This works offers an extension of the CRNN methodology to heterogeneous reaction networks including reversible and coverage dependent reactions called hCRNN. The demonstrated construction offers clearly separated and interpretable network layers directly corresponding to terms in the general kinetic expression. As a consequence, fitting arbitrarily complex observed kinetic behavior is possible while at the same time ensuring that the recovered kinetic obeys fundamental physicochemical principles of chemical kinetics. This contrasts the hCRNN against black box models and allows for the discovery of kinetic parameters. Since the hCRNN works the same way as a conventional kinetic expression, usage in common chemical engineering applications, i.e, reactor scale simulations is trivially accessible. The fully trained model can function as a drop-in replacement for conventional kinetic expressions: