Notice of RetractionPrediction of the crystal´s nucleation rate based on BPNN and rough sets

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Ammonium dihydrogen phosphate(DAP) is widely used in the industry. Usually this product contains impurities and substances and may not satisfy the need of modern agricultural and industrial demand, so we must dip and recrystalize for high quality product. In the process of crystallization, the nucleation rate and the growth rate are the most important parameter, then we study these parameter in order to instruct the technology of crystallization. The nucleation rate in a Liquid fluidized bed is decided manly by the supersaturation, cooling temperature, saturation temperature and suspension density. In the paper we build a model predicting the nucleation rate through these conditions based on Back Propagation (BP) neural network with experimental data as training data. The experimental data, which collected from a Liquid fluidized bed, is preprocessed using the level of consistency in rough sets theory before be using as training sets in modeling process. The simulation results show that the neural network model given in this paper is capable of forecasting the behavior of nucleation rate exactly and rapidly, and the maximum relative error does not exceed 5.9% as compared with measured values. It also indicates the BP network has prodigious practicability.