Fouling in heat exchangers is a critical challenge in industries where fluid flows through tubes and heat transfer surfaces, leading to the deposition of dissolved impurities. As these fluids reach saturation, the buildup of scale and other materials reduces heat transfer efficiency and can create sites for corrosion. This not only compromises the performance of the equipment but also accelerates the degradation of critical components.
Leveraging machine learning (ML) to predict fouling allows distilleries to stay ahead of potential issues. By developing predictive maintenance models and utilizing physics-based features, operators can monitor and anticipate changes in the overall heat transfer coefficient (U) over time. These insights enable timely corrective actions, reducing the risk of unplanned downtime and extending the life of heat exchangers. The result is improved operational reliability, reduced maintenance costs, and enhanced process efficiency.
