Cooling towers are used as a final heat sink which cools the reactor auxiliary systems and transfers the heat energy to the atmosphere. The reliability endowed upon the cooling tower operation in a nuclear power plant is considerably high when compared to other power plants since the proper functioning of auxiliary systems is crucial for the decay heat removal from the core. Hence, appropriate chemistry control is mandatory in the operation of cooling towers for corrosion control, scale prevention, and control of biological growth. The operating data and water chemistry parameters like conductivity, turbidity, free residual chlorine, pH, cycles of concentration, chlorides, hardness, and alkalinity are analyzed for a typical nuclear power plant. The effect of the individual water quality parameter is correlated by the Pearson matrix to find its influence on the system water chemistry parameter. It is observed that the build-up of chloride ions in the cooling water is the major contributing factor for the cooling tower feed and bleed operations. The aspect of reducing the feed and bleed operation is analyzed, and suitable modifications are suggested to reduce water consumption.

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A broad spectrum of water quality parameters is regularly monitored and assessed in auxiliary water systems. The integrity and availability status of a system are ensured by observing the trend of water quality indices which further warrants investigating the regulatory requirements of both safety and non‐safety‐related systems of a nuclear power plant. To evaluate the water chemistry state in a raw water system, we conducted a statistical study investigating water quality indices over 3 years. Obligatory statistical tools were utilized to comprehend and correlate the effects of the source water on the system. Most water quality parameters were found to be within an acceptable range, and correlations were found between the parameters, namely, conductivity, sodium, and chloride. Multivariate optimization and regression studies using the Box–Behnken design were conducted to minimize the system conductivity levels. The experimental data were fitted into a quadratic equation with an r2 value of 0.84. Model validation was conducted, and the fit between the experimental and model results was within 10%. This assessment identifies preventive action levels to avoid impairment of system integrity at an early stage.

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