Advanced Paradigms of Digital Process Engineering for Multiscale Intelligent Optimization, Monitoring, and Control of Reactors and Separation Columns in Midstream and Downstream Industries Based on Artificial Intelligence, Digital Twins, and Big Data Analytics

Digital transformation in midstream and downstream industries is rapidly reshaping how chemical reactors and separation columns are designed, operated, and optimized. This paper-oriented overview explores advanced paradigms of Digital Process Engineering enabled by the convergence of artificial intelligence (AI), digital twins, and big data analytics for multiscale intelligent optimization, monitoring, and control. These industries are characterized by highly nonlinear, dynamic, and multiscale systems where conventional modeling and control strategies often struggle to ensure optimal performance under uncertainty. As a result, there is a growing need for integrated digital frameworks that can continuously learn from operational data and adapt in real time. The integration of AI enables predictive and prescriptive decision-making through machine learning, deep learning, and reinforcement learning techniques applied to complex process systems. Digital twin technology provides a real-time virtual replica of physical assets, allowing continuous synchronization between simulated environments and industrial operations. This capability enhances process visibility, improves fault detection, and enables scenario-based optimization. Meanwhile, big data analytics supports the ingestion and processing of massive, heterogeneous datasets generated from industrial IoT sensors, distributed control systems, and historical operational databases. This work highlights how multiscale modeling bridges molecular-level phenomena with plant-wide behavior, particularly in reactors and separation columns where transport, reaction kinetics, and thermodynamic interactions occur simultaneously across different scales. The combined use of AI-driven models and physics-based simulations creates hybrid systems that improve accuracy, interpretability, and robustness. Furthermore, intelligent monitoring frameworks based on anomaly detection and predictive maintenance are discussed as essential components for ensuring operational reliability and reducing downtime. The study also identifies key challenges, including data quality, computational complexity, cybersecurity risks, and integration with legacy systems. Finally, future directions emphasize the role of Industry ۵.۰, sustainability-driven optimization, and autonomous process systems. This convergence marks a significant shift toward fully intelligent, self-optimizing industrial ecosystems capable of continuous adaptation and high-efficiency operation.

کلیدواژه ها:

Artificial Intelligence ، Digital Twins ، Big Data Analytics ، Process Optimization ، Chemical Reactors ، Separation Columns