Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.

Fouling in food‑processing heat exchangers is a major operational problem. It reduces thermal efficiency, raises pressure drops, and can increase safety hazards. Most plants rely on fixed‑interval Clean‑in‑Place (CIP) cycles, which often lead to overuse of chemicals, wasted energy, and unnecessary downtime. This research explores a hybrid‑intelligence approach that combines physical thermal‑hydraulic models with machine‑learning algorithms to predict fouling growth and set optimal CIP schedules. Real‑time sensor data—such as temperature differences, flow rates, and pressure drops—feed the model, allowing it to capture both predictable physical degradation and random operational variations. A systematic review framework is provided to help structure and implement predictive‑maintenance models that adjust CIP triggers based on real‑time

estimates of fouling resistance. The methodology‑focused approach makes it easy to compare different hybrid designs, from purely data‑driven models to physics‑informed neural networks (PINNs). The goal is to create adaptive cleaning protocols that cut chemical and energy use while keeping heat‑exchanger performance at its best.

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