A Practical Approach To Combustion Process Optimization Using An Improved Immune Optimizer

Abstract

An improved version of the immune inspired optimizer SILO is presented in this paper. The new model identification method allows for utilization of model gains constraints. Moreover the operation of a new

Transition State  algorithm is analyzed based on a real-life example. The improved version of SILO was implemented in a real power boiler. Results from a real combustion process optimization are presented in this paper.