Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger

Abstract

SUMMARYThermodynamic and thermoeconomic optimization of a horizontal geothermal air conditioning system has been performed. A model based on energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. An artificial intelligence technique known as evolutionary algorithm has been utilized for optimization. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective optimization, thermoeconomic single objective optimization and multi-objective optimization (with simultaneous optimization of thermodynamic and thermoeconomic objectives) are performed. In multi-objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto front is presented here. The results obtained using the various optimization approaches are compared and discussed.

Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system

“…In this decision-making process, the point of Pareto frontier that has shortest distance from the equilibrium point is selected as a final optimum solution. This solution is not only located on the Pareto frontier but also it archives the minimum possible values for both objectives (Sayyaadi et al, 2009 andAmlashi, 2010). The presented data for the optimum solution of the multi-objective optimization scenario reveals the corresponding data for this selected solution as described in Fig.…”

Thermodynamic and thermoeconomic optimization of a cooling tower-assisted ground source heat pump

“…In this decision-making process, the point of the Pareto frontier that has the shortest distance from the equilibrium point is selected as the final optimum solution. This solution is not only located on the Pareto frontier, but it also archives the minimum possible values for both objectives Sayyaadi and Amlashi, 2010). The presented data for the optimum solution of the multiobjective optimization scenario is the corresponding data for the selected solution from the Pareto frontier, as described in Fig.…”

“…5. This process is described in detail by Sayyaadi et al (2009) and Sayyaadi and Amlashi (2010). In this decision-making process, the point of the Pareto frontier that has the shortest distance from the equilibrium point is selected as the final optimum solution.…”

“…The evaporator water inlet and outlet temperatures are 12 • C and 7 • C, respectively. The mean ground temperature (T g ) is obtained at 20 • C at the design condition Sayyaadi and Amlashi, 2010). The atmosphere dry bulb and wet bulb temperatures at design condition in Tehran are 40 • C and 24 • C, respectively.…”

Multi-objective genetic algorithm optimization of thermoelectric heat exchanger for waste heat recovery

“…Different objective functions and constraints (e.g. pumping power, heat transfer rate, efficiency, total cost, volume, heat transfer surface area) 18–26, and design variables (e.g. heat exchanger geometry and configuration) have been considered in literature.…”