Optimization of power and heating systems based on a new hybrid algorithm

Abstract

A novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is applied on a base case cogeneration optimization problem called the modified CGAM problem with two objective functions. The first objective function, the exergetic efficiency, should be maximized and the second one is the total cost rate that should be minimized. The effects of important parameters, such as equivalence ratio, emission, and unit cost of fuel are studied on the exergetic and economic performance of the system. ª 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

A multi-fluid PSO-based algorithm for the search of the best performance of sub-critical Organic Rankine Cycles

“…The ASD-PSO was already adopted in a previous analysis on the influence of the working fluid on the ORC performance, but the results achieved by means of singlefluid optimizations on a limited number of working fluids prevented from the identification of an effective selection criterion [32]. In literature, other studies applied standard versions of the PSO or novel hybridization for carrying out mono-and multiobjective optimization of ORC systems, but none of them considered the choice of the working fluid within the optimization procedure [33,34].…”