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Data-Driven Prediction of Sintering burn-through point Based on Novel Genetic Programming

Journal of Iron and Steel Research International 2010年第12期17卷 1-5,10页

作者： SHANG Xiu-qin LU Jian-gang SUN You-xian LIU Jun YING Yu-qian State Key Laboratory of Industrial Control Technology Department o{ Central Science and EngineeringZhejiang University Hangzhou 310027 Zhejiang China

An empirical dynamic model of burn-through point（BTP）in sintering process was *** K-means clustering was used to feed distribution according to the cold bed permeability,which was estimated by the superficial gas velocity in the cold *** each clustering,a novel genetic programming（NGP）was proposed to construct the empirical model of the waste gas temperature and the bed pressure drop in the sintering *** least square method（LSM）and M-estimator were adopted in NGP to improve the ability to compute and resist *** results show the superiority of the proposed method.

关键词： burn-through point genetic programming K-means clustering

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An Intelligent Control Scheme for burn-through point to Carbon Efficiency Optimization in Iron Ore Sintering Process

An Intelligent Control Scheme for Burn-Through Point to Carb...

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第37届中国控制会议

作者： Sheng Du Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

The sintering process is an important step of preparing raw material for iron and steel smelting. It is a series of complex physico-chemical changes with high energy consumption, high pollution and large CO emissions. How to reduce carbon consumption while ensuring the quality and yield of sinter ore are the problems to be solved urgently. In this paper,An intelligent control scheme for burn-through point(BTP) to carbon efficiency optimization in iron ore sintering process is presented. The comprehensive coke ratio is employed as a measure of carbon efficiency;and the BTP, a measure of the stability of the sintering process. First, a model is established to predict the comprehensive coke ratio using a back propagation neural network, and the carbon efficiency is optimized using the particle swarm optimization algorithm. This yields an optimal strand velocity. Then, the control of the BTP is taken into consideration using an expert-fuzzy control strategy. This yields another strand velocity. Finally, these two kinds of strand velocity are integrated using the fuzzy satisfaction method to produce a control input for the strand velocity. It not only improves the carbon efficiency, but also ensures the stable running of the sintering process. Part of the strategy was tested in an actual plant, and the results show the effectiveness of the scheme.

关键词： Sintering process Carbon efficiency Optimization burn-through point Intelligent control

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