Production of H2 via sorption enhanced auto-thermal reforming for small scale Applications-A process modeling and machine learning study

Gul, Hania; Arshad, Muhammad Yousaf; Tahir, Muhammad Wasim

doi:10.1016/j.ijhydene.2022.12.217

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…To enhance the computation time, sparingly soluble complexes are referred to as soluble and denoted by 1's. Python Anaconda version 3.7 is used as an interpreter [45,46]. Seaborne, Pandas, Scikit, and Matplotlib libraries of Python are used extensively in ML algorithm implementation [46].…”

Section: K-nearest Neighbor (K-nn) Machine Learning Validation For Th...mentioning

confidence: 99%

Metal(II) triazole complexes: Synthesis, biological evaluation, and analytical characterization using machine learning-based validation

et al. 2023

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The synthesis of many transition metal complexes containing 3,5-diamino-1,2,4-triazole (Hdatrz) as a ligand with different counter anions Br⎺, Cl⎺, ClO4⎺ and SO42- has been studied extensively, but the chemistry of transition metal nitrate and acetate compounds and their reactivity are relatively unexplored. In this research work, two new series of metal(II) complexes (M = Ni, Co, and Zn) {[Ni3(Hdatrz)6(H2O)6](NO3)6 (1), [Co3(Hdatrz)6(H2O)6](NO3)6 (2), [Zn3(Hdatrz)6(H2O)6](NO3)6 (3), [Ni3(Hdatrz)6(H2O)6](OAc)6 (4), [Co3(Hdatrz)6(H2O)6] (OAc)6 (5) and [Zn3(Hdatrz)6(H2O)6](OAc)6 (6)} have been synthesized. These synthesized complexes were characterized by various physicochemical techniques such as UV-vis spectroscopy, Fourier transform infrared spectroscopy, and magnetic susceptibility measurements. All six complexes were found to be trinuclear and bridged through the Hdatrz ligand. Spectral data suggested a distorted octahedral environment around the central metal ions in these complexes. It also revealed that the NH and OH groups are involved in hydrogen bonding. These complexes were tested against the fungal strains Colletotrichum gloeosporioides and Aspergillus niger. These synthesized complexes have not been observed to have antifungal activities. The machine learning K-nearest neighbours evaluates the analytical characteristics and solubility behavior of the metal complexes. Machine learning models provide results with 75% precision.

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Section: K-nearest Neighbor (K-nn) Machine Learning Validation For Th...mentioning

confidence: 99%

Metal(II) triazole complexes: Synthesis, biological evaluation, and analytical characterization using machine learning-based validation

et al. 2023

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“…A cost function is a minimum for RSS and MSE values. The LR algorithm gives the cost function for optimal values of β o & β i for the best-fitting vector positioning [61].…”

Section: Mathematical Understanding Machine Learning Linear Regressio...mentioning

confidence: 99%

“…In order to enhance the accuracy of the ML prediction system, the ML linear regression (LR) model is evaluated using a set of key metrics. Favorable conditions for ML running the LR model include low variance and higher bias, as they contribute to improved prediction accuracy and faster computation, albeit with a greater number of assumptions [61,62]. One important metric used is the R 2 or determination of R 2 coefficient.…”

Section: Model Evaluation Metricsmentioning

confidence: 99%

Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor

Arshad,

Saeed,

Tahir

et al. 2023

Energies

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This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors.

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“…Greenhouse gas emissions have received significant attention globally . While providing 86% of the global energy supply, fossil fuel contributes around 75% of CO 2 emissions worldwide . Therefore, clean energy sources are promoted worldwide to reduce carbon emissions, including solar energy, , wind energy, , hydropower, , bioenergy, , and geothermal energy. , Nonetheless, these energy sources have seasonal and geographical dependencies comprising their reliability.…”

Section: Introductionmentioning

confidence: 99%

Integrating Capacity and Efficiency for Optimal Hydrogen Storage Site Selection in Saline Aquifers

Chen,

Mao

et al. 2024

Energy Fuels

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Hydrogen (H 2 ) energy is a promising transition pathway from conventional fossil fuels to sustainable clean energy. However, H 2 requires a large storage capacity because of its low volumetric energy−density nature. Underground H 2 storage sites provide ample space for H 2 storage. In this work, we proposed a general workflow to select saline aquifers' optimal H 2 storage sites, considering the capacity and operational efficiency. We developed a comprehensive data set of high-fidelity numerical simulations to quantify the effects of geologic and operating parameters on H 2 storage performance. The simulation results are used to train a robust reduced-order model (ROM) to estimate H 2 storage performance. Due to its high accuracy and flexibility, we selected the multilayer perceptron to develop our ROM. The mean squared errors of all ROMs are less than 0.0001, and the coefficients of determination (R 2 ) were higher than 0.99. Integrating the performance estimations from the ROMs with volumetric calculations of H 2 storage capacity, we quantitatively evaluated the H 2 storage in saline aquifers using a designed objective function. We applied our workflow in the Intermountain-West (I-WEST) region, which is the central mountain area in the United States, including Arizona, Colorado, New Mexico, Montana, Utah, and Wyoming. We identified the top three promising saline aquifers for H 2 storage from 12 potential storage sites. Our workflow and ROMs are agnostic to the region and could be applied to other areas. Generally, this work supports safe and efficient H 2 storage operations in saline aquifers in the I-WEST region.

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Production of H2 via sorption enhanced auto-thermal reforming for small scale Applications-A process modeling and machine learning study

Cited by 20 publications

References 32 publications

Metal(II) triazole complexes: Synthesis, biological evaluation, and analytical characterization using machine learning-based validation

Metal(II) triazole complexes: Synthesis, biological evaluation, and analytical characterization using machine learning-based validation

Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor

Integrating Capacity and Efficiency for Optimal Hydrogen Storage Site Selection in Saline Aquifers

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