Stanišić Darko, Mejić Luka, Jorgovanović Bojan, Ilić Vojin, Jorgovanović Nikola
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
Sensors (Basel). 2024 Mar 19;24(6):1948. doi: 10.3390/s24061948.
Soft sensors are increasingly being used to provide important information about production processes that is otherwise only available through off-line laboratory analysis. However, usually, they are developed for a specific application, for which thorough process analysis is performed to provide information for the appropriate selection of model type and model structure. Wide industrial application of soft sensors, however, requires a method for soft sensor development that has a high level of automatism and is applicable to a significant number of industrial processes. A class of processes that is very common in the industry are processes with distinct operating conditions. In this paper, an algorithm that is suitable for the development of soft sensors for this class of processes is presented. The algorithm possesses a high level of automatism, as it requires minimal user engagement regarding the structure of the model, which makes it suitable for implementation as a customary industrial solution. The algorithm is based on a radial basis function artificial neural network, and it enables the automatic selection of the model structure and the determination of model parameters, only based on the training data set. The testing of the presented algorithm is done on the cement production process, since it represents a process with distinct operating conditions. The results of the test show that, besides providing a high level of automatism in model development, the presented algorithm generates a soft sensor with high estimation performance.
软传感器正越来越多地用于提供有关生产过程的重要信息，否则这些信息只能通过离线实验室分析获得。然而，通常情况下，软传感器是针对特定应用开发的，为此需要进行全面的过程分析，以便为模型类型和模型结构的适当选择提供信息。然而，软传感器的广泛工业应用需要一种软传感器开发方法，该方法具有高度的自动化，并且适用于大量的工业过程。在工业中非常常见的一类过程是具有不同操作条件的过程。本文提出了一种适用于此类过程软传感器开发的算法。该算法具有高度的自动化，因为它在模型结构方面所需的用户参与最少，这使其适合作为常规工业解决方案来实施。该算法基于径向基函数人工神经网络，并且仅基于训练数据集就能实现模型结构的自动选择和模型参数的确定。所提出算法的测试是在水泥生产过程中进行的，因为它代表了一个具有不同操作条件的过程。测试结果表明，所提出的算法除了在模型开发中提供高度的自动化外，还能生成具有高估计性能的软传感器。
