26(2): 356-363. DOI：

摘要

Abstract

Source term identification is very important for the contaminant gas emission event. Thus

it is necessary to study the source parameter estimation method with high computation efficiency

high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However

it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO (partial swarm optimization)-Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence

a new 1-step nonlinear method combined Tikhonov regularization and PSO algorithm with nonlinear forward dispersion model was proposed. First

the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then

the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO-Tikhonov regularization method. 1-step nonlinear method even performs better than other two methods in some cases

especially for source strength and downwind distance estimation. Compared with 2-step nonlinear method

1-step method has higher computation efficiency. On the other hand

the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally

single PSO algorithm was compared with 1-step nonlinear PSO-Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of 1-step nonlinear PSO-Tikhonov regularization method is its reasonable confidence interval

which is not obtained by single PSO algorithm. Therefore

1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.

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