Optimization of Lithium-Bromide Absorption Refrigeration Systems Using Stochastic Methods

In this research project, we will attempt to optimize the thermodynamic performance and cost efficiency of current Lithium-Bromide absorption systems. These Li-Br systems are assumed to be driven by an arbitrary low temperature heat source such as waste heat from processing plants or solar collectors. We will develop a computational model in MATLAB in conjunction with stochastic methods to perform multi-objective optimization. The stochastic methods used are genetic algorithm optimization and particle swarm optimization. These two methods will stochastically vary several parameters such as temperatures, pressures, and/or flow rates at different parts of the system (absorber, condenser, evaporator, generator) and simulations in MATLAB will develop a Pareto Front, which is a representation of optimal cost efficiency versus thermodynamic performance. Eventually we want to be able to modify certain parameters of the system to come as close to the Pareto front as possible. The implications of this are far more cost effective and thermodynamically efficient Li-Br systems which benefit people, companies, and the environment.