Multisource Data Ensemble Modeling for Clinker Free Lime Content Estimate in Rotary Kiln Sintering Processes

Clinker free lime (f-CaO) content plays a crucial role in determining the quality of cement. However, the existing methods are mainly based on laboratory analysis and with significant time delays, which makes the closed-loop control of f-CaO content impossible. In this paper, a multisource data ensemble learning-based soft sensor model is developed for online estimation of clinker f-CaO content. To build such a soft sensor model, input flame images, process variables, and the corresponding output f-CaO content data for a rotary cement kiln were collected from No. 2 rotary kiln at Jiuganghongda Cement Plant which produces 2000 tonnes of clinker per day. The raw data were preprocessed to distinguish the flame image regions of interest (ROI) and remove process variable outliers. Three types of flame image ROI features, i.e., color, global configuration, and local configuration features, were then extracted without segmentation. Further, a kernel partial least square technique was applied for extracting the compressed score matrix features from the concatenated flame image features and filtered process variables to avoid high-dimensional, nonlinear, and correlated problems. Feed-forward neural networks with random weights were employed as base learners in our proposed ensemble modeling framework, which aims to enhance the model's reliability and prediction performance. A total of 157 flame images, the associated process variable data, and the experimentally measured f-CaO content data were used in our experiments. A comparative study on the f-CaO content estimator built by various feature compressed techniques and learner models and robustness analysis were carried out. The results indicate that the proposed multisource data ensemble soft sensor model performs favorably and has good potential in real world applications.