利用线性回归、人工神经网络和基因表达编程对 R466A 的热力学特性进行比较研究

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL Journal of Thermal Analysis and Calorimetry Pub Date : 2024-09-06 DOI:10.1007/s10973-024-13509-6
Erkan Dikmen
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引用次数: 0

摘要

为了改善全球环境的宜居性,人们倾向于使用新一代制冷剂。在此背景下,使用各种方法对 R466A(一种低臭氧消耗潜能值和全球变暖潜能值的新一代制冷剂)的热力学特性进行了建模。线性回归、人工神经网络(ANN)和基因表达编程(GEP)模型被用来预测 R466A 在饱和液-气相中的温度-压力关系以及在过热气相中的焓-熵关系。根据确定系数 (R2)、平均绝对误差和均方根误差 (RMSE) 等统计参数对模型的性能进行了评估,并与实际值进行了比较。研究结果表明,GEP 模型在预测饱和气相的热力学性质时获得了最低的均方根误差值。另一方面,ANN 模型更适合估算过热气相中的属性。ANN 模型的 R2 值介于 0.999 和 0.986 之间,而 GEP 模型的 R2 值介于 0.999 和 0.982 之间。尽管 GEP 模型的性能略低于某些 ANN 模型,但它采用了显式方程。
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A comparative study of thermodynamic properties of R466A using linear regression, artificial neural network and gene expression programming

The use of next-generation refrigerant fluids is preferred to improve the global environment’s livability. In this context, the thermodynamic properties of R466A, a new-generation refrigerant with low ozone depletion potential and global warming potential, have been modelled using various methods. Linear regression, artificial neural network (ANN), and gene expression programming (GEP) models were used to predict R466A’s temperature–pressure relationship in the saturated liquid–vapor phase and its enthalpy-entropy relationship in the superheated vapor phase. The models’ performance was evaluated based on statistical parameters such as the determination coefficient (R2), mean absolute error, and root mean square error (RMSE), and compared with actual values. The research results indicate that the GEP model achieved the lowest RMSE values for predicting thermodynamic properties in the saturated vapor phase. On the other hand, ANN models were found to be more suitable for estimating properties in the superheated vapor phase. The R2 values for ANN models ranged from 0.999 to 0.986, whereas GEP models exhibited R2 values between 0.999 and 0.982. Despite slightly lower performance compared to some ANN models, GEP models employed explicit equations.

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来源期刊
CiteScore
8.50
自引率
9.10%
发文量
577
审稿时长
3.8 months
期刊介绍: Journal of Thermal Analysis and Calorimetry is a fully peer reviewed journal publishing high quality papers covering all aspects of thermal analysis, calorimetry, and experimental thermodynamics. The journal publishes regular and special issues in twelve issues every year. The following types of papers are published: Original Research Papers, Short Communications, Reviews, Modern Instruments, Events and Book reviews. The subjects covered are: thermogravimetry, derivative thermogravimetry, differential thermal analysis, thermodilatometry, differential scanning calorimetry of all types, non-scanning calorimetry of all types, thermometry, evolved gas analysis, thermomechanical analysis, emanation thermal analysis, thermal conductivity, multiple techniques, and miscellaneous thermal methods (including the combination of the thermal method with various instrumental techniques), theory and instrumentation for thermal analysis and calorimetry.
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