A comprehensive performance analysis of meta-heuristic optimization techniques for effective organic rankine cycle design

Abstract

Optimizing the operating parameters is of great importance to improve Organic Rankine Cycle (ORC) system efficiency. Nowadays, meta-heuristic algorithms are widely used to obtain fast and effective solutions. However, it is a difficult task to determine the most effective algorithm among dozens of available algorithms to solve the ORC design problem like many other real-world optimization problems. Moreover, defining a feasible solution and determining a method that can find this solution quickly and decisively is a major challenge. To overcome these challenges, a well-planned and comprehensive research is essential. In this article, a research consisting of two stages was conducted to determine the most effective meta-heuristic optimization methods that can find the optimum and feasible solutions of the ORC design problem in a stable and fast way. 31 algorithms selected among the most up-to-date and powerful meta-heuristic search algorithms in the literature were used in simulation studies. The data obtained were analyzed by using the non-parametric statistical test methods. The results show that TLABC, DE, and PSO algorithms are very successful in finding a feasible solution. In addition, the TLABC algorithm can find a feasible solution in a shorter time than its alternatives. The maximum net power output and the total cost of the system were calculated as 74.593 kW and 692,452 $, respectively for optimum design. Thus, the total cost per net power output of the ORC system was determined as 9283.066 $/kW.