This paper aims to provide a comprehensive and critical review of corrosion issues in wet flue gas desulfurization (WFGD) systems. It systematically analyzes the corrosive environment, common corrosion forms and factors influencing material degradation. The review synthesizes recent advancements in corrosion control methods and outlines targeted future research directions to enhance system durability, with a particular emphasis on synergistic damage mechanisms and the integration of monitoring and predictive technologies.
This review begins by outlining flue gas desulfurization technologies and the working principles of wet scrubbing. It then meticulously examines the corrosive environment within WFGD processes, focusing on the mechanisms of acid condensation, chloride attack, scale deposition and erosion. The analysis covers prevalent corrosion forms, including uniform, pitting and erosion-corrosion. Furthermore, the paper critically evaluates recent studies on the performance of alloys, surface treatments and the role of microalloying, incorporating quantitative data where available.
The analysis confirms that the stability of passive films on materials like stainless steel is highly susceptible to chloride-induced breakdown and operational parameters, especially temperature. The synergistic effect of erosion-corrosion and the combined action of chlorides and sulfates significantly accelerate material failure. While current control methods offer mitigation, their effectiveness is highly dependent on the specific WFGD environment. The incorporation of elements like Mo, Cr and N is crucial for enhancing passivation film resilience.
This review offers a structured and synthesized analysis that integrally links the unique WFGD corrosive environment with material performance and degradation mechanisms. It moves beyond descriptive summary to provide critical evaluation, highlighting the central role of synergistic effects. The study distinctly identifies research gaps concerning multi-factor corrosion and provides targeted future directions, including the integration of real-time monitoring and machine learning for lifespan prediction, addressing specific bottleneck challenges in the field.
