KPCA-ESN Soft-Sensor Model of Polymerization Process Optimized by Biogeography-Based Optimization Algorithm
For solving the problem that the conversion rate of vinyl chloride monomer (VCM) is hard for real-time online measurement in the polyvinyl chloride (PVC) polymerization production process, a soft-sensor modeling method based on echo state network (ESN) is put forward. By analyzing PVC polymerization...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/493248 |
| Summary: | For solving the problem that the conversion rate of vinyl chloride monomer (VCM) is
hard for real-time online measurement in the polyvinyl chloride (PVC) polymerization production
process, a soft-sensor modeling method based on echo state network (ESN) is put forward. By
analyzing PVC polymerization process ten secondary variables are selected as input variables of the
soft-sensor model, and the kernel principal component analysis (KPCA) method is carried out on
the data preprocessing of input variables, which reduces the dimensions of the high-dimensional
data. The k-means clustering method is used to divide data samples into several clusters as inputs of
each submodel. Then for each submodel the biogeography-based optimization algorithm (BBOA)
is used to optimize the structure parameters of the ESN to realize the nonlinear mapping between
input and output variables of the soft-sensor model. Finally, the weighted summation of outputs of
each submodel is selected as the final output. The simulation results show that the proposed
soft-sensor model can significantly improve the prediction precision of conversion rate and
conversion velocity in the process of PVC polymerization and can satisfy the real-time control
requirement of the PVC polymerization process. |
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| ISSN: | 1024-123X 1563-5147 |