Experimental surrogate-based MPC of twin-screw extrusion from sparse measurements

We demonstrate real-time surrogate-model-based temperature control of a co-rotating twin-screw extruder. A one-dimensional mechanistic extruder model describing mass transport, pressure dynamics, and heat transfer is extended and experimentally validated. To enable real-time optimization, neural network surrogate models in a nonlinear autoregressive with exogenous inputs formulation are trained on simulation data from the mechanistic model. Given the sparsity of available measurements, a surrogate is combined with a particle filter for robust state observation, reconstructing the complete melt and barrel temperature profiles from only three melt temperature sensors. A Bayesian optimization algorithm is proposed for optimal sensor placement that maximizes state observation accuracy under hardware constraints. A surrogate is integrated into a model predictive control formulation that simultaneously maximizes melt temperature, tracks throughput references, and enforces constraints imposed by material characteristics and process limitations for industrially relevant product cases. The complete closed-loop scheme is validated experimentally on a pilot-scale extrusion process.