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Autori: Tarca, L. A.; Grandjean, B. P. A.; Larachi, F
Editorial: Industrial and Engineering Chemistry Research, 42(8), p.1707-1712, 2003.
To increase confidence in neural network modeling of multiphase reactor characteristics, we have to take advantage of some a priori knowledge of the physical laws governing these systems in order to build neural models having phenomenological consistency (PC). A common form of PC is the monotonicity constraint of a characteristic to be modeled with respect to some important dimensional variables describing the multiphase system. When the inputs of a neural model are functions (usually dimensionless) of the variables with respect to which monotonicity is expected, the monotonicity might not be guarantied, but such a drawback is only observed after the training. A genetic algorithm based methodology was proposed to produce several highly accurate and nearly phenomenologically consistent networks differing by their inputs and architecture. PC and accuracy were shown to be boosted up meaningfully by combining such networks in a linear meta-model. A new optimality criterion for the meta-model parameters identification was proposed and the results were compared with classical MSE optimality criterion. The proof of concept of the approach was illustrated in modeling the two-phase pressure drop in counter-currently operated randomly packed beds.
Cuvinte cheie: neural network, meta-model, phenomenological consistency, pressure drop, counter-current packed bed