Fossil-fueled power plants increasingly demand alloys that remain reliable under extreme and dynamic service conditions. Water and wastewater systems – central to heat exchange, steam generation, and pollution control – perform best within defined chemistry windows but become suboptimal when deviations occur. Increasing energy demand and the intermittency of renewable generation have forced operators to cycle power output more frequently. Under these conditions, many systems reliant on water struggle to quickly adapt, and corrosion risks escalate and vary in time and location within the plant. Localized corrosion poses a major threat to the reliability of pollution control equipment in power plants, as it often leads to costly repairs and even unplanned shutdowns. Several corrosion-resistant alloys (CRAs), particularly stainless steels and Ni-based alloys, have been used with varying degrees of success, as aggressive conditions can still drive crevice corrosion and pitting in under-deposit regions, at surface imperfections, and weld heat-affected zones. Consequently, material selection and asset management must be tailored to site-specific factors including water chemistry, pH, temperature, fuel characteristics, equipment design, operational mode, and life-cycle cost. Together, this underscores the need for rigorously validated predictive tools that link alloy performance to the operational chemistries and transients actually encountered. To address this gap, a predictive model is used, which combines experimental and electrolyte speciation based modeling results with field experience. The electrolyte speciation based model is used to predict the repassivation potential (Erp) as a function of alloy composition and solution chemistry. Statistical design of experiments (DoE) is used to measure the effect of major contributing factors on the Erp of duplex stainless steel 2205 (UNS S32205), super-austenitic stainless steel AL6XN (UNS N08367), alloy C-276 (UNS N10276), and alloy 625 (UNS N06625). Tests were conducted in simulated solutions representative of plant solutions containing various combinations of aggressive, inhibitive, and diluent species (Cl–, Br–, F–, NO3–, SO42–, and dissolved CO2). A comprehensive suite of electrochemical techniques – including cyclic potentiodynamic polarization (CPP), critical crevice temperature (CCT), the Tsujikawa-Hisamatsu electrochemical (THE) test method, long-term open-circuit potential (OCP), and galvanic zero-resistance ammeter (ZRA) measurements – provide a robust dataset across chemistries and alloys. The modeling and experimental outputs are then integrated into a probabilistic Bayesian Network (BN) model that captures causal relationships among environmental drivers (e.g., temperature, halides, pH, etc.) and alloy attributes (e.g., Cr, Ni, Mo, and N content) in the form of conditional probabilities while explicitly accounting for data and model uncertainties.
