Hybrid modelling of chemical processes: a unified framework based on deductive, inductive, and abductive inference

Abstract

Hybrid modelling offers a powerful means of combining mechanistic principles with data-driven learning for complex chemical processes. However, most existing approaches rely on structural coupling without a principled basis for integrating distinct modes of reasoning or enabling modular reuse. This work introduces a unified layered hybrid modelling architecture grounded in three epistemic layers: deductive, inductive, and abductive. Roles of each layer are: enforcing physical laws, learning unknown dynamics, and inferring latent states. The formulation is expressed in operator-theoretic terms. Results demonstrate improved accuracy, interpretability, and adaptability, highlighting the framework’s potential as a transparent and generalizable strategy for hybrid modelling under uncertainty in chemical process systems, while also supporting compositional reasoning and layer-wise retraining.

The first case study considers a single-unit non-isothermal batch polymerization reactor with unknown reaction kinetics and partial temperature observability. The deductive layer encodes mass and energy balances, the inductive layer learns kinetics via a neural network, and the abductive layer reconstructs latent temperature states. The second case study examines a multi-unit fed-batch bioreactor flowsheet, representative of typical chemical process configurations. Here, the deductive layer models feed-flow dynamics (unit #1), the inductive layer predicts biomass growth (unit #2), and the abductive layer estimates latent physiological states such as oxygen uptake rate and pH (unit #3). These examples demonstrate that the framework can integrate multiple inference modes within a single unit or distribute them across a flowsheet, enabling application to a wide range of hybrid modelling scenarios. The approach is general and suited for scalable, transparent modelling under uncertainty.