S. Sarawasthy, K.Divya, Santosh Kumar.B, Gokul.M
The complexity exists almost in all the industrial applications to determine the optimal solution and predict how the solution behaves for the changes in the operational parameters. The engineering process problem will have a large no. of solutions out of which some are feasible and some are infeasible solutions. The aim of optimizing task is to get the best solution out of the feasible solutions set. The solution space contains the complete set of feasible solutions and optimization involves some kind of search with in the space. In both the cases of stochastic and deterministic searching methods it is possible to enhance the performance by judicious operating procedure. In this paper, first section represents the implementation of K-means clustering method by considering hydrogen, ratio of hydrogen and gross calorific value along with its corresponding loss. The resultant clustering patterns were analysed with the objective of achieving linearity. The second section demonstrates implementation of genetic algorithm to perform multilevel optimization and results that overcome the intricacy produced by K-Means over boiler samples.
K –Means, Boiler, Efficiency, Optimization ,Loss , optimal solution