Comparative Analysis of Intelligence Optimization Algorithms in the Thermo-Economic Performance of an Energy Recovery System Based on Organic Rankine Cycle

Abstract

This paper compares the performance of a group of intelligent algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), and repulsive particle swarm optimization (RPSO) based on the optimization of thermo-economic indicators such as the payback period (PBP), the levelized energy cost (LEC), the specific investment cost (SIC), and also in the optimization of the thermodynamic process (net power output) of an energy recovery system in a 2 MW natural gas internal combustion engine based on an organic Rankine cycle. Four parameters were considered to analyze and compare the performance of these algorithms: integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and the integral of time-weighted squared error (ITSE). Analyses of variances (ANOVA) were proposed for each of the parameters studied. The PSO and RPSO algorithms presented the best performance in terms of the mean and the standard deviation of the ISE, IAE, ITAE, and ITSE parameters. Significant differences were not found between the three algorithms in terms of the parameters considered. However, significant differences did exist when comparing groups (pairs) of algorithms considering a significance level of 5%. The ANOVA analysis showed that ITAE was the most affected parameter by population size, while the IAE and ITSE parameters were the less affected. In the optimization, the PSO algorithm obtained the best performance in terms of convergence with values of 0.1110 USD/kWh (LCOE), 4.6971 years (PBP), 1114 USD/kWh (SIC), and 173.64 kW (Wnet). PSO-based algorithms obtained better performance in computational terms compared with the genetic algorithms.

Assessing sustainable operational conditions of a bottoming organic Rankine cycle using zeotropic mixtures: An energy-emergy approach

“…In this work, the authors minimized the environmental impact by maximizing energy and exergy indicators. Other works have focused on the study of different optimization algorithms as reported by Duarte et al [ 31 ]. In this study, the authors evaluated the performance of three optimization algorithms (PSO, RPSO, and GA) using an organic Rankine cycle.…”

Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization

“…Based on machine learning, the GA and PSO were combined into a GA-PSO hybrid algorithm to predict and optimize the pump isentropic efficiency under full operating conditions in an ORC system [24]. The performances of GA, PSO, and repulsive particle swarm optimization (RPSO) based on the optimization of thermo-economic indicators were compared as well as in the optimization of the thermodynamic process of an ORC energy recovery system [25]. A multi-objective optimization using the PSO algorithm aimed at the proposed heat exchanger was conducted and the optimal design scheme had the potential to achieve a 71.46% decrease in the total annual cost [26].…”