## 5 Conclusions  
In this paper, a multi-iterative adaptive regularization optimization reconstruction algorithm (MIARO) for laser absorption spectroscopy tomography is proposed, which eliminates the cause of the problem related to a high reconstruction sensitivity to the initial value encountered by MAART. The reconstruction parameters are also introduced into the regularization, which ensures both the quality of the reconstruction target and the convergence of regularization. The effectiveness of the reconstruction with the MIARO algorithm was verified through simulation by setting axisymmetric and non-axisymmetric temperature and vapor distributions. The simulation results show that MIARO outperformed the other three algorithms using both numerical settings. To validate the noise resistance of the proposed algorithm, noise at four different levels was superposed on the signals, and MIARO exhibited the best quality compared to the other algorithms. In fact, with the MIARO algorithm applied in the non-axisymmetric simulation model, the temperature and concentration reconstruction errors were reduced by 0.010 and 0.038 compared with the MAART reconstruction algorithm; the standard deviation of the temperature reconstruction was reduced by 60.5%, and that of the concentration reconstruction was brought down by 51.0% for non-axisymmetric settings. The simulation results also confirm that the proposed new algorithm can achieve a clear boundary of the dynamic flow field, eliminate artifacts, and improve the measurement accuracy and robustness. Experiments using a butane-burner-generated flame were conducted. With the results obtained by thermocouples used as references, MIARO clearly demonstrated its advantages over the ART and Landweber algorithms. Even compared with the MAART algorithm, MIARO performed better for most of the measurement points. Actually, the average relative error of the MIARO reconstruction algorithm was 1.82%, and that of the MAART reconstruction algorithm was 2.44% in the experiments. The reconstruction algorithm is currently only used for relatively stable combustion flames in the laboratory, and further validation is required for extremely harsh combustion flames.  
Figure 19: Temperature measurements using different reconstruction algorithms at the flame center cross-section.  
**Author Contributions:** Conceptualization, R.Z. and B.Z.; methodology, R.Z.; software, R.Z.; validation, J.Z., B.Z. and R.C.; formal analysis, R.Z.; investigation, C.D.; resources, Z.Y.; data curation, Z.Y.; writing--original draft preparation, R.Z.; writing--review and editing, J.Z.; visualization, Z.Y.; supervision, C.D.; project administration, B.Z.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China (Grant No. 50976024 and No. 50906013) and the National Key Research and Development Program of China (Grant No. 2017 YFB0603204).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.