FTIR Quantitative Analysis of High-Concentration CO₂: Overcoming Saturation Effects via an MIV-GWO-ELM Integration...

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.