Syngas Compositions Prediction by Neural Estimator Based on Multi-Scale Analysis and Dynamic PCA

@article{Guo2007SyngasCP,
  title={Syngas Compositions Prediction by Neural Estimator Based on Multi-Scale Analysis and Dynamic PCA},
  author={R. Guo and Xiaojuan Wang and Haijun Hu},
  journal={2007 International Conference on Mechatronics and Automation},
  year={2007},
  pages={3077-3082}
}
Prediction of syngas compositions, the most important parameter in determining the product's grade and quality control of raw syngas produced in coal gasification, was studied. A neural estimator model based on dynamic principal component analysis (DPCA), back-propagation (BP) networks, and multi-scale analysis (MSA) was proposed to infer the syngas compositions from real process variables. DPCA was carried out to select the most relevant process features and to eliminate the correlations of… CONTINUE READING
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