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Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory.

Mohammed A A Al-Qaness1, Ahmed A Ewees2,3, Hung Vo Thanh4,5

  • 1College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China. alqaness@zjnu.edu.cn.

Environmental Science and Pollution Research International
|December 10, 2022
PubMed
Summary

This study introduces AOSMA, a hybrid algorithm optimizing Long Short-Term Memory (LSTM) networks for predicting carbon dioxide (CO2) storage efficiency. Accurate predictions can accelerate the adoption of underground carbon storage (UCS) technologies.

Keywords:
Air pollutionAquila optimizerCarbon dioxide (CO2)LSTMSlime mould algorithmSustainable environment

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Area of Science:

  • Environmental Science and Engineering
  • Artificial Intelligence in Environmental Monitoring
  • Computational Geoscience

Background:

  • Global environmental goals necessitate reducing fossil fuel use and greenhouse gas emissions due to climate change and air pollution.
  • Underground carbon storage (UCS) is a key technology for mitigating carbon dioxide (CO2) emissions, but its widespread adoption is hindered by feasibility assessment challenges.
  • Accurate prediction of CO2 storage efficiencies is crucial for promoting UCS project development and investment.

Purpose of the Study:

  • To develop an advanced computational tool for accurately predicting CO2 storage efficiencies in geological formations.
  • To enhance the predictive capabilities of Long Short-Term Memory (LSTM) networks through novel optimization techniques.
  • To evaluate the performance of a new hybrid swarm intelligence algorithm for optimizing machine learning models in environmental applications.

Main Methods:

  • Development of a hybrid swarm intelligence algorithm, AOSMA, by integrating the Aquila Optimizer (AO) with the Slime Mould Algorithm (SMA).
  • Utilizing the AOSMA algorithm to optimize the parameters of a Long Short-Term Memory (LSTM) neural network.
  • Assessing the prediction accuracy of the optimized LSTM model for two key CO2 trapping indices: residual trapping index (RTI) and solubility trapping index (STI).

Main Results:

  • The AOSMA algorithm demonstrated superior performance in optimizing LSTM parameters compared to standalone AO, SMA, and other swarm intelligence algorithms.
  • The hybrid AOSMA-LSTM model achieved significant improvements in predicting CO2 storage efficiencies (RTI and STI).
  • The developed model shows potential for rapid and accurate storage efficiency assessment for similar geological projects within the database range.

Conclusions:

  • The AOSMA-LSTM model offers a powerful and accurate approach for predicting CO2 storage efficiencies, addressing a critical barrier to UCS implementation.
  • This advancement in swarm intelligence and machine learning can significantly contribute to the feasibility assessment and deployment of underground carbon storage projects.
  • The developed smart tool has the potential to be a game-changer in accelerating the transition towards a sustainable environment by facilitating carbon emission reduction strategies.