Speaker
Description
Abstract. Industrial drying is an energy-intensive process in which manual monitoring of process parameters limits the continuous assessment of thermal performance. At the investigated industrial facility, process parameters were manually recorded from instrument panels, resulting in discrete datasets that lim-ited the assessment of continuous energy performance. This study developed a physics-based Internet of Things system for live thermal energy assessment in an industrial drying process. The proposed framework integrates data from two con-trol panels to continuously monitor the hot-water circulation system and oven op-erating conditions. The acquired data were automatically processed to estimate thermal energy supply and drying performance in real time. The oven temperature remained stable at 75–80 °C, while the inlet–outlet water temperature difference confirmed effective heat transfer to the drying chamber. The theoretical energy requirement was estimated from laboratory experiments using sensible and latent heat calculations and used as the minimum energy threshold for industrial pro-duction. This threshold was continuously compared with the actual thermal ener-gy supplied, which was calculated in real time from the monitored hot-water tem-perature and flow-rate data. The average actual and theoretical thermal energy re-quirements were 151,291 and 115,920 kcal h⁻¹, respectively. The difference re-sults in an energy difference of 35,371 kcal h⁻¹, which is equivalent to 23.4% of the supplied thermal energy. This energy difference represents the thermal energy (heat) loss between the heating system and the drying process. The proposed framework enables continuous assessment of thermal performance, facilitating the identification of energy losses and supporting data-driven energy management in industrial drying systems.
Keywords: Heat loss; industrial drying; internet of things; live monitoring; ther-mal energy assessment
