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<p>This study investigates the influence of waveform design on the performance of pulsed current cathodic protection (PCCP) under soil-simulating conditions relevant to buried pipelines and oil well casings. Square, exponential rise, exponential fall, and ramp waveforms were experimentally evaluated in terms of potential distribution, current consumption, surface deposit morphology, and pitting behavior. The results show that the ramp waveform consistently outperforms other modes by inducing the strongest skin effect, minimizing concentration polarization, and ensuring the most uniform potential distribution along the protected surface. Complementary adaptive neuro-fuzzy inference system (ANFIS) modeling was employed to establish predictive relationships among waveform, frequency, time, rectifier voltage, and distance from the drain point with pipe-to-soil potential and protective current. The trained models achieved high accuracy (Pearson correlation coefficients ≥ 0.97) and enabled multi-objective optimization of operating frequency, with an optimum around 4.8&#xa0;kHz for the ramp waveform. SEM and XRD analyses further revealed that the ramp waveform promotes layer-by-layer (Frank–van der Merwe) deposit growth, correlating with superior resistance to pitting corrosion, whereas the square waveform fosters rough island-type deposits prone to localized attack. Overall, this work highlights waveform engineering as a decisive factor in PCCP efficiency and demonstrates that coupling experimental insights with machine learning provides a powerful framework for optimizing corrosion protection strategies in buried steel structures.</p> Graphical Abstract <p></p>

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Mechanistic Insights Regarding the Role of Skin Effect in Pulsed Current Cathodic Protection: Experimental Studies, ML Modeling, and Multi-objective Optimization

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Babak Hashemi

摘要

This study investigates the influence of waveform design on the performance of pulsed current cathodic protection (PCCP) under soil-simulating conditions relevant to buried pipelines and oil well casings. Square, exponential rise, exponential fall, and ramp waveforms were experimentally evaluated in terms of potential distribution, current consumption, surface deposit morphology, and pitting behavior. The results show that the ramp waveform consistently outperforms other modes by inducing the strongest skin effect, minimizing concentration polarization, and ensuring the most uniform potential distribution along the protected surface. Complementary adaptive neuro-fuzzy inference system (ANFIS) modeling was employed to establish predictive relationships among waveform, frequency, time, rectifier voltage, and distance from the drain point with pipe-to-soil potential and protective current. The trained models achieved high accuracy (Pearson correlation coefficients ≥ 0.97) and enabled multi-objective optimization of operating frequency, with an optimum around 4.8 kHz for the ramp waveform. SEM and XRD analyses further revealed that the ramp waveform promotes layer-by-layer (Frank–van der Merwe) deposit growth, correlating with superior resistance to pitting corrosion, whereas the square waveform fosters rough island-type deposits prone to localized attack. Overall, this work highlights waveform engineering as a decisive factor in PCCP efficiency and demonstrates that coupling experimental insights with machine learning provides a powerful framework for optimizing corrosion protection strategies in buried steel structures.

Graphical Abstract