文献类型：期刊文献

中文题名：A novel chemical composition estimation model for cement raw material blending process

英文题名：A novel chemical composition estimation model for cement raw material blending process

作者：Yaoyao Bao[1];Yuanming Zhu[1];Weimin Zhong[1];Feng Qian[1]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2019

卷号：27

期号：11

起止页码：2734

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2019_2020】；

基金：Supported by the National Key R&D Program of China(2016YFB0303401);the National Natural Science Foundation of China(61333010,61503138).

语种：英文

中文关键词：Raw;material;blending;process;Chemical;component;estimation;Modulus;prediction;System;identification

外文关键词：Raw material blending process;Chemical component estimation;Modulus prediction;System identification

摘要：Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.
Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.