Applying TLBO Algorithm for Optimizing a Double Flash Geothermal Power Plant

Abstract

In this paper the teaching learning based optimization (TLBO) is applied for the nonlinear constrained simulation based optimization of a double flash (DF) geothermal power plant. The TLBO algorithm is a teaching-learning process inspired algorithm based on the effect of influence of a teacher on the output of learners in a class. A DF system is able to achieve better energy utilization than a single flash cycle, which means that the application has a higher efficiency. The DF system modeled in this paper is a classic geothermal DF power plant. Geothermal brine exits the production well as a saturated liquid above atmospheric pressure and after completing the thermodynamic process the mechanical work of turbines is used to generate electrical power via a generator. The turbine exhaust is condensed in a condenser, while the saturated liquid exiting the separator is re injected into the geothermal reservoir via an injection well. The objective function of optimization problem is the power plant specific output (kJ/kg) and the variables are the temperatures at which the separator operates. In the end, the performance of some algorithms was investigated and results showed TLBO algorithm has the best performance among other optimization algorithms.