## 1 Introduction
### Paper Contribution  
In this paper, a HVAC modeling framework, developed as part of a building digital twin framework, is showcased for simulation of ventilation system in a university campus building. The created building model is tested with different control strategies to demonstrate the framework's potential for operational strategy testing and planning. Most of the examined control strategies incorporate multi-objective optimization with MPC and compare ventilation system operation with different weightings of the objectives: energy cost, indoor air quality and CO2 emissions attributed to energy consumption.  
This study introduces a novel HVAC modeling framework developed within the context of building digital twins, offering significant advancements over existing research in the field of building energy systems. By incorporating a multi-objective model predictive control (MPC) framework, it addresses the limitations of single-objective optimization approaches often found in the literature. The study demonstrates how the integration of energy cost, indoor air quality, and CO2 emissions into the optimization process provides a holistic strategy for building performance management. Through scenario-based comparisons, the framework showcases the superiority of MPC over rule-based control, achieving a notable 18% energy reduction while significantly improving air quality, a dual benefit that aligns with and extends the findings of previous studies.  
A key innovation of this study is the introduction of a novel air quality metric to evaluate ventilation system performance. This metric addresses the lack of standardized approaches for assessing indoor air quality in terms of CO2 thresholds, offering a practical and meaningful tool for system comparison and optimization. Furthermore, the study embeds the HVAC modeling framework within a building digital twin environment, bridging the gap between traditional static building models and dynamic operational realities. This integration enhances the framework's relevance for real-time simulations and predictive analytics, opening new avenues for proactive building management. The proposed framework adds value through its rapid and automated model setup process. By leveraging a combination of documented parameters, historical data, and literature-based estimates, the framework supports the quick and efficient deployment of system models, making it scalable for diverse building types and applications. Despite minor discrepancies during validation, the model's ability to closely replicate actual system behavior ensures its reliability for operational planning. Moreover, the customizable objective prioritization within the MPC framework allows stakeholders to tailor operational strategies to specific goals, e.g., cost reduction, carbon footprint minimization, or enhancing occupant comfort.  
The potential impact of this framework is significant, particularly in terms of energy efficiency and sustainability. With 18% energy savings demonstrated in this study, the framework contributes to achieving global sustainability goals while reducing operational costs. Its scalability and robustness further enhance its practical value, with the possibility of generalizing the approach to various building systems. Additionally, the integration of this framework into digital twin systems positions it as a critical enabler for smart building management, facilitating real-time optimization, fault detection, and adaptive control. Overall, this study offers a comprehensive and innovative solution for HVAC system modeling and optimization. Its integration of multi-objective MPC and digital twin technologies establishes a strong foundation for advancing building energy systems. The practical implications of this work extend beyond academic research to scalable, real-world applications, reinforcing its significance in the transition toward smarter, more sustainable building operations.