Establishment and Application of Classification Model and Calibration Model of Steel Scrap Based on Laser Induced Breakdown Spectroscopy and Back Propagation Neural Network

Based on self-built laser induced breakdown spectroscopy (LIBS) equipment, software and back propagation (BP) neural network, the classification model and calibration model were established using the LIBS spectral data together with category and certified values of Si, Mn, Cr, Ni, Cu of 18 steel standard samples, which were used for the detection of actual samples. A three-layer BP neural network model was established with the relative intensity under the optimized spectral line of each element (the spectral lines of Si, Mn, Cr, Ni, Cu were 251.60, 293.86, 286.41, 227.01, 213.60 nm) and the corresponding spectral line of iron element (the corresponding spectral lines of Fe were 263.54, 292.66, 271.44, 263.54, 206.98 nm) as input variables. For classification model, the maximum number of iterations was 500, the learning rate was 0.01, and the number ratio of the training set to the test set in the 360 groups of data was 3∶1. For calibration model, the optimal number of iterations for Si, Mn, Cr, Ni, and Cu were 200, 200, 200, 160, 280, the number ratio of the training set to the test set in 360 groups of data was 4∶1, and the performance of the calibration model was evaluated by the 4 indices, including linear correlation fit (R²), root mean square error (RMSE), mean percentage error (MPE) and sum of squares of residuals (PRESS). As found by the results, the predicted accuracy of the test set classification with the classification model was 100%. R² of the calibration models of the 5 elements in the test set were 0.941, 0.983, 0.983, 0.988, 0.987, and RMSE were 0.061 2, 0.060 7, 0.042 5, 0.049 6, 0.016 9, respectively. The predicted values of actual samples with calibration models were consistent with those given by GB/T 4336-2016. The established method could be used for the classification of steel scrap samples and the rapid detection of their components in the steel industry.