Entransy analysis and optimization of irreversible Carnot-like heat engine

¹ Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
² Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran

^a Corresponding author: mohammadhosein.ahmadi@gmail.com

Received: 14 October 2015
Accepted: 14 March 2016

Abstract

Owing to the energy demands of the world and the issues involved with global warming, analyzing and optimizing power cycles have increased in importance. In this work, the concepts of entransy dissipation, power output, entropy generation, energy, exergy output, exergy efficiencies for irreversible heat engine cycles are applied as a means of analyzing them. This paper presents thermo-dynamical study of an irreversible heat engine cycle with the aim of optimizing the performance of the heat engine cycle. Moreover, four different strategies in the process of multi-objective optimization are proposed, and the outcomes of each strategy are evaluated separately. In the first scenario, in order to maximize the exergy output, ecological coefficient of performance (ECOP) and exergy-based ecological function (EECF), a multi-objective optimization algorithm was executed. In the second scenario, three objective functions comprising the ecological function, ECOP and exergetic performance criterion were maximized at the same time by employing multi objective optimization algorithms. In the third scenario, in order to minimize the entransy dissipation and maximize the ECOP and EECF, a multi-objective optimization algorithm was executed. In the fourth scenario, three objective functions comprising the exergetic performance criterion and ECOP and EECF were maximized at the same time by employing multi objective optimization algorithms. All the strategies in the present work are executed via the multi objective evolutionary algorithms based on NSGA|| method. Finally, to govern the final answer in each strategy, three well-known decision makers are executed.