Xuefeng Yan

Soft sensing is critical for real-time state awareness and quality control in biomanufacturing processes. However, existing soft sensing methods often underperform because they overlook the intrinsic multistage characteristics of these processes. Moreover, interpretability based on global variable contributions cannot provide actionable guidance for process optimization. To address these issues, we proposed the Bayesian optimization-driven multimetric partitioning with generalized additive models (BOMP-GAM). First, the framework partitions process data into different stages by fusing spatiotemporal and causal similarity through Bayesian optimization. Second, a node-level generalized additive model is used to yield stage-specific predictions and shape function of measured variables. Third, the final predictions were obtained by dynamically weighting the stagewise predictions based on multimetric similarity. Experiments on both simulated and real biomanufacturing processes show that BOMP-GAM achieves state-of-the-art predictive accuracy while uncovering stage-specific shape functions of measured variables. These insights enable targeted operational adjustments and ultimately improve production efficiency.