Journal ArticleDOI
Multi-objective particle swarm optimization of binary geothermal power plants
Joshua Clarke,James T. McLeskey +1 more
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In this paper, a method for determining the optimum use of a superheater and/or recuperator in a binary geothermal power plant is developed, and a multi-objective optimization algorithm is developed to intelligently explore the trade-off between specific work output and specific heat exchanger area.About:
This article is published in Applied Energy.The article was published on 2015-01-15. It has received 48 citations till now. The article focuses on the topics: Geothermal power & Particle swarm optimization.read more
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Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review
TL;DR: A review of emerging approaches with a particular emphasis on computer-aided design methods is presented in this article, where a number of approaches have been developed that address the systematic selection of efficient working fluids as well as the design, integration and control of ORCs.
Journal ArticleDOI
ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II
TL;DR: In this paper, the combination of ANN and non-dominated sorting genetic algorithm II (NSGA-II) has been implemented for modeling and reducing CO and NOx emissions from a direct injection dual-fuel engine.
Journal ArticleDOI
Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process
TL;DR: In this article, a two-stage solution approach for solving the problem of multi-objective optimal power flow (MOPF) is proposed for hybrid AC/DC grids with VSC-HVDC.
Journal ArticleDOI
Insights into geothermal utilization of abandoned oil and gas wells
Yong-Le Nian,Wen-Long Cheng +1 more
TL;DR: In this paper, the authors evaluate the performance of thermal energy extraction from abandoned oil and gas (AOGW) and geothermal power generation using AOGW with organic rankine cycle (ORC) systems.
Journal ArticleDOI
Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models
TL;DR: In this article, a generation planning model of grid-connected micro-grid system with photovoltaic (PV) and energy storage system was established with the objective of the maximum lifecycle net profit, and many optimization algorithms such as the particle swarm optimization (PSO), artificial fish swarm (AFS), genetic algorithm (GA), Artificial Bee Colony (ABC) algorithm and interior point algorithm were respectively used to solve the model for comparison analysis.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Book
Perry's Chemical Engineers' Handbook
TL;DR: In this paper, conversion factors and mathematical symbols are used to describe conversion factors in physical and chemical data and Mathematical Symbols are used for converting, converting, and utilising conversion factors.
Perrys chemical engineers handbook
TL;DR: Perry's Chemical Engineers' Handbook as mentioned in this paper is a free download pdf for chemical engineering applications, from the fundamentals to details on computer applications and control, and it can be found in any computer science course.
Book
Computational Intelligence: An Introduction
TL;DR: Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.
Journal ArticleDOI
Recent approaches to global optimization problems through Particle Swarm Optimization
TL;DR: A Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.