DSEF-CNN: FTIR-based differential-spectral FPN-CNN for real-time multi-gas leak identification in petrochemical monitoring

Addressing the limitations of conventional gas leak detection techniques in petrochemical environments, this study introduces a deep learning framework (DSEF-CNN) based on Fourier transform infrared spectroscopy. Traditional approaches such as multilayer perceptron (MLP) and 1D-CNN exhibit reduced recognition accuracy. This is due to spectral overlap among multiple gases, industrial noise interference, and their low sensitivity to low-concentration leaks. The DSEF-CNN is designed specifically for high-resolution FTIR spectral data and integrates four targeted components: (1) a differential processing module amplifies dynamic concentration changes during leaks, which leverages FTIR's subtle spectral capture to boost sensitivity to low-concentration leaks; (2) a 1D feature pyramid network fuses multi-scale features, from fine fingerprint peaks to broad functional group peaks, which resolves spectral overlap and aligns with FTIR's wavenumber-dependent characteristics; (3) an efficient channel attention mechanism selectively emphasizes wavenumber channels related to target gases, which mitigates industrial noise in complex FTIR profiles; and (4) a lightweight CNN backbone enables end-to-end classification, which is optimized for real-time processing of continuous FTIR spectral streams. Extensive simulations and field experiments demonstrate that the DSEF-CNN model significantly improves recognition accuracy. It is thereby validated for real-time multi-gas leak identification in petrochemical monitoring scenarios.