  Accurate temperature measurements are essential for the proper monitoring and
control of industrial furnaces. However, measurement uncertainty is a risk for
such a critical parameter. Certain instrumental and environmental errors must
be considered when using spectral-band radiation thermometry techniques, such
as the uncertainty in the emissivity of the target surface, reflected radiation
from surrounding objects, or atmospheric absorption and emission, to name a
few. Undesired contributions to measured radiation can be isolated using
measurement models, also known as error-correction models. This paper presents
a methodology for budgeting significant sources of error and uncertainty during
temperature measurements in a petrochemical furnace scenario. A continuous
monitoring system is also presented, aided by a deep-learning-based measurement
correction model, to allow domain experts to analyze the furnace's operation in
real-time. To validate the proposed system's functionality, a real-world
application case in a petrochemical plant is presented. The proposed solution
demonstrates the viability of precise industrial furnace monitoring, thereby
increasing operational security and improving the efficiency of such
energy-intensive systems.
