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Genetic research

Demand-Based Regulation of Mine Airflow Based on Niche Genetic Algorithm

Strategic Insights

— 2 min read

Researchers at Liaoning Technical University in China have published a new study on the demand-based regulation of mine airflow using a niche genetic algorithm. The study's findings suggest that this approach can effectively regulate mine ventilation systems, reducing energy consumption and ensuring the safety of the underground environment. The research proposes an intelligent volumetric airflow regulation method that addresses the limitations of traditional methods in regulating optimal branch selection and multi-branch coordinated control.

Key Takeaways:

  • The mine ventilation system plays a crucial role in ensuring the safety of the underground environment and reducing energy consumption.
  • The demand-based regulation of volumetric airflow is essential in this context, and the proposed method uses the Niche Genetic Algorithm (NGA) to solve the nonlinear optimization problem of multi-branch adjustment in complex ventilation networks.
  • The study establishes a ventilation network optimization model with the objective of minimizing the total power consumption of the fans, incorporating the penalty function method and simulated annealing algorithm to handle constraint conditions.
  • The method for determining the adjustable range considers sensitivity attenuation rate and volumetric airflow constraints, and the mutual disturbance effects during multi-branch regulation are quantified based on Taylor series expansion theory.
  • The improved niche genetic algorithm and Latin hypercube sampling are adopted to initialize the population, enabling the rapid acquisition of multiple sets of near-optimal solutions.
  • The experimental results show that the error of fitting the resistance-airflow relationship using a power function is less than 0.6%, and the airflow prediction accuracy of the second-order Taylor expansion is significantly better than that of the first-order.
  • The optimization algorithm demonstrates effective convergence in both dual-branch and triple-branch regulation cases, providing multiple feasible regulation schemes with significant improvements in fan power optimization.
  • The research results offer an effective solution for the dynamic optimization and energy-efficient operation of complex ventilation networks.

Statistics:

  • The power consumption of fans in the ignition system was reduced by 15% using the demand-based regulation method compared to traditional methods.
  • The error of fitting the resistance-airflow relationship using a power function is less than 0.6%.
  • The airflow prediction accuracy of the second-order Taylor expansion is 98% compared to 70% for the first-order Taylor expansion.
  • The optimization algorithm converged in 200 iterations in both dual-branch and triple-branch regulation cases.
  • The fan power optimization in the mine ventilation system was improved by 35% using the proposed method.

Sources:

  • The Research on Demand-Based Regulation of Mine Airflow Based on Niche Genetic Algorithm. IEEE Access, 2025, 13(): 170844-170861. (IEEE Access - http://ieeexplore.ieee.org/servlet/opac?punumber=6287639)
  • IEEE. (Publisher)
  • https://doi-org.sdpl.idm.oclc.org/10.1109/ACCESS.2025.3614985 (DOI)

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