Asynchronous multisource alignment-driven real-time online detection of ash content in flotation clean coal - Wang, Lanhao; Liu, Jiahui; Dai, Wei; Gui, Xiahui; Wang, Hongyan (International journal of coal preparation and utilization, 2025)
Accurate real-time monitoring of the ash content in flotation clean coal is pivotal for intelligent optimization and closed-loop control of the flotation process, directly affecting product quality and the economic performance of coal preparation plants. To address the limitations of traditional approaches-namely response lag, insufficient accuracy, and inefficient fusion of multisource information-this study proposes an intelligent online sensing method based on multisource data fusion, with the prediction pipeline decoupled into three stages: alignment - representation - prediction. First, a multiscale, differentiable dynamic time-warping (MSSoftDTW) scheme is employed to precisely align asynchronous multisource time-series data, thereby enhancing cross-modal temporal consistency. Second, an interpretable Constructive algorithm with response-weight mechanism (ICA-RW) is introduced to enable feature learning and structural adaptation, suppressing redundancy and collinearity while improving feature robustness. Third, an ensemble regression model that combines a relevance vector machine with adaptive boosting (RVM-Adaboost) is developed to better accommodate nonlinear relationships and drifts in operating conditions. By fusing X-ray fluorescence (XRF) spectra, key process variables, and features extracted from tailings images, the method achieves high-accuracy, real-time prediction of clean-coal ash content. Validation on industrial-site data demonstrates significant gains in both accuracy and stability over conventional regression baselines, meeting the real-time requirements of online monitoring and control and providing deployable support for flotation process optimization and intelligent upgrading.