Abstract
A real-time optimization method with the combination of simplified mechanism and deep belief neural network model for continuous catalytic reforming (CCR) process is presented this paper. The basic model of reforming reaction is established based on the simplified mechanism as well as the kinetic parameters are estimated with deep belief neural network(DBNN), and the objective functuon together the maximization of aromatics yield with the minimization of energy consumption is solved by the non-line optimizer. The hybrid mode, can not only reflect the physical characteristics of the CCR process, but also improve the model extrapolation ability, which is greatly guaranteed the efficiency and stability for the real-time optimization of CCR. Finally, the effectiveness of method proposed is validated through a case study on a industry CCR process.
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