  We propose a grid-like computational model of tubular reactors. The
architecture is inspired by the computations performed by solvers of partial
differential equations which describe the dynamics of the chemical process
inside a tubular reactor. The proposed model may be entirely based on the known
form of the partial differential equations or it may contain generic machine
learning components such as multi-layer perceptrons. We show that the proposed
model can be trained using limited amounts of data to describe the state of a
fixed-bed catalytic reactor. The trained model can reconstruct unmeasured
states such as the catalyst activity using the measurements of inlet
concentrations and temperatures along the reactor.
