Abstract：
Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.
摘要：
工业4.0是传感器数据分析的主要应用领域。工业炉（IFs）是由特殊的热力学材料和技术制成的复杂机器，用于工业生产应用，需要特殊的热处理周期。操作IF时最关键的问题之一是黑碳（EoBC）的排放，这是由于大量的因素，如燃料的质量和数量，炉效率,用于该过程的技术，操作实践，负载类型和与炉操作时流体的工艺条件或机械性能相关的其他方面。本文提出了一种使用机器学习（ML）的预测模型在IFs运行期间预测EoBC的方法。我们利用具有历史操作的真实数据集来训练ML模型，通过使用真实数据进行评估，我们确定了最适合实际生产环境中数据集和实施约束特征的最合适的方法。评估结果证实，可以提前很好地预测不良的EoBC，通过预测模型。据我们所知,本文是在IF行业中详细介绍用于预测EoBC的机器学习概念的第一种方法。
