Browsing by Author "Amar, Menad Nait"

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Intelligent prediction of rock mass deformation modulus through three optimized cascaded forward neural network models
(Springer, 2022) Hasanipanah, Mahdi; Jamei, Mehdi; Mohammed, Ahmed Salih; Amar, Menad Nait; Ouaer, Hocine; Khedher, Khaled Mohamed
Rock mass deformation modulus (Em) is a key parameter that is needed to be determined when designing surface or underground rock engineering constructions. It is not easy to determine the deformability level of jointed rock mass at the laboratory; thus, researchers have suggested different in-situ test methods. Today, they are the best methods; though, they have their own problems: they are too costly and time-consuming. Addressing such difficulties, the present study offers three advanced and efficient machine-learning methods for the prediction of Em. The proposed models were based on three optimized cascaded forward neural network (CFNN) using the Levenberg–Marquardt algorithm (LMA), Bayesian regularization (BR), and scaled conjugate gradient (SCG). The performance of the proposed models was evaluated through statistical criteria including coefficient of determination (R2) and root mean square error (RMSE). The computational results showed that the developed CFNN-LMA model produced better results than other CFNN-SCG and CFNN-BR models in predicting the Em. In this regard, the R2 and RMSE values obtained from CFNN-LMA, CFNN-SCG, and CFNN-BR models were equal to (0.984 and 1.927), (0.945 and 2.717), and (0.904 and 3.635), respectively. In addition, a sensitivity analysis was performed through the relevancy factor and according to its results, the uniaxial compressive strength (UCS) was the most impacting parameters on Em
Predicting the viscosity of hydrogen – methane blends at high pressure for hydrogen transportation and geo-storage: Integration of robust white-box machine learning frameworks
(Elsevier, 2025) Alatefi, Saad; Youcefi, Mohamed Riad; Amar, Menad Nait; Djema, Hakim
The integration of hydrogen into underground storage systems is pivotal for large-scale energy management, often involving blends with methane to leverage existing infrastructure. Accurate viscosity prediction of hydrogen – methane blends under subsurface conditions is essential for optimizing flow assurance and operational safety. Accordingly, this study employs three data-driven models, namely Genetic Expression Programming (GEP), Group Method of Data Handling (GMDH), and Multi-Gene Genetic Programming (MGGP), to predict the viscosity of hydrogen – methane mixtures for transportation and underground storage applications. A comprehensive dataset of 313 experimentally measured values from the literature were utilized to develop and validate the established correlations. The MGGP paradigm emerged as the top performer, achieving a root mean square error (RMSE) of 0.4054 and an R2 value of 0.9940, outperforming both GEP and GMDH, as well as prior predictive models. The consistency of the dataset was confirmed using the Leverage approach, ensuring robust predictions. In addition, the Shapley Additive Explanations technique revealed key factors influencing the viscosity predictions, enhancing the interpretability of the best-performing correlation. Furthermore, comparative trend analysis demonstrated the MGGP correlation's superior accuracy and robustness across varying blend compositions and operational conditions. These findings offer a reliable and simple-to-use predictive correlation for engineers and researchers designing hydrogen transport and storage systems, supporting efficient energy storage and the transition to a low-carbon economy
Predicting viscosity of CO2–CH4 binary mixtures using robust white-box machine learning frameworks: implication for carbon capture, utilization, and storage
(Springer Science and Business Media, 2025) Alatefi, Saad; Youcefi, Mohamed Riad; Amar, Menad Nait; Djema, Hakim
Carbon capture, utilization, and storage (CCUS) technologies, particularly those involving pure and impure carbon dioxide (CO2) injection for enhanced oil recovery (EOR), are vital for mitigating greenhouse gas emissions while optimizing energy production. The viscosity of carbon dioxide-methane (CO2–CH4) binary systems plays a critical role in determining flow behavior, injectivity, and storage efficiency in subsurface formations. However, direct experimental measurements of viscosity are often costly, time-consuming, and constrained by operational limitations. Furthermore, existing predictive correlations frequently exhibit limited accuracy across wide ranges of pressure, temperature, and composition, hindering their application in practical CCUS and EOR scenarios. This study introduces a white-box machine learning framework based on multi-gene genetic programming (MGGP) to predict the viscosity of CO2–CH4 mixtures with enhanced precision. A comprehensive dataset comprising 742 experimental measurements was utilized to construct explicit mathematical correlations as functions of pressure, temperature, and CO2 mole fraction. Extensive statistical analyses and graphical validations confirmed the high fidelity of the developed models. The MGGP-based schemes achieved a low total RMSE of 2.6343 and an excellent R2 of 0.9942, outperforming four previously established models. Trend analyses and Shapley additive explanations (SHAP) further reinforced the model’s reliability, highlighting the dominant influence of pressure, followed by CO2 mole fraction and temperature, on viscosity behavior. The proposed explicit and user-friendly correlations, combining accuracy with interpretability, provide valuable tools for industrial applications, particularly in the simulation, design, and optimization of CCUS and CO2-EOR projects under a wide range of operating conditions.