The quantitative analysis of Fourier transform infrared (FTIR) spectroscopy for monitoring high-concentration industrial gases, such as CO2 in steel manufacturing, is severely challenged by spectral absorption saturation, which introduce strong nonlinearities and degrade the accuracy of traditional methods. To address this, we propose a hybrid inversion framework, the mean impact value-enhanced gray wolf optimizer-based extreme learning machine (MIV-GWO-ELM), which synergizes intelligent spectral feature selection via MIV to mitigate saturation effects and employs GWO for the global optimization of ELM’s initial parameters to enhance stability. Experimental results on high-concentration CO2 (5.0%–9.75%) demonstrate the model’s performance, reducing MAE, RMSE, and MAPE by 79.72%, 79.50%, and 78.58%, respectively, compared to the baseline ELM, while also outperforming the Levenberg–Marquardt algorithm and a multilayer perceptron with approximate error reductions of 71% and 58%, all within a training time of under 13 s. This work effectively enhances the precision of FTIR systems under nonlinear conditions, offering a reliable and efficient solution for industrial gas monitoring and a promising strategy for complex spectroscopic inversions.
Zhao et al. (Sat,) studied this question.