Modeling and optimization of R-717 and R-134a ice thermal energy storage air conditioning systems using NSGA-II and MOPSO algorithms

Rahdar, Mohammad; Heidari, Mohammad Mehdi; Ataei, Abtin; Choi, Jun-Ki

doi:10.1016/j.applthermaleng.2015.11.068

Cited by 40 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…10 Mohammad HS et al modeled the energy consumption and economy of the refrigeration system, adopting non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) to solve the objective functions. 11 Subsequently, on this basis, a distributed multi-objective differential evolution improved particle swarm optimization algorithm (D-MOPSODE) based on decentralized control structure was proposed to solve the optimal hourly load rate of chillers and the cooling ratio of ice tank in the ISAC system. 12 However, in practical applications, due to the poor local search ability of genetic algorithm, consuming time is long, and the search efficiency is low in the later stage of evolution.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

Zhou

Zhang

et al. 2021

Building Services Engineering Research and Technology

View full text Add to dashboard Cite

Reasonable distribution of cooling load between chiller and ice tank is the key to realize the economical and energy-saving operation of ice-storage air-conditioning (ISAC) system. A multi-objective optimization model based on improved firefly algorithm (IFA) was established in this study to fully exploit the energy-saving potential and economic benefit of the ISAC system. The proposed model took the partial load rate of each chiller and the cooling ratio of the ice tank as optimization variables, and the lowest energy consumption loss rate and the lowest operating cost of the ISAC system were calculated. Chaotic logic self-mapping was used to initialize population to avoid falling into local optimum, and Cauchy mutation was used to increase the population’s diversity to improve the algorithm’s global search ability. The experimental results show that compared with the operation strategy based on constant proportion, particle swarm optimization (PSO) algorithm, and firefly algorithm (FA), the optimal operation strategy based on IFA can achieve more significant energy-saving and economic benefits. Meanwhile, the convergence accuracy and stability of the algorithm are significantly improved. Practical application: The optimized operation strategy of the ice-storage air-conditioning system can reduce energy loss and operating costs. The traditional operation strategies have the problems of low optimization precision and poor optimization effect. Therefore, this study presents an optimal operation strategy based on IFA. The convergence accuracy and stability of the algorithm are increased after the algorithm is improved. The operation strategy can get the maximum energy-saving effect and economic benefit of the ISAC system.

show abstract

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

Zhou

Zhang

et al. 2021

Building Services Engineering Research and Technology

View full text Add to dashboard Cite

show abstract

“…En [5] se presenta un modelo para optimización de sistemas de aire acondicionado con termoacumulación y los resultados de la simulación del sistema. Considerando que, la creciente demanda de sistemas de aire acondicionado ha llevado a un mayor consumo de energía durante las horas pico [6], los sistemas de acumulación térmica son cada vez más populares [7], permitiendo el desarrollo de estudios que abarcan aspectos físicos, técnicos, económicos, ambientales [8,9] y de consumo de materia prima [10] de los sistemas de almacenamiento de energía térmica de refrigeración y sus aplicaciones [2], con el fin de encontrar un método que mejore su eficiencia [11].…”

Section: Introductionunclassified

Estudio paramétrico para optimización de un generador de hielo tubular de laboratorio

Guapulema

Hidalgo²

2019

ings

View full text Add to dashboard Cite

En Ecuador existe un gran consumo energético por climatización y refrigeración en los sectores industrial, comercial y residencial. Un método para reducir la demanda eléctrica máxima es incorporar un sistema óptimo de almacenamiento de energía térmica de refrigeración. Este trabajo tiene por objetivo realizar el estudio paramétrico de un generador de hielo tubular de laboratorio para su posterior optimización. Se estudiaron los principales parámetros que intervienen en la formación de hielo como temperatura del agua en el reservorio, temperaturas del refrigerante en el evaporador y condensador, temperatura de subenfriamiento del hielo y velocidad de formación de hielo. Los parámetros destacados que intervinieron en el proceso fueron las condiciones ambientales del lugar y la temperatura del agua utilizada. Al disminuir la temperatura ambiental, disminuye la carga térmica y mejora la eficiencia del condensador, el cual influye directamente sobre la eficiencia del equipo. La inestabilidad observada en la primera hora de prueba intervino en la temperatura final del agua, la misma que varió en el rango de 1,1 °C a –0,4 °C en 3 horas.

show abstract

“…Generally, in multi‐objective optimization problems, a numerous set of answers called Pareto frontier is examined, to represent the closest match in the objective function area. On this subject, researchers have evaluated multi‐objective optimization for different energy systems in order to determine the best optimal operation conditions of the system …”

Section: Introductionmentioning

confidence: 99%

Multi‐objective performance optimization of irreversible molten carbonate fuel cell–Stirling heat engine–reverse osmosis and thermodynamic assessment with ecological objective approach

Ahmadi

Sameti

Pourkiaei

et al. 2018

Energy Science & Engineering

View full text Add to dashboard Cite

This paper aims to investigate a hybrid cycle consisting of a molten carbonate fuel cell (FC) and a Stirling engine which, by connecting to a seawater reverse osmosis desalination unit, provides fresh water. First, a parametric evaluation is performed to study the effect of some key parameters, including the current density and the working temperature of the FC and the thermal conductance between the working substance and the heat reservoirs in the Stirling engine, on the objective functions. The objective functions include the energy efficiency, the exergy destruction rate density, the fresh water production rate, and the ecological function density. After investigating each double combination of these objective functions, two scenarios are defined in quest to concurrently optimize three functions together. The first scenario aims to optimize the energy efficiency, the exergy destruction rate density, and the fresh water production rate; and the second scenario attempts to optimize the energy efficiency, the fresh water production rate, and the ecological function density. A multi‐objective evolutionary algorithm joined with the nondominated sorting genetic algorithm (NSGA‐II) approach is employed to obtain Pareto fronts in each case scenario. In order to ascertain final solutions between Pareto fronts, three fast and robust decision‐making methods are employed including TOPSIS, LINMAP, and Fuzzy. Finally, a sensitivity analysis is conducted to critically analyze the performance of the system.

show abstract

Modeling and optimization of R-717 and R-134a ice thermal energy storage air conditioning systems using NSGA-II and MOPSO algorithms

Cited by 40 publications

References 21 publications

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

Estudio paramétrico para optimización de un generador de hielo tubular de laboratorio

Multi‐objective performance optimization of irreversible molten carbonate fuel cell–Stirling heat engine–reverse osmosis and thermodynamic assessment with ecological objective approach

Contact Info

Product

Resources

About