## 8 Current Challenges and Future Perspectives
### Integration with Real-Time Process Control  
The transition toward Industry 4.0 is transforming food manufacturing through automation, interconnectivity, and data-driven decision-making. Central to this transformation is the concept of the digital twin--a dynamic, virtual representation of a physical process that continuously mirrors and responds to real-world operations. When combined with computational models and real-time data streams, digital twins enable predictive, adaptive, and autonomous control strategies [159].  
In food engineering, digital twins integrate high-fidelity physics-based models (e.g., CFD), sensor inputs (temperature, moisture, torque), machine learning algorithms for anomaly detection and surrogate modeling, and control logic for process optimization. CFD plays a pivotal role by simulating heat and mass transfer, fluid flow, and complex multiphase interactions that are otherwise difficult to observe directly. It also enables the creation of virtual sensors and boundary condition estimators, providing deeper insight into internal process dynamics [160].  
Applications of CFD-enhanced digital twins in food processing include spray drying systems (to monitor droplet trajectories and prevent fouling), extrusion (to estimate pressure and specific mechanical energy), mixing (to control residence time and shear distribution), and thermal treatments (to ensure uniform pasteurization under varying loads). These simulations can be synchronized with sensor feedback in real time, enabling operators to perform what-if analyses, anticipate system deviations, and fine-tune process parameters dynamically [161].  
Implementing such systems requires a robust digital infrastructure. This includes IoT-enabled devices, edge computing for local data processing, cloud integration for storage and analytics, and interoperable communication protocols (such as OPC UA or MQTT) that facilitate data exchange between simulation environments and control platforms like SCADA or MES [120].  
The industrial benefits are considerable: improved product consistency, reduced energy usage, early detection of faults, enhanced responsiveness to raw material variability, and accelerated scale-up using validated virtual prototypes. These capabilities embody the core principles of Industry 4.0, including interoperability, transparency, and cyber-physical system integration [159].  
Nevertheless, challenges remain. Traditional CFD models are often too computationally intensive for real-time deployment, necessitating the use of surrogate models or reduced-order approaches. Maintaining model accuracy over time requires continuous recalibration to reflect equipment wear or fouling. Standardized protocols for simulation-control integration are still under development, and cybersecurity must be rigorously addressed [162].  
Future advancements are expected to focus on hybrid modeling approaches that combine physics-based and AI-driven components, self-updating digital twins, and integrated sustainability tracking (e.g., CO\({}_{2}\) footprint, energy intensity). As food manufacturing continues its digital evolution, the integration of CFD with real-time control offers a powerful framework for smarter, more flexible, and sustainable processing systems.