辐射测温误差分析的新方法及其在工业炉中的应用
A novel method for error analysis in radiation thermometry with application to industrial furnaces
摘要：
精确的温度测量对于工业炉的正确监测和控制至关重要。然而，测量不确定性是这样一个关键参数的风险。在使用光谱波段辐射测温技术时，必须考虑某些仪器误差和环境误差，例如目标表面发射率的不确定性、周围物体反射的辐射或大气吸收和发射等。可以使用测量模型（也称为误差校正模型）来隔离对测量辐射的不期望的贡献。本文提出了一种方法，预算在石油化工炉的情况下，温度测量过程中的误差和不确定性的重要来源。还提供了一个连续监测系统，在基于深度学习的测量校正模型的辅助下，允许领域专家实时分析熔炉的运行。为了验证所提出的系统的功能，在一个石化厂的实际应用案例。所提出的解决方案证明了精确工业炉监控的可行性，从而提高了运行安全性并提高了此类能源密集型系统的效率。
英文摘要：
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.
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