SITE QUANTITATIVE-ANALYSIS; LIBS SPECTRA; POWER-PLANTS; ASH CONTENT; WELL LOGS; VALUE GCV; CALIBRATION; PREDICTION; MODEL; PERFORMANCE

Online measurement for the gross calorific Value (GCV) of coal is important in the coal Utilization industry. This paper proposed a rapid GCV determination method that combined a laser-induced breakdown spectroscopy (LIBS) technique with artificial neural networks (ANNs) and genetic algorithm (GA). Input variables were selected according to the physical mechanism and mathematical significance to improve the prediction of the ANN. GA was applied to determine an,optirnal architecture for the network instead of a trial and error method. As a result, the mean standard deviatiori (MSD) of the GCV for four prediction set samples is,0.38 MJ/kg in 50 trials (repetitions of training the ANN with the same; input data but different random initial weights and biases), proving that the ANN model is able to provide high modeling repeatability in the GCV analysis. The mean absolute error (MAE) of the GCV for the prediction set is 039 MJ/kg. The result meets the requirements (0.8 MI/kg) for coal online analyses using the neutron activation method in the Chinese national standard (GB/T 29161-2012).

Laser-induced breakdown spectroscopy (LIBS); Tobacco leaves; Plasma; Limit of detection (LOD);

The rapid detection and real-time monitoring of plant elements is very important in the field of agriculture. A method using Laser Induced BreakdownSpectroscopy (LIBS) for the determination of the spectroscopy characteristics of Cu in tobacco was evaluated. To perform the calibration curve, several tobacco samples containing Cu arranged from 8. 59 to 156. 35 mu g . g(-1) were prepared. The amount of Cu prepared in laboratory was measured using atomic absorption spectroscopy. Experiments indicated that the characteristic spectrum of Cu at the wavelength of 324. 75 nm was quite clear, and easy to obtain. From the characteristic spectra obtained from Cu-contained samples, the temperature and density of the plasma induced by laser were calculated to be 39 458. 94 K and 0. 74X 10(16), respectively. The fitting curve between the intensity of the spectral lines and the sample concentrations was established with the correlation coefficient (r square) of 0. 98, the mean relative standard deviation (RSD) of 2. 59%, the limit of detection (LOD) of 7. 72 mu g . g(-1) and the spectral signal-noise ratio of 7. 86. The method was successfully applied to predict the Cu amounts in tobacco samples, which has a potential to be used in cigarette production lines and tobacco breeding.