Intelligent Integrated Control for Burn-Through Point to Carbon Efficiency Optimization in Iron Ore Sintering Process

The iron ore sintering process is an important step in preparing raw material for ironmaking. How to reduce carbon consumption while ensuring the stable running of the sintering process is an urgent problem to be solved. In this brief, an intelligent integrated control strategy for the burn-through point (BTP) to carbon efficiency optimization in the sintering process is presented. The comprehensive coke ratio (CCR) is employed as a measure of carbon efficiency, and the BTP is a measure of the stability of the sintering process. First, a short time scale model is established to predict the CCR, and the carbon efficiency is optimized by using the particle swarm optimization algorithm. This yields an optimal carbon efficiency and one control quantity of strand velocity. Another control quantity of strand velocity is obtained by a BTP expert-fuzzy controller. Both control quantities are integrated by a well-designed intelligent integrated controller, so that the optimal strand velocity, as the final control input, is determined. An experiment is carried out to verify the effectiveness of the proposed strategy. The experimental results show that the proposed strategy improves the carbon efficiency while ensuring the stable running of the sintering process, which has a good application prospect in the industrial site.

[1]  Min Wu,et al.  Hybrid modeling and online optimization strategy for improving carbon efficiency in iron ore sintering process , 2019, Inf. Sci..

[2]  Mohammad Hossein Abbaspour-Fard,et al.  An intelligent integrated control of hybrid hot air-infrared dryer based on fuzzy logic and computer vision system , 2017, Comput. Electron. Agric..

[3]  Jinhua She,et al.  Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process , 2016 .

[4]  Xianpeng Wang,et al.  An Improved Particle Swarm Optimization Algorithm for the Hybrid Flowshop Scheduling to Minimize Total Weighted Completion Time in Process Industry , 2010, IEEE Transactions on Control Systems Technology.

[5]  Pan Zhang,et al.  A Multilevel Prediction Model of Carbon Efficiency Based on the Differential Evolution Algorithm for the Iron Ore Sintering Process , 2018, IEEE Transactions on Industrial Electronics.

[6]  Fuwen Yang,et al.  Event-Triggered Asynchronous Guaranteed Cost Control for Markov Jump Discrete-Time Neural Networks With Distributed Delay and Channel Fading , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Min Wu,et al.  Intelligent Coordinating Control Between Burn-Through Point and Mixture Bunker Level in an Iron Ore Sintering Process , 2017, J. Adv. Comput. Intell. Intell. Informatics.

[8]  Jin-Hua She,et al.  A hybrid time series prediction model based on recurrent neural network and double joint linear-nonlinear extreme learning network for prediction of carbon efficiency in iron ore sintering process , 2017, Neurocomputing.

[9]  Ye Li,et al.  An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries , 2015 .

[10]  Fuwen Yang,et al.  A Novel Sliding Mode Estimation for Microgrid Control With Communication Time Delays , 2019, IEEE Transactions on Smart Grid.

[11]  Jinhua She,et al.  A hybrid just-in-time soft sensor for carbon efficiency of iron ore sintering process based on feature extraction of cross-sectional frames at discharge end , 2017 .

[12]  Xin Chen,et al.  An Intelligent Control Strategy for Iron Ore Sintering Ignition Process Based on the Prediction of Ignition Temperature , 2020, IEEE Transactions on Industrial Electronics.

[13]  Takao Terano,et al.  Decoupling Control Method With Fuzzy Theory for Top Pressure of Blast Furnace , 2019, IEEE Transactions on Control Systems Technology.

[14]  Hongye Su,et al.  Neural Network-Based State of Charge Observer Design for Lithium-Ion Batteries , 2018, IEEE Transactions on Control Systems Technology.

[15]  Jin-Hua She,et al.  Hybrid multistep modeling for calculation of carbon efficiency of iron ore sintering process based on yield prediction , 2016, Neural Computing and Applications.

[16]  Jin-Hua She,et al.  An intelligent control system based on prediction of the burn-through point for the sintering process of an iron and steel plant , 2012, Expert Syst. Appl..

[17]  Min Wu,et al.  Hierarchical Intelligent Control System and Its Application to the Sintering Process , 2013, IEEE Transactions on Industrial Informatics.

[18]  Min Wu,et al.  T–S Fuzzy Logic Based Modeling and Robust Control for Burning-Through Point in Sintering Process , 2017, IEEE Transactions on Industrial Electronics.