English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

For solving the problem that the conversion rate of vinyl chloride monomer (VCM) ishard for real-time online measurement in the polyvinyl chloride (PVC) polymerization productionprocess, a soft-sensor modeling method based on echo state network (ESN) is put forward. Byanalyzing PVC polymerization process ten secondary variables are selected as input variables of thesoft-sensor model, and the kernel principal component analysis (KPCA) method is carried out onthe data preprocessing of input variables, which reduces the dimensions of the high-dimensionaldata. The k-means clustering method is used to divide data samples into several clusters as inputs ofeach 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 betweeninput and output variables of the soft-sensor model. Finally, the weighted summation of outputs ofeach submodel is selected as the final output. The simulation results show that the proposedsoft-sensor model can significantly improve the prediction precision of conversion rate andconversion velocity in the process of PVC polymerization and can satisfy the real-time controlrequirement of the PVC polymerization process.

KPCA-ESN Soft-Sensor Model of Polymerization Process Optimized by Biogeography-Based Optimization Algorithm