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Measuring Dissolved Methane in Aquatic Ecosystems Using An Optical Spectroscopy Gas Analyzer
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Machine-learning based prediction of hydrogen/methane mixture solubility in brine.

Farag M A Altalbawy1, Mustafa Jassim Al-Saray2, Krunal Vaghela3

  • 1Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia. f_altalbawy@yahoo.com.

Scientific Reports
|December 5, 2024
PubMed
Summary

Predicting hydrogen and methane mixture solubility in brine is crucial for underground hydrogen storage. Machine learning models, specifically LSSVM-GA and LSSVM-CSA, accurately predict solubility, advancing safe and affordable hydrogen storage.

Keywords:
Data-driven modelsMachine learningOutlier detectionRelevancy factor

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Area of Science:

  • Geochemistry
  • Chemical Engineering
  • Data Science

Background:

  • Underground hydrogen storage (UHS) often involves pre-existing methane reservoirs, leading to hydrogen/methane mixtures.
  • Accurate solubility data for these mixtures in brine is essential for safe and efficient UHS operations.
  • Laboratory measurements are challenging due to the corrosive and flammable nature of the gases.

Purpose of the Study:

  • To develop accurate data-driven intelligent models for predicting hydrogen/methane mixture solubility in brine.
  • To utilize hybrid machine learning approaches optimized with metaheuristic algorithms.
  • To investigate the influence of pressure, temperature, mixture composition, and brine salinity on solubility.

Main Methods:

  • Development of hybrid models combining Adaptive Neuro-Fuzzy Inference System (ANFIS) and Least Squares Support Vector Machine (LSSVM).
  • Optimization of LSSVM models using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Coupled Simulated Annealing (CSA).
  • Validation using laboratory data and evaluation of model performance through statistical metrics (AARE%, MSE, R-squared).

Main Results:

  • The developed models demonstrated high accuracy in predicting hydrogen/methane mixture solubility.
  • Sensitivity analysis revealed pressure and hydrogen mole fraction as key influencing factors.
  • LSSVM-GA and LSSVM-CSA models exhibited superior performance with the lowest AARE% and MSE, and highest R-squared values.

Conclusions:

  • Machine learning, particularly LSSVM-GA and LSSVM-CSA, provides a reliable and efficient method for predicting hydrogen solubility in brine for UHS.
  • These models can support the development of intelligent, cost-effective, and secure underground hydrogen storage technologies.
  • The findings contribute to de-risking hydrogen storage operations and advancing the hydrogen economy.