Statistical Approach to Constructing Predictive Models for Thermal Resistance Based on Operating Conditions

We have constructed statistical models that predict thermal resistance after fouling layer formation in a heat exchanger, in which a slurry of stearic acid in toluene was cooled. Chemoinformatics was used, and the initial rate of increase in thermal resistance (dU–1/dt) was calculated from experimental conditions such as coolant flow rate and the degree of supersaturation. We then constructed models for thermal resistance at a steady state using calculated values of dU–1/dt and experimental conditions. Our model gives a good correlation with the experimental results. The contribution of operating conditions to fouling layer formation was discussed semiquantitatively on the basis of linear regression coefficients that were obtained from our model. Because only operating conditions and set values were used as input, our approach is very practical for prediction of thermal resistance given certain operating conditions.