Eurasian Journal of Chemical, Medicinal and Petroleum Research

Abstract Cooling systems in thermal power plants play a decisive role in maintaining steam cycle efficiency, enhancing equipment reliability, and reducing maintenance costs. In the present study, the water chemistry and corrosion behaviour in the hybrid (dry-wet) cooling system of a sample thermal power plant were investigated. This plant, with a nominal capacity of 1000 MW, supplies the bulk of its water demand from treated municipal wastewater and employs a combination of dry and wet cooling towers to reduce water consumption. Sampling was conducted on demineralised water, recirculating water of the dry tower, inlet water to heat exchangers, wet tower water, and makeup water. General parameters including pH, electrical conductivity, TDS, temperature, and salinity were measured. Additionally, the concentrations of metallic elements were determined using ICP-MS. The results indicated that the demineralised water exhibits exceptionally high purity and is virtually devoid of metal ions, whereas in the dry cycle, a significant increase in aluminium concentration up to approximately 260 ppb was observed, indicative of active corrosion in the aluminium radiators of the dry tower. In contrast, in the wet tower, the concentrations of calcium, sodium, potassium, silica, copper, and zinc increased markedly, attributable to evaporation, concentration of dissolved solids, and the use of wastewater as make-up water. Comparison of the results with EPRI guidelines revealed that although iron and copper levels fall within acceptable ranges, the elevated aluminium concentration may be regarded as the primary indicator of heat transfer equipment degradation. Accordingly, the revision of the water chemistry control programme, continuous monitoring of dissolved aluminium, and reassessment of the dry tower operating conditions are proposed as the most critical management strategies. The findings of this research can serve as a basis for the development of intelligent corrosion monitoring programmes in power plants equipped with hybrid cooling systems.

Abstract Electrochemical characterization techniques have become indispensable for understanding corrosion phenomena, enabling both fundamental mechanistic insights and practical corrosion monitoring across diverse industrial applications. This comprehensive review systematically examines the principles, applications, and data interpretation strategies for key electrochemical methods used in corrosion research. Cyclic voltammetry (CV) has emerged as a powerful mechanistic probe, capturing real-time redox activity and surface transformations, though historically underutilized due to the irreversible nature of corrosion reactions . Electrochemical impedance spectroscopy (EIS) remains the most versatile technique, providing frequency-dependent information on charge transfer resistance, double-layer capacitance, and diffusion processes, with applications ranging from reinforced concrete diagnosis to nanostructured coating evaluation . Potentiodynamic polarization enables rapid determination of corrosion current density, Tafel slopes, and pitting potentials through the Stern–Geary relationship . Electrochemical noise analysis detects spontaneous current and potential fluctuations sensitive to localized corrosion events such as metastable pit growth . Recent advances in machine learning have revolutionized data interpretation, with hybrid models achieving R² > 0.99 prediction accuracy for corrosion rate forecasting through integration of swarm intelligence optimization with deep learning architectures . Four-dimensional impedance analysis has emerged for time-varying systems, enabling instantaneous impedance determination during non-stationary corrosion processes . This review concludes that effective corrosion characterization requires integrated approaches combining complementary techniques with advanced data analytics, bridging laboratory mechanistic understanding with field-applicable monitoring solutions.