A Physics-Informed Neural Network (PINN) framework for generic bioreactor modelling

Many previous studies have explored hybrid semiparametric models merging Artificial Neural Networks (ANNs) with mechanistic models for bioprocess applications. More recently, Physics-Informed Neural Networks (PINNs) have emerged as promising alternatives. Both approaches seek to incorporate prior knowledge in ANN models, thereby decreasing data dependency whilst improving model transparency and generalization capacity. In the case of hybrid semiparametric modelling, the mechanistic equations are hard coded directly into the model structure in interaction with the ANN. In the case of PINNs, the same mechanistic equations must be “learned” by the ANN structure during the training. This study evaluates a dual-ANN PINN structure for generic bioreactor problems that decouples state and reaction kinetics parameterization. Furthermore, the dual-ANN PINN is benchmarked against the general hybrid semiparametric bioreactor model under comparable prior knowledge scenarios across 2 case studies. Our findings show that the dual-ANN PINN can level the prediction accuracy of hybrid semiparametric models for simple problems. However, its performance degrades significantly when applied to extended temporal extrapolation or to complex problems involving high-dimensional process states subject to time-varying control inputs. The latter is primarily due to the more complex multi-objective training of the dual-ANN PINN structure and to physics-based extrapolation errors beyond the training domain.