Multi-objective Optimization for Multi-Zone HVAC System Via Physics-Integrated Predictive Control Framework with CFD-assisted Visualization

HVAC systems need to collaboratively optimize energy consumption, air quality, and thermal comfort under dynamic occupancy to address building energy efficiency and indoor environmental quality. However, traditional control strategies often struggle to balance these objectives, especially in enclosed spaces with high occupancy density and sudden variations, such as classrooms, where CO₂ can rapidly exceed safety limits within a short period without effective ventilation. Therefore, this study proposes a physics-integrated hierarchical predictive control framework using reduced-order models calibrated with measured data and a personalized “center-edge-corner” zoning strategy for adaptive spatial load matching. A three-layer decision mechanism (emergency response, optimal control, energy-saving control) enables dynamic multi-zone regulation. Results show that the proposed framework achieves satisfactory thermal comfort, with average temperatures of 23.0 °C in the uniform scenario, 22.6 °C in the center-concentrated scenario, and 22.4 °C in the dynamic scenario. In terms of air quality, the proportion of CO₂ concentration exceeding 800 ppm is 0% in the uniform scenario. In the center-concentrated scenario, the proportion exceeding 1000 ppm is 1.4%, with a 13.2% energy reduction compared to the baseline strategy. In the dynamic scenario, the energy consumption of the proposed strategy is nearly identical to that of the fixed strategy, but the proportion of CO₂ concentration exceeding 800 ppm is reduced from 10.2% to 5.9%. CFD simulation results provide high-resolution quantitative visualizations and further confirm that the proposed framework effectively prevents local pollutant accumulation and overheating in high-density zones. This study provides an efficient and deployable solution for intelligent energy-saving retrofits of existing buildings.