Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Mohammadi, Mohammad-Reza; Hadavimoghaddam, Fahime; Pourmahdi, Maryam; Atashrouz, Saeid; Munir, Muhammad Tajammal; Hemmati-Sarapardeh, Abdolhossein; Mosavi, Amir; Mohaddespour, Ahmad

doi:10.1038/s41598-021-97131-8

Cited by 69 publications

(28 citation statements)

References 67 publications

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“…In other words, it repeatedly chooses the training inputs in order to complement several classifiers and apply the proper weight for every classifier depending on its performance, with larger weights allocated to miscategorized data sets. The following are the common parts of the AdaBoost procedure 71 :…”

Section: Model Developmentmentioning

confidence: 99%

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Mohammadi

Hadavimoghaddam

Atashrouz

et al. 2022

Sci Rep

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Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

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Section: Model Developmentmentioning

confidence: 99%

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Mohammadi

Hadavimoghaddam

Atashrouz

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…Moreover, the histogram approach does not necessitate extra archiving of pre-sorted findings and results can be saved in an 8-bit integer upon feature determination, which reduces storage use to 1/8 of what it was before. In spite of this, the model's efficiency suffers because of the strict dividing mechanism (Mohammadi et al, 2021).…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action.Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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“…The results demonstrate that the XGBoost travel time prediction model considerably enhances the performance and efficiency. XGBoost algorithm is also utilized to develop models for different domains such as estimating the hydrogen solubility in hydrocarbons [40] and predicting flooding susceptibility [41].…”

Section: Related Work: Comparative Analysis Of Travel Time Prediction...mentioning

confidence: 99%

“…Compared with XGBOOST, LightGBM is computationally less expensive and has better prediction accuracy [44]. We used the standard hyper-parameters of the two learning algorithms [40,41,45]: learning_rate, colsample_bytree, n_estimators, and max_depth for XGBoost, and learning_rate, bagging_frequency, n_estimators, and max_dept for LightGBM.…”

Section: Model Training and Tuningmentioning

confidence: 99%

Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

et al. 2021

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Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.

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Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Cited by 69 publications

References 67 publications

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

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