Abstract
Cement is regarded as the most widely used construction material worldwide; however, its production is also recognized as a major contributor to global CO2 emissions. Strict control of cement quality is therefore required to prevent excessive consumption of raw materials and energy, which would otherwise increase the process environmental footprint. Cement quality is largely governed by clinker quality, which is primarily characterized by two quality control parameters: free-lime content and alite fraction. At present, these are characterized by costly and time-consuming laboratory analyses that are not optimal for real time process control and optimization. Hence, in this work, a soft sensor for the real-time estimation of the clinker alite fraction is proposed. The developed soft sensor is designed to adapt to process drifts and operating condition changes, capture nonlinear and dynamic behavior, and retain interpretability through a Partial Least Squares (PLS) modelling framework. To this end, a novel recursively adaptive local dynamic soft-sensing strategy (ALD-PLS) is introduced and implemented within a multi-model ensemble structure referred to as Quasi-Ensemble PLS (QE-PLS). Unlike conventional ensemble approaches, where model diversity is generated through data resampling or training-testing partitioning, the proposed framework constructs multiple sub-models using combinations of model hyperparameters, evaluated on the same evolving dataset. As a result, improved predictive accuracy and robustness are achieved, while estimation uncertainty is quantified. The proposed QE-PLS soft sensor is shown to outperform, in terms of R2 and RMSE, PLS-based and single-instance implementations ALD-PLS for a similar task. The novel methodology is validated against data collected from an industrial cement production plant.