Thermodynamic modeling based optimization for thermal systems in heat recovery steam generator during cold start-up operation

In this research paper, a comprehensive thermodynamic model of a thermal system in a dual pressure heat recovery steam generator during cold start-up operation is presented. The model consists of unknown parameters identified by two parameter identification techniques. The first algorithm is an online adaptive parameter identification algorithm which is based on gradient algorithm with integral cost function and forgetting factor. Second algorithm is a designed parameter identification algorithm based on the genetic algorithm method. Results are compared with a broad set of actual data taken from one of the Iranian power plants during cold start-up. Simulation results represent the effectiveness and reliability of the developed model and each of two parameter identification techniques. A comprehensive study is carried out in order to compare two applied techniques. The first technique leads to time-varying parameters and the second reaches the constant parameters with a piecewise model. In order to achieve a simulated model for heat recovery steam generator cold start-up, the costs of the modeling and identification process, and the concepts of the optimization lead to the designed algorithm based on genetic algorithm.