A hybrid OBL-NSGA-III/MOTLBO optimization approach for sustainable design of earth-air heat exchangers in building construction

Abstract

This paper presents a novel hybrid optimization method combining the opposition-based learning non-dominated sorting genetic algorithm III (OBL-NSGA-III) and the multi-objective teaching–learning-based optimization (MOTLBO) method to optimize sustainable design of earth-air heat exchangers (EAHE) in construction. EAHE systems utilize stable underground temperatures to precondition air, offering an energy-efficient alternative to conventional HVAC systems. The hybrid methodology addresses multi-objective challenges, including minimizing lifecycle costs, carbon emissions, installation time, and pressure drop, while maximizing energy performance. The hybrid OBL-NSGA-III/MOTLBO approach integrates the global search capability of OBL-NSGA-III with the refinement potential of MOTLBO, ensuring enhanced convergence and solution diversity. The effectiveness of the model is demonstrated by a case study on a 150 m² residential building in Mathura, Uttar Pradesh, which achieved considerable reductions in carbon emissions and pressure drop, a 30% improvement in energy performance, and a 20% decrease in lifecycle costs when compared to existing systems. Sensitivity and trade-off analyses provide decision-makers with useful information by highlighting the interactions between design parameters like pipe length, diameter, and depth. This study promotes economical, energy-efficient, and ecologically conscious building methods, thereby establishing a reproducible foundation for optimising sustainable HVAC solutions and supporting global sustainability goals.