Prediction of refrigerant critical temperature with genetic neural network based on group topology

SU Wen; ZHAO Li; DENG Shuai

doi:10.11949/j.issn.0438-1157.20160289

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Prediction of refrigerant critical temperature with genetic neural network based on group topology

更新时间：2025-12-10

- Prediction of refrigerant critical temperature with genetic neural network based on group topology
- CIESC Journal Issue 11, Pages: 4689-4695(2016)
- 作者机构：
  
  天津大学中低温热能高效利用教育部重点实验室,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20160289
  CLC： K123
- Online First：05 November 2016，
  
  Published：05 November 2016
- 稿件说明：
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SU Wen, ZHAO Li, DENG Shuai. Prediction of refrigerant critical temperature with genetic neural network based on group topology[J]. CIESC Journal, 2016, (11): 4689-4695.
DOI：

SU Wen, ZHAO Li, DENG Shuai. Prediction of refrigerant critical temperature with genetic neural network based on group topology[J]. CIESC Journal, 2016, (11): 4689-4695. DOI： 10.11949/j.issn.0438-1157.20160289.

摘要

用遗传神经网络预测工质的临界温度，网络的输入参数为分子基团和拓扑指数，输出参数为临界温度。所划分的16个分子基团涵盖了制冷、热泵及有机朗肯循环系统中的大部分工质，所选拓扑指数能够分辨工质中所有的同分异构体。通过遗传算法优化得到网络结构及初始参数后，由神经网络对工质临界温度进行预测，同时为了提高网络对临界温度预测的泛化能力，将200种工质划分成训练集、验证集及测试集。所得网络能够区分所有的同分异构体，且与实验值相比，各数据集临界温度的平均相对误差分别为1.18%、1.69%、1.28%，表明该网络对工质临界温度具有很好的预测能力。

Abstract

A genetic neural network was presented to predict the critical temperature of refrigerants. The inputs of the network included molecular groups and a topological index

and the output was the critical temperature. 16 molecular groups divided can cover most of the refrigerants or working fluids in refrigeration

heat pump and Organic Rankine Cycle research. The chosen topological index was able to distinguish all refrigerant isomers. The critical temperatures of refrigerants were estimated by the neural network after obtaining the optimized network structure and initial parameters by genetic algorithm. At the same time

in order to improve network generalization ability of prediction

200 data points were divided into three data sets including the training

validation

and test sets. The calculated results based on the developed network showed a good agreement with experimental data. The network can distinguish all refrigerant isomers and compared with the experimental data. The average absolute relative deviations for training

validation and test sets were 1.18%

1.69% and 1.28%

respectively.

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