Em. Assaf et al., THERMAL RUNAWAY OF ETHYLENE OXIDATION REACTORS - PREVISION THROUGH NEURONAL NETWORKS, Chemical Engineering Science, 51(11), 1996, pp. 3107-3112
The dynamic behavior of an ethylene oxidation fixed-bed reactor has be
en originally simulated by a phenomenological model, encompassing mass
and energy balances of the catalytic bed. This model makes use of the
one-dimensional pseudo-homogeneous approach, with apparent kinetic pa
rameters obtained from the literature. The resulting set of partial-di
ferential equations is solved by discretization of the space variable
in finite-differences and integration of the attained ordinary-differe
ntial equations with respect to time with a marching algorithm that ac
counts for the problem of stiffness near the runaway point. This paper
focuses on the use of a neuronal network in forecasting possible ther
mal runaway situations of this highly exothermic process. The final ob
jective is to build a reliable inference alarm algorithm for fast dete
ction and prevention of this situation. The neuronal network also pred
icts eventual hot spot position and temperature, based on informations
such as inlet flows, temperatures and pressures, provided by the plan
t instrumentation. Feedforward neuronal networks were used, with one h
idden layer. A training algorithm based on a combination of backpropag
ation and gaussian random guesses was applied. The neuronal network re
presents well the evolution of the transient temperature profile.