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Integration of Multiple Optimization Algorithms with Machine Learning: Predicting Nitrogen Adsorption Volume of Coal

Junjie Cai1,2,3, Xijian Li1,2,3, Shoukun Chen3

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|March 24, 2026
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Summary
This summary is machine-generated.

Predicting nitrogen adsorption capacity in coal rocks is crucial for carbon sequestration and methane development. Machine learning models, optimized with algorithms like Northern Goshawk Optimization (NGO), significantly improve prediction accuracy, with coal particle size being a key factor.

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Area of Science:

  • Interface Science: Focuses on solid-gas interactions and porous material characterization.
  • Materials Science: Investigates the physical and chemical properties of coal rocks.
  • Computational Science: Utilizes machine learning for predictive modeling.

Background:

  • Nitrogen adsorption capacity is vital for characterizing porous materials, carbon sequestration, and coalbed methane development.
  • Accurate prediction of adsorption volume is challenging due to complex, nonlinear relationships between coal properties and adsorption behavior.
  • Understanding solid-gas interface interactions is key in interface science.

Purpose of the Study:

  • To systematically analyze pore structure characteristics of coal rocks with varying particle sizes using low-temperature liquid nitrogen adsorption data.
  • To develop and compare machine learning models (Random Forest and Support Vector Machine) optimized with algorithms for predicting nitrogen adsorption volume.
  • To identify key factors influencing nitrogen adsorption in coal rocks, particularly particle size.

Main Methods:

  • Low-temperature liquid nitrogen adsorption experiments to analyze pore structure.
  • Application of Random Forest (RF) and Support Vector Machine (SVM) models.
  • Integration of four optimization algorithms: Sparrow Search Algorithm (SSA), Snake Optimization (SO), Chameleon Swarm Algorithm (CSA), and Northern Goshawk Optimization (NGO).

Main Results:

  • Optimized models, especially RF enhanced by NGO and CSA, significantly outperformed other approaches.
  • The RF model demonstrated superior prediction accuracy over SVM, achieving an R² of 0.9804 with NGO/CSA optimization.
  • Coal rock particle size (120-200 mesh) was identified as a critical predictive factor, improving validation set performance by nearly 10%.

Conclusions:

  • Integrating optimization algorithms with machine learning models provides an effective quantitative method for predicting nitrogen adsorption capacity in coal rocks.
  • Coal particle size is a significant factor influencing nitrogen adsorption volume.
  • The study validates the effectiveness of advanced computational approaches for understanding solid-gas interface interactions.