Thermo-economic modeling and optimization of underfloor heating using evolutionary algorithms

Thermal modeling and optimal design of an underfloor heating system are presented in this paper. Analytical modeling is coupled with some experimental data is used to obtain the rate of heat transfer and temperature distribution in the presented system. After thermal modeling, tube length, tube radius, water mass flow rate and number of panels are considered as four design parameters. Both the Genetic Algorithm and the Particle Swarm Optimization Algorithm are applied to obtain the minimum total annual cost (sum of investment and operational costs) as objective function with discrete and continues variables.Both algorithms are converged with 0.2% differences. The optimization results show that the underfloor heating optimum configuration has 13 panels, each of them with 80m length, 7mm tube inside radius where water passes through the tubes with the rate of 0.503kg/s. The sensitivity analysis of change in the optimum total annual cost, rate of heat transfer and panel temperature with change in design parameters of the underfloor heating are also performed. The results show that by increase of design parameters, both the total annual cost and the rate of heat transfer increase. In addition, the tube length and water mass flow rate were found as the most important parameters in the design and optimization of underfloor heating system. Finally, a closed form equation is presented between the rate of heat transfer and five important variables with acceptable precision by using Artificial Neural Network.