The temperature and silicon content of hot metal are essential parameters for the thermal control of a blast furnace. However, the physical structure of the blast furnace prevents direct and online methods from accurately predicting these parameters. In this study, we propose a new algorithm based on fuzzy c-means (FCM) and exogenous nonlinear autoregressive model (NARX) to develop a soft sensor for predicting the temperature and silicon content of hot metal. FCM is a data modeling technique that works by clustering similar data objects while separating dissimilar ones. FCM is highly effective in the identification of natural groupings in the observed data; in this case, determination of groups of operational conditions. The NARX neural network presents a model for the accurate prediction of temperature and silicon content of hot metal. The proposed algorithm was evaluated based on its efficiency in simulating the industrial process for manufacturing hot metal in a blast furnace. The results showed that a soft sensor based on FCM-NARX models has a better performance than that using the conventional NARX model.

铁水的温度和硅含量是高炉热控制的基本参数。但是，高炉的物理结构会阻止直接和在线方法准确预测这些参数。在这项研究中，我们提出了一种基于模糊c均值（FCM）和外源非线性自回归模型（NARX）的新算法，以开发一种软​​传感器来预测铁水的温度和硅含量。FCM是一种数据建模技术，通过聚集相似的数据对象同时分离不相似的对象来工作。FCM在识别观测数据中的自然分组方面非常有效；在这种情况下，确定运行条件组。NARX神经网络为准确预测铁水的温度和硅含量提供了一个模型。基于该算法在模拟高炉中生产铁水的工业过程中的效率，对该算法进行了评估。结果表明，与传统的NARX模型相比，基于FCM-NARX模型的软传感器具有更好的性能。。