基于密度和神经网络建模预测有机化合物在超临界二氧化碳中溶解度的对比分析

IF 3.4 3区 工程技术 Q2 CHEMISTRY, PHYSICAL
Mohammad Mahdi Barzegar, Feridun Esmaeilzadeh, Ali Zandifar
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引用次数: 0

摘要

本研究探讨了超临界二氧化碳(SC-CO)中溶质溶解度的估算方法,其压力和温度范围分别为 80 巴至 490.29 巴和 308 K 至 423 K。我们提出了一个新的经验模型,该模型建立了相关参数与目标溶解度之间的相关性。特征重要性算法促进了这一经验模型的开发。利用 40 个已发表的实验数据集对该模型的准确性进行了全面评估,平均绝对相对偏差 (AARD) 为 9.9%。与之前建立的 12 个模型相比,该模型表现出更优越的性能。此外,还开发了一种微调人工神经网络(ANN),以利用机器学习技术的独特能力。人工神经网络的性能优于所提出的模型,AARD% 明显降低到 4.38。这一结果凸显了机器学习技术,特别是人工神经网络在实现更高精度方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A comparative analysis of density-based and neural network modeling for predicting the solubility of organic compounds in supercritical carbon dioxide

A comparative analysis of density-based and neural network modeling for predicting the solubility of organic compounds in supercritical carbon dioxide

This study investigates the estimation of solute solubility in supercritical carbon dioxide (SC-CO2) within a pressure and temperature range of 80 bar to 490.29 bar and 308 K to 423 K. We propose a novel empirical model that establishes a correlation between relevant parameters and the targeted solubility. A feature importance algorithm facilitated the development of this empirical model. The model’s accuracy is comprehensively evaluated using 40 published experimental datasets, with an average absolute relative deviation (AARD) of 9.9 %. It demonstrates superior performance compared to 12 previously established models. Furthermore, a fine-tuned artificial neural network (ANN) is developed to harness the unique capabilities of machine learning techniques. The ANN outperforms the proposed model, achieving a significantly lower AARD% of 4.38. This outcome emphasizes the potential of machine learning techniques, particularly ANNs, for achieving superior accuracy.

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来源期刊
Journal of Supercritical Fluids
Journal of Supercritical Fluids 工程技术-工程:化工
CiteScore
7.60
自引率
10.30%
发文量
236
审稿时长
56 days
期刊介绍: The Journal of Supercritical Fluids is an international journal devoted to the fundamental and applied aspects of supercritical fluids and processes. Its aim is to provide a focused platform for academic and industrial researchers to report their findings and to have ready access to the advances in this rapidly growing field. Its coverage is multidisciplinary and includes both basic and applied topics. Thermodynamics and phase equilibria, reaction kinetics and rate processes, thermal and transport properties, and all topics related to processing such as separations (extraction, fractionation, purification, chromatography) nucleation and impregnation are within the scope. Accounts of specific engineering applications such as those encountered in food, fuel, natural products, minerals, pharmaceuticals and polymer industries are included. Topics related to high pressure equipment design, analytical techniques, sensors, and process control methodologies are also within the scope of the journal.
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