Multi-objective airflow distribution optimization for mine ventilation networks using a hybrid intelligent optimization algorithm

Abstract High ventilation energy demand, complex airflow regulation, and the sensitivity of airflow distribution to resistance disturbances remain major challenges in mine ventilation networks. To address these issues, this paper develops an airflow distribution planning framework that couples multi-objective optimization with decision-making. The framework simultaneously considers ventilation energy consumption, the number of regulators, and ventilation network stability, and formulates an integrated multi-objective optimization model. To improve solution efficiency, a hybrid multi-objective intelligent optimization algorithm, SPEA/R-IWO, which integrates the Strength Pareto Evolutionary Algorithm based on Reference Direction (SPEA/R) with Invasive Weed Optimization (IWO), is proposed. The Pareto-optimal candidates produced by SPEA/R-IWO are further evaluated using the entropy-weighted TOPSIS method to determine the preferred airflow control scheme. A case study demonstrates that the proposed framework can effectively reduce energy consumption and control-related costs while improving the stability of the ventilation network.