## 7 Process Optimization Through CFD Simulations
### Coupling CFD with AI/ML for Optimization  
The integration of CFD with Artificial Intelligence (AI) and ML offers significant potential for advancing food process optimization. While CFD provides detailed, physics-based insights into transport phenomena, it remains computationally demanding--especially for high-resolution models, SA, or multi-objective optimization. By coupling CFD with AI/ML, it becomes possible to approximate simulation outputs rapidly, explore large design spaces, and enable real-time decision-making and control [151].  
AI-driven surrogate models--trained on CFD or experimental data--can predict key outcomes such as temperature profiles, shear stress, or product attributes with high speed and acceptable accuracy. These models reduce computational time from hours to seconds and are particularly valuable for tasks like uncertainty quantification, parameter sweeps, and adaptive control. Among the most commonly applied ML techniques are Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), Random Forests, and deep learning architectures such as autoencoders. These toolsare trained using datasets generated either offline (via CFD) or online (via sensors), or through hybrid physics-informed approaches [152].  
The typical CFD-ML workflow includes data generation through simulations or experiments, feature selection and preprocessing, model training and validation, and deployment for optimization or control. Active learning loops, in which additional CFD runs are iteratively used to refine the surrogate model, further improve accuracy and robustness [153].  
Practical applications of CFD-ML coupling in food engineering include spray drying (e.g., predicting wall deposition and droplet behavior), extrusion (e.g., forecasting expansion or texture based on process conditions), and mixing or fermentation (e.g., optimizing impeller design and oxygen transfer rates). Deep learning models have also been used to predict spatial temperature and moisture gradients in drying equipment, enabling faster and more precise system optimization [154].  
Key advantages of CFD-ML integration include drastically reduced simulation time, improved scalability for high-dimensional problems, and the ability to perform real-time control. However, challenges remain: model performance depends heavily on the quality and representativeness of the training data, black-box models may lack physical interpretability, and extrapolation beyond the training domain is often unreliable. Emerging solutions include physics-informed neural networks (PINNs), which embed physical laws into the learning process, and hybrid frameworks combining CFD, ML, and kinetic models [155].  
Looking ahead, adaptive systems that update surrogate models in real time using live process data, coupled with explainable AI and automated ML pipelines (AutoML), are expected to become central to intelligent food manufacturing. The integration of CFD with AI/ML not only enhances computational efficiency but also paves the way for flexible, responsive, and sustainable production systems aligned with Industry 4.0 paradigms [156].