Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{NOUSHABADI:2023:energy,
-
author = "Abolfazl Sajadi Noushabadi and Ebrahim Nemati Lay and
Amir Dashti and Amir H. Mohammadi and
Abdoulmohammad Gholamzadeh Chofreh and Feybi Ariani Goni and
Jiri Jaromir Klemes",
-
title = "Insights into modelling and evaluation of
thermodynamic and transport properties of refrigerants
using machine-learning methods",
-
journal = "Energy",
-
volume = "262",
-
pages = "125099",
-
year = "2023",
-
ISSN = "0360-5442",
-
DOI = "doi:10.1016/j.energy.2022.125099",
-
URL = "https://www.sciencedirect.com/science/article/pii/S0360544222019946",
-
keywords = "genetic algorithms, genetic programming, Refrigerants,
Thermodynamic properties, Transport properties, Machine
learning, Correlation",
-
abstract = "The thermophysical properties of refrigerating systems
should be accurately understood for designing
low-temperature refrigeration cycles of economic
acceptance. The present work has tried to simplify this
complicated procedure by proposing reliable and new
correlative methods for determining thermodynamic and
transport properties of four refrigerating substance
classes, namely halocarbon, inorganic, hydrocarbon, and
cryogenic fluids. New machine learning methods e.g.,
particle swarm optimisation adaptive neuro-fuzzy
inference system (PSO-ANFIS), genetic programming (GP),
and hybrid adaptive neuro-fuzzy inference system
(Hybrid ANFIS) algorithms were used. The development of
a new, simple and comprehensive correlation was for the
first time introduced to estimate saturated vapour
enthalpy, entropy, velocity of sound, and viscosity of
refrigerants without having in-depth knowledge of
complicated parameters. The accuracy and validity of
the proposed models were assessed using a variety of
statistical and graphical demonstrations. The findings
were compared, and it was found that Hybrid ANFIS
models are more accurate because Absolute Average
Relative Errors (percentAARD) for enthalpy, entropy,
the velocity of sound, and viscosity were estimated as
0.5558, 1.3105, 0.5215, and 1.5727 in respective order.
In addition, the proposed models' results were compared
to the results of recently previously published models,
and it confirms the reliability of our results. The
innovation of this research is the design of reliable
correlative methods having elevated precisions for
thermodynamic and transport specifications of
refrigerating substances",
- }
Genetic Programming entries for
Abolfazl Sajadi Noushabadi
Ebrahim Nemati Lay
Amir Dashti
Amir H Mohammadi
Abdoulmohammad Gholamzadeh Chofreh
Feybi Ariani Goni
Jiri Jaromir Klemes
Citations