When deep learning encounters Fenton like reactions: A paradigm shift in intelligent degradation of organic pollutants in high salt matrices

The treatment of high salt organic wastewater is a major challenge in the field of environmental engineering. Although traditional Fenton like reactions can efficiently degrade organic matter, there are problems such as catalyst deactivation (reduced activity by 40–60%), complex reaction pathways (increased side reactions by 3–5 times), and difficult regulation in high salt environments. In recent years, the rapid development of deep learning (DL) technology has provided a new intelligent research paradigm for optimizing Fenton like reactions, achieving significant breakthroughs in multiple key indicators: model prediction accuracy has been improved by 35–45% (R 2 has increased from 0.65 to 0.92), drug consumption has been reduced by 25–35%, and processing costs have been reduced by 18–22%. This article systematically explores how the cross fusion of deep learning and Fenton like reactions can drive a paradigm shift in the degradation of high salt organic pollutants. Comparative analysis shows that the DL model exhibits unique advantages in treating complex nonlinear problems in high salt wastewater: within the range of Cl - concentration of 5–15%, the accuracy of DL in predicting ·OH quenching effect reaches 92±3%, which is 40% higher than traditional models; The real-time optimization system based on reinforcement learning has increased the utilization rate of H 2 O 2 from 65% to 88±5%. The research elaborates on the breakthroughs in the application of DL in four key areas: (1) In terms of process simulation, the CFD-DL coupled model shortens the computation time from 12 hours to 15 minutes, and the flow field prediction error is less than 5%; (2) In terms of process control, the intelligent dosing system achieves a dosing accuracy of ±2%, reducing the cost of water treatment by 1.2–1.8 yuan per ton; (3) In terms of state estimation, the real-time COD monitoring error of soft sensors is ≤ 8.5%, and the response time is<30 seconds; (4) In terms of hybrid modeling, the ASM-LSTM model improves generalization ability by 21% and reduces data requirements by 40%. Despite significant progress, there are still challenges such as data scarcity (the cost of a single high salt experiment is about 12000 yuan) and insufficient model interpretability. The future key development directions include: building a dedicated database covering 0–20% salinity gradients (target sample size>100000 groups), developing lightweight models with parameter sizes<500000 (maintaining accuracy of over 95%), and achieving deep integration of DL and digital twins (virtual factory error<3%). This review not only provides detailed experimental data support (including 8 industrial cases), but also plans a clear technical route for the intelligent upgrade of high salt wastewater treatment, which is expected to promote a 30–50% increase in treatment efficiency and set a benchmark for the cross innovation of environmental engineering and artificial intelligence. • Intelligent driven efficient degradation. • Breakthrough in adaptability to high salt environments. • Green chemistry and energy conservation and emission reduction. • Interdisciplinary integration innovation.