Soft-sensing in complex chemical process based on a sample clustering extreme learning machine model

Abstract

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.