Multi-objective particle swarm optimization of binary geothermal power plants

In this paper, a method for determining the optimum use of a superheater and/or recuperator in a binary geothermal power plant is developed. Additionally, a multi-objective optimization algorithm is developed to intelligently explore the trade-off between specific work output and specific heat exchanger area and allow visualization of the entire Pareto-optimal set of designs for a wide range of geothermal brine temperatures and dry-bulb temperatures. Selected data is tabulated to show representative optimal designs for each combination of dry-bulb temperature and brine temperature. This work illustrates the development and use of a sophisticated analysis tool utilizing multi-objective particle swarm optimization to allow calculation of the Pareto-optimal set of designs under any combination of dry-bulb temperature and brine temperature while accounting for necessary real-world constraints.