We report the first application of gas total emission using a DFB diode laser for gas concentration measurements combined with two LEDs for gas velocity measurements. In situ gas total emissions and particle density measurements in an industrial pipeline using simultaneous tunable diode laser absorption spectroscopy (TDLAS) and optical scintillation cross-correlation technique (OSCC) are presented. Velocity mean values obtained are 7.59 m/s (OSCC, standard deviation is 1.37 m/s) and 8.20 m/s (Pitot tube, standard deviation is 1.47 m/s) in a steel plant pipeline for comparison. Our experiments demonstrate that the combined system of TDLAS and OSCC provides a new versatile tool for accurate measurements of total gas emissions.

Based on tunable diode laser absorption spectroscopy (TDLAS), two-dimensional (2D) distribution reconstructions of gas concentration and temperature are realized using an algebraic reconstruction technique (ART). The influence of the beam distribution and grid size on combustion field reconstruction is investigated to attain optimal reconstruction results with a limited number of beams. Under limited optical-path numbers, it shows that a better spatial resolution is attainable only when the laser beam paths are vertical and parallel to the symmetry axis of the combustion field. Furthermore, experiments with 16 beam paths using one and two flat flame combustion fields are carried out in different fuel-air equivalence ratios under room temperature. The results are in agreement with the simulation results, and the time resolution is less than 1 s.

In situ rapid classification of rock lithology is crucial in various fields, including geological exploration and petroleum logging. Laser-induced breakdown spectroscopy (LIBS) is particularly well-suited for in situ online analysis due to its rapid response time and minimal sample preparation requirements. To facilitate in situ raw rock discrimination analysis, a portable LIBS device was developed specifically for outdoor use. This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. Initially, spectral data were collected from random areas of each rock sample, and a series of pre-processing steps and data dimensionality reduction were performed to enhance the accuracy and efficiency of the LIBS device. Subsequently, four classification algorithms—linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were employed for classification discrimination. The results were evaluated using a confusion matrix. The final average classification accuracies achieved were 95.71%, 93.57%, 92.14%, and 98.57%, respectively. This work not only demonstrates the effectiveness of the portable LIBS device in classifying various original rock types, but it also highlights the potential of the XGBoost algorithm in improving LIBS analytical performance in field scenarios and geological applications, such as oil logging sites.

A novel re-injection off-axis integrating cavity system is developed for simultaneously measuring N ₂ O, H ₂ O and CO in the atmosphere.