A data-driven framework for plant-wide modeling and simulation in biopharmaceutical manufacturing

Plant-wide simulation in biopharmaceutical manufacturing relies heavily on mechanistic models that are costly to develop, while data-driven approaches are typically confined to single unit operations and lack plant-wide integration. This work presents a modular, data-driven modeling framework for plant-wide simulation, where interconnected predictive models represent different sections of the process and enable systematic propagation of predicted outputs and process variables across the entire plant without relying on mechanistic constraints. The framework supports flexible reconfiguration across process topologies and scales, allowing rapid prediction and scenario analysis. A sensitivity analysis is integrated to quantify the impact of intermediate outputs and process parameters on final product yield. A case study on an end-to-end industrial-scale vaccine biopharmaceutical plant, involving 12 manufacturing steps and 665 batches, is used herein to demonstrate the approach. The plant-wide model achieves a yield prediction RMSE of 0.141 and maintains stable predictive performance under ± 5% input perturbations. Results reveal highly uneven cross-stage influence, with downstream operations dominating final yield variability. The proposed framework provides a practical and scalable tool for plant-wide modeling and early-stage prediction, as well as a basis for process optimization in biopharmaceutical systems.