Soft-sensing in complex chemical process based on a sample clustering extreme learning machine model∗∗This project is supported by the National Natural Science Foundation of China (No.61104131, No.61473026) and the Fundamental Research Funds for the Central Universities (No.YS1404, No.JD1413).
Di Peng, Yuan Xu, Yanqing Wang, Zhiqiang Geng, Qunxiong Zhu · IFAC-PapersOnLine · 2015
In actual chemical processes, the fact that some essential variables cannot be directly measured makes the production quality out-of-control and even results in large economic losses. In this study, a novel sample clustering extreme learning machine (SC-ELM) model is developed to achieve timely and accurate measurement. SC-ELM is a fast training algorithm with an excellent generalization performance, and the combined sample clustering approach solves the non-optimal input weights of ELM. The network structure is designed by a fast leave-one-out cross-validation (FLOO-CV) method. Meanwhile, the validity of SC-ELM model is firstly tested by two classical regression datasets. With the comparison of other ELM models, SC-ELM is proved to be an effective model in both modeling accuracy and network structure. Then, SC-ELM is applied in measuring the quality index of a high-density polyethylene (HDPE) process running in a chemical plant, and the experiment results demonstrate that SC-ELM model can achieve quality estimation with higher measuring accuracy and less training time.