Increase of energy engineering equipment performance based on neural network modeling and cluster data analysis

The questions of increase power equipment productivity based on neural network modeling and adaptive cluster analysis are considered. As an research object a boiler units of the heat network of a typical pulp-and-paper industry enterprise, which is a large consumer of steam for technological needs, are considered. The data mining algorithm in the field of power equipment operation, on the basis of which the ranges of the best boiler productivity and steam losses of heat network sections, is offered.