According to the characteristics of non-linearity, strong coupling and a large time delay in the sintering process, the overall analysis for the sintering process has been carried out from the process parameter control point. The sinter performance evaluation indexes and the main influential parameters were determined. The quality prediction model for the sintering process was established using back propagation (BP) neural network algorithm with momentum and variable learning rate. The simulation experimental results show that the model has a higher prediction accuracy and a stronger self-learning ability. The predictive hit rate of random samples is over 81% by adopting BP neural network with the structure of 15-24-4 and network error is 0.65×10−3, thereby verifying the accuracy and effectiveness of the quality prediction model on the basis of process parameters control.
BaiRTongSCChaiTY (2009) Intelligent prediction model of technical indices in the industrial process and its application. In: Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, 16–18 December, 2009, Shanghai, P.R. China, pp. 7291–7296. Piscataway, NJ, USA: IEEE.
2.
ChenZLZhangJLZhangYP, et al. (2016) Influence of basicity and w(SiO2) on sinter mineral quality. Iron Steel51(12): 8–14 (in Chinese).
3.
ErJMLiaoJLinJY (2000) Fuzzy neural networks-based quality prediction system for sintering process. IEEE Transactions on Fuzzy Systems8(3): 314–324.
4.
FanXHHuangXXChenXL, et al. (2016) Research and development of the intelligent control of iron ore sintering process based on fan frequency conversion. Ironmaking and Steelmaking43(7): 488–493.
5.
FanXHLongHMWangY, et al. (2006) Application of expert system for controlling sinter chemical composition. Ironmaking and Steelmaking33(3): 253–255.
GiriaBKRoyGG (2012) Mathematical modeling of iron ore sintering process using genetic algorithm. Ironmaking and Steelmaking39(1): 59–66.
8.
IliadisLJaneC (2017) Special issue: Engineering applications of neural networks. Neural Computing and Applications28(6): 1189–1191.
9.
LaitinenPJSaxénH (2007) A neural network based model of sinter quality and sinter plant performance indices. Ironmaking and Steelmaking34(2): 109–114.
10.
LiZPFanXHChenG, et al. (2017) Optimization of iron ore sintering process based on ELM model and multi-criteria evaluation. Neural Computing and Applications28(8): 2247–2253.
11.
LiuCJDingWFLiZ, et al. (2017) Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm. International Journal of Advanced Manufacturing Technology89(5): 2277–2285.
12.
LiuCXXieZHSunFR, et al. (2016) Optimization for sintering proportioning based on energy value. Applied Thermal Engineering103: 1087–1094.
13.
LvQHuangHHCaoLH, et al. (2014) Influence of FeO content of crude ore on sinter. Sinter Pelletizing39(5): 1–4 (in Chinese).
14.
NathNKMitraK (2005) Mathematical modeling and optimization of two-layer sintering process for sinter quality and fuel efficiency using genetic algorithm. Materials and manufacturing Processes20(3): 335–349.
15.
WangDLBaiYZhaoJY (2012) Robot manipulator calibration using neural network and a camera-based measurement system. Transactions of the Institute of Measurement and Control34(1): 105–121.
16.
WangJLDingJLCaoWD, et al. (2018) Neural network fuzzy control for enhancing the stability performance of quad-rotor helicopter. Transactions of the Institute of Measurement and Control40(11): 3333–3344.
17.
WuJXLiuC (2018) A new pattern based Petri net to model sintering production process. Transactions of the Institute of Measurement and Control40(9): 2771–2778.
18.
WuMChenXXCaoWH, et al. (2014) An intelligent integrated optimization system for the proportioning of iron ore in a sintering process. Journal of Process Control24(1): 182–202.
19.
WuMXuCHSheJH, et al. (2012) Neural-network-based integrated model for predicting burn-through point in lead–zinc sintering process. Journal of Process Control22(5): 925–934.
20.
WuMXuCHSheJH, et al. (2009) Intelligent integrated optimization and control system for lead-zinc sintering process. Control Engineering Practice17(2): 280–290.
21.
XiaoRBTaoZWChenTJ (2011) An analytical approach to the similarities between swarm intelligence and artificial neural network. Transactions of the Institute of Measurement and Control34(6): 736–745.
22.
YangWRyuCChoiS, et al. (2004) Modeling of combustion and heat transfer in an iron ore sinter bed with considerations of multiple solid phases. ISIJ International44(3): 492–499.
23.
ZhangHHuangCXZhaoG (2012) A prediction model for sinter quality based on the characteristic of auxiliary material’s movement. Journal of Iron and Steel Research24(4): 14–18 (in Chinese).
24.
ZhangJHXieAGShenFM (2007) Multi-objective optimization and analysis model of sintering process based on BP neural network. Journal of Iron and Steel Research, International14(2): 1–5.
