Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

Davoodi, Shadfar; Thanh, Hung Vo; Wood, David A.; Mehrad, Mohammad; Al-Shargabi, Mohammed; Rukavishnikov, Valeriy S.

doi:10.1016/j.seppur.2023.123807

Cited by 33 publications

(4 citation statements)

References 119 publications

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“…Generally, in classification problems with nonlinearly separable data, kernel functions are used to transform the data into a higher-dimensional feature space, enabling linear separation. In regression scenarios, kernelization is applied for nonlinear SVR [ 51 , 52 , 53 , 54 ]. Figure 3 shows the execution of an SVM classifier on a dataset containing two classes and two features (linear SVR).…”

Section: Methodsmentioning

confidence: 99%

A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time

Al-Rubaii,

Al-Shargabi,

Aldahlawi

et al. 2023

Sensors

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When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECDeffc.m and MWeffc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software.

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Section: Methodsmentioning

confidence: 99%

A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time

Al-Rubaii,

Al-Shargabi,

Aldahlawi

et al. 2023

Sensors

Self Cite

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“…Hu et al (2021) [ 24 ] explored strategies for cultivating diverse talents in the field of carbon finance. Davoodi et al (2023) [ 25 ] presented a novel hybrid forecasting framework to address the challenges in carbon price prediction, incorporating diverse influencing factors through advanced algorithms. The study introduced a kernel-based optimal extreme learning machine model, which efficiently combines the multi-objective chaotic sine-cosine algorithm optimizer, demonstrating outstanding generalization and stability.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of China’s carbon market price fluctuation and international carbon credit financing mechanism using random forest model

Song

2024

PLoS ONE

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This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model’s Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

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“…Davoodi et al [25] extend the application of ML to optimize H 2 storage systems, displaying the efficacy of least squares support vector machines (LSSVM) in predicting hydrogen uptake by porous carbon media. Meanwhile, Shi et al [26] focus on developing advanced sensing technologies for hydrogen leakage detection, leveraging ML models to estimate H 2 detection response for various nanocomposites.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Artificial Intelligence-Driven Innovations in Hydrogen Safety

Patil,

Calay,

Mustafa

et al. 2024

Hydrogen

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This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems.

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Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

Cited by 33 publications

References 119 publications

A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time

A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time

Analysis of China’s carbon market price fluctuation and international carbon credit financing mechanism using random forest model

Artificial Intelligence-Driven Innovations in Hydrogen Safety

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