Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack

Glasby, Lawson T.; Moghadam, Peyman Z.

doi:10.1016/j.patter.2021.100305

Cited by 5 publications

(2 citation statements)

References 12 publications

(11 reference statements)

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“…In 1956, John McCarthy coined the term “artificial intelligence” (AI), marking the inception of a computer science subfield centered on machine learning (ML) and an aspiration to emulate human intelligence . AI-driven algorithms have since permeated diverse disciplines, , with notable advancements in sectors such as image understanding, pattern recognition, autonomous driving, automatic programming, big data, , robotics, and human-machine collaboration . Despite their transformative potential, the inner mechanics of AI remain obscured.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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Wastewater treatment, especially the efficient degradation of contaminants such as m-cresol, remains a pivotal challenge. This study investigates the application of artificial neural networks (ANN) in predicting total organic carbon (TOC) removal rates from m-cresol-contaminated wastewater by using the ultraviolet (UV)-Fenton oxidation process. Six key variables, namely, Fe 2+ dosage, H 2 O 2 dosage, catalyst quantity, reaction time, pH, and substrate concentration, were employed as inputs to the ANN model. Leveraging this multivariable input and a comprehensive data set, the ANN model projected a maximum TOC removal rate of 87.12%, validated by an efficiency of 86.26% achieved through experiments under the derived optimal conditions: Fe 2+ dosage at 16.09 mg/L, H 2 O 2 dosage at 1.40 mg/L, catalyst quantity at 0.11 g/L, reaction time of 29.80 min, initial pH of 3.66, and substrate concentration of 50 mg/L. Comparative analysis with other machine learning algorithms further revealed that the ANN model notably outperformed linear regression, support vector regression, and random forest in terms of precision. This work paves the way for resource-optimized experimental designs, fostering real-time wastewater monitoring and refining advanced oxidation process proficiency in industrial applications.

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Section: Introductionmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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“…While MOFs have shown promising potential for hydrogen storage, several challenges need to be addressed for their practical use, including the need to optimize the pore size and surface area for hydrogen storage, the development of efficient regeneration methods to release the stored hydrogen, and the need to address the stability of MOFs under high-pressure hydrogen adsorption conditions. Nevertheless, MOFs represent a promising avenue for hydrogen storage, and ongoing research in this field is expected to further advance their development for practical applications[252,253].…”

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confidence: 99%