PINN-Assisted Low-Carbon Operation Configuration for Catalytic Cracking Units: An Economic–Environmental–Energy Co-Optimization Framework Based on Life Cycle Assessment

This study addresses challenges in fluid catalytic cracking (FCC) units, including inaccurate quantification of carbon emissions, difficulties in implementing low-carbon operational optimization, and low computational efficiency in solving complex process kinetics. A low-carbon operation optimization method based on physics-informed neural networks (PINNs) is proposed. First, a unit-level carbon footprint assessment model is established using process life cycle assessment (PLCA) to achieve high-resolution quantification of both direct and indirect carbon emissions. Second, a multi-objective low-carbon operation optimization model is developed considering carbon tax scenarios, incorporating carbon emissions and corresponding carbon tax costs into the optimization objectives to achieve economic and low-carbon synergistic optimization. Finally, a PINN-assisted surrogate model is designed by embedding material balance constraints into the neural network training process, enabling efficient approximation of complex product yield kinetics and improving optimization solution efficiency and predictive reliability. The proposed method is applied to optimize the operation of an FCC unit at a refinery site. The results indicate an increase of 12,048.851 CNY/h in profit, a reduction of 1088.921 kgCO2eq/h in CO2 emissions, and a decrease of 324.281 m3/h in steam consumption. Meanwhile, the PINN model exhibits excellent performance in product yield prediction, with an average R2 of 0.9968 and an average RMSE of 0.1482, outperforming conventional data-driven methods. The proposed approach balances carbon emission quantification accuracy, physical consistency in yield prediction, and optimization solution efficiency, providing a systematic and implementable technical framework for low-carbon operation optimization of FCC units.