Viscosities of Liquid Refrigerants from a Rough Hard-Sphere Theory-Based Semi-Empirical Model

Yousefi, Fakhri; Hosseini, Sayed Mostafa; Hamidi, K.; Pierantozzi, Mariano

doi:10.1007/s10765-019-2541-1

Cited by 12 publications

(1 citation statement)

References 47 publications

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“…Te accuracy of thermophysical properties of materials is known as a main parameter in process design [2,5,6]. However, a large number of refrigerants' physical properties have been reported in the previous research studies [7][8][9]. Developing alternative desirable refrigerants is necessary because of increasing attention to the greenhouse efect and depletion of the ozone layer [10].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligent Approach to Predict the Normal Boiling Point of Refrigerants

Liu

Nouroddin

2023

International Journal of Chemical Engineering

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Due to the desirable and interesting applications of refrigerants in organic Rankine cycles, heat pumps, and refrigeration, engineers and researchers are becoming more interested in refrigerant properties. One of the most dominant thermophysical properties of these fluids is their normal boiling point (Tb). In the current study, a novel extreme learning method (ELM) and ensemble decision tree boosted algorithm (EDT Boosted) are proposed to forecast the normal boiling point from 16 different molecular groups and one topological index. To this end, a total of 334 data points of Tb are gathered to prepare and test ELM and EDT boosted algorithms. The visual and mathematical comparisons of model outputs and real Tb express that proposed models have great potential to predict Tb of refrigerant. Moreover, sensitivity analysis is applied to explain the effectiveness of input parameters on the determination of Tb for refrigerants.

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