Abstract

Integrated modeling of industrial reactor networks presents a synergistic approach by combining first-principles and data-driven methods. First-principles models, grounded in fundamental physical laws, provide a mechanistic understanding of reactor dynamics, while data-driven approaches utilize empirical data for calibration and validation, enhancing predictive accuracy under diverse operating conditions. This integration yields comprehensive models capable of optimizing reactor performance, improving process efficiency, and enabling robust control strategies. Case studies demonstrate successful applications across various industries, highlighting the potential for advancing process engineering through integrated modeling techniques. Future research focuses on refining methodologies and addressing challenges to further enhance model robustness and scalability in industrial settings.