Q-learning-based metaheuristic algorithm for thermoeconomic optimization of a shell-and-tube evaporator working with refrigerant mixtures - Turgut, Oguz Emrah; Turgut, Mert Sinan; Kirtepe, Erhan (Springer, 2023)
This research study proposes a Q-learning-based metaheuristic algorithm framework for thermal design optimization of a shell-and-tube evaporator operating with different refrigerant mixtures, which is a highly complex real-world design problem and has not been investigated yet, in previous literature approaches before. The proposed method, called QL-HEUR, uses Q-learning as a high-level heuristic to iteratively guide the competitive recently emerged low-level metaheuristic algorithms. QL-HEUR is applied to 32 unconstrained optimization benchmark functions, and results are evaluated in statistical analysis. Moreover, three multidimensional constrained optimization problems will be solved. Respective solutions unravel that QL-HEUR is very effective in finding optimum solutions to constrained and unconstrained optimization problems. QL-HEUR is employed on the design optimization of a shell-and-tube heat exchanger running with different mixture pairs as a challenging real-world benchmark case. For the design case in which R134a-R1234yf (0.8:02) mixture is considered, 8.71% of the total cost is saved compared to the preliminary design of a heat exchanger operated with pure R1234yf refrigerant. For the second design case, the application of QL-HEUR results in a decrease of 8.93% for refrigerant composition R32-R134a (0.6:0.4) in comparison with the configuration running with pure R134a. It is also seen that the heat exchanger configuration running with pure R32 refrigerant yields the lowest total cost compared to the cases accomplished by varying mixture ratios of R290 and R32. It can be concluded that the optimum configuration of the heat exchanger operated with a refrigerant mixture can be conveniently employed for minimum total cost and global warming potential.
Keywords: Heat exchanger; Metaheuristics; Optimization; Q-learning; Thermal design