Soft-sensor Modeling of Vessel Interior Temperatures under Different Nozzle Configurations in a Cooling Process

Abstract

Temperature monitoring plays an important role in industrial processes, and soft sensors have attracted increasing attention for this purpose. This study evaluates the performance of a gray-box soft sensor model, which requires only online temperature data at the nozzle and at one reference location as inputs, for vessel interior temperature prediction in a cooling process with different jet inflows. Lab-scale experiments of water vessels were conducted with two types of nozzles to measure local temperatures at multiple locations. The model was trained by a data set, and validated by another data set under the same conditions except for nozzle types. The reference location selection was found to be able to significantly influence the model’s predictive accuracy. Flow visualization experiments were performed to analyze fluid dynamics near the reference location. Significant flow-pattern variations were found in its vicinity, which may affect predictive accuracy. Results show that the model can achieve satisfactory error levels under varied flow fields induced by different nozzle configurations, with mean and maximum errors reduced to below 0.5 °C and 1.1 °C, respectively. The findings highlight the unfavorable influences of inflow-induced convection on predictive performance and demonstrate that appropriate reference selection plays an important role in predictive accuracy. This study widens the applicable scenarios of a gray-box soft sensor in industrial thermal processes and related fields, such as air conditioning.