应用机器学习预测纯成分与水和抑制剂溶液的水合物形成

IF 1.6 4区 工程技术 Q3 ENGINEERING, CHEMICAL
Qazi Nasir, Humbul Suleman, Wameath S. Abdul Majeed
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引用次数: 0

摘要

本研究采用机器学习方法预测天然气水合物系统中的水合物形成压力(HFP)。在由 3137 个实验数据点组成的大型数据集上训练和评估了先进的机器学习模型,包括决策树回归器 (DTR)、随机森林回归器 (RFR)、极梯度提升 (XGB)、梯度提升回归器 (GBR)、直方图梯度提升回归器 (HGBR) 和 CatBoost 回归器 (CB)。使用 R 平方 (R2)、均方误差 (MSE)、平均绝对误差 (MAE) 和均方根误差 (RMSE) 对模型进行了评估。研究表明,就 HFP 预测目的而言,CatBoost 的表现优于所有其他机器学习模型。在测试集上,CatBoost 表现出很高的准确性,R2 值为 0.9922,RMSE(1.61 × 10-3)、MAE(7.90 × 10-4)和 MSE(2.58 × 10-6)均为最低,增强了其预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

The present work investigates the use of machine learning approaches for the prediction of hydrate formation pressure (HFP) in gas hydrate systems. Advanced machine learning models, including the decision tree regressor (DTR), random forest regressor (RFR), extreme gradient boosting (XGB), gradient boosting regressor (GBR), histogram gradient boosting regressor (HGBR), and CatBoost regressor (CB), are trained and evaluated on a large dataset consists of 3137 experimental data points. The models are evaluated using R-squared (R2), mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The study indicates that for the intent of HFP prediction, CatBoost outperformed all other machine learning models. It demonstrated high accuracy on the testing set with an R2 value of 0.9922, and with the lowest RMSE (1.61 × 10−3), MAE (7.90 × 10−4), and MSE (2.58 × 10−6), CatBoost strengthened its prediction ability.

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来源期刊
Canadian Journal of Chemical Engineering
Canadian Journal of Chemical Engineering 工程技术-工程:化工
CiteScore
3.60
自引率
14.30%
发文量
448
审稿时长
3.2 months
期刊介绍: The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.
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