OICC Press Signal Processing and Renewable Energy (SPRE) 2588-7335 10 1 2026 03 31 Physics-Guided Temporal Transformer for CO2 Corrosion with SHAP Case study: Asaluyeh SPGC Complex, Iran 10.57647/spre.2026.1001.01 EN Afshin Mohammadi Department of Computer Engineering, Sar.C., Islamic Azad University, Sari, Iran Ebrahim Akbari Department of Computer Engineering, Sar.C., Islamic Azad University, Sari, Iran Homayun Motameni Department of Industrial and Computer Engineering, Mazandaran University of Science and Technology, Babol, Iran Faraein Aeini Department of Computer Engineering, Sar.C., Islamic Azad University, Sari, Iran Journal Article 2026 03 31 The structural integrity of the oil and gas industry is at high risk due to CO₂-induced corrosion; consequently, practical, explainable mechanisms are required to accurately predict this phenomenon. The available mechanistic models, like the de Waard–Milliams equation, though entrapping fundamental thermodynamics, do not withstand dynamic dosing and time-varying conditions. Although the conventional machine learning and deep learning models improve accuracy, though often lack physics consistency and interpretability. A Physics-Guided Temporal Transformer (PGTT), which integrates a Temporal Fusion Transformer backbone with a physics-informed loss (of de Waard–Milliams), and SHapley Additive exPlanations (SHAP) for feature attribution, is proposed here. By applying a comprehensive sequential dataset from 22 inhibitor dosing experiments (15,400 samples during 60+ hours), this PGTT achieves higher performance with an average , , and  of  over five independent runs, and outperforms Random Forest , , and MLP  baselines. According to the SHAP, Temperature (26.5%) and CO₂ pressure (17.7%) are identified as the dominant stimulants, consistent with corrosion science. This case study reveals a reduction in prediction error of less than 5%, supporting proactive inhibitor dosing and pipeline integrity management.