Application of Technological Algorithms and Mathematical Modelling in Cycle Chemistry Monitoring Systems

Most Russian fossil fuelled power plants equipped with cycle chemistry monitoring systems (CCMS), as a tool to maintain water chemistry within operating limits, use these systems in informative mode only without performing any particular control function. Modern modeling methods allow for the optimizing of water chemistry control. According to the Russian guidelines, CCMS of a single unit depending on its type (drum or once-through boiler) should be based on on-line monitoring performed by 25–35 chemical analyzers; 50–100 “grab” sample analyses (daily) and about 20 on-line monitored operating parameters. Significant volume of the incoming information needs to be organized and presented to operating personnel in order to improve and optimize water chemistry at power plants, which is the main idea of CCMS, this can be achieved by implementing technological algorithms and modeling in the systems. Most of the operating CCMS are supplied with alarms to indicate water chemistry failure, alerting the operating personnel but not informing them about the danger and gravity of the event. The use of water chemistry evaluating algorithms based on system of indices provides data analysis for the operating personnel informing them on the danger of the event. Implementation of CCMS at power plants should change the role of laboratory monitoring from routine to diagnostic. The application of theoretical and experimental dependencies of different water quality parameters can be used for water chemistry optimization. In order to predict impurity distribution and deposit build-up on heat transfer surfaces and turbine blades, mathematical models of thermal cycles based on balance equations can be used in CCMS. Such models can be formed for ion-impurities, silica acid and corrosion products. Another type of model predicts impurity behavior in the event of deteriorating feed water chemistry. By means of such models operating personnel receive information about the time available to prevent steam chemistry failure. The model results should be compared with the available impurities solubility data. The majority of power plants have their particular water chemistry problems and technological disadvantages, which together with preventive and emergency measures are described in water chemistry guidelines applied at power plants. The application of logical algorithms based on these guidelines can be used in CCMS as an informative support to operating personnel in case of water chemistry failure. All the algorithms and mathematical models should only be used after their refinement to the individual power plant.