Hybrid modeling for improved extrapolation and transfer learning in the chemical processing industry

Abstract

In the Chemical Processing Industry, shifts in market demand often require the implementation of new operational modes that balance economic advantages with the need to meet quality and sustainability targets. Hybrid modeling can support these process changes by combining physics-based and data-driven modeling principles to improve prediction accuracy and interpretation. In this work, the use of hybrid modeling for improved extrapolation and transfer learning activities is examined in the context of simulated biodiesel production. We study and compare different configurations of hybrid modeling, including the parallel and serial structures. We also compare hybrid modeling approaches with physics-based and data-driven models, and find that hybrid modeling consistently outperforms these benchmarks in both extrapolation and transfer learning tasks. Hybrid modeling also requires fewer samples than other benchmarks for the transfer learning task. These results suggest that hybrid modeling is an effective approach for supporting decision-making and optimizing process changes in the CPI.