Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan, Hebei, China.
Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, China.
Comput Intell Neurosci. 2022 Apr 12;2022:3343427. doi: 10.1155/2022/3343427. eCollection 2022.
The quality control process for sintered ore is cumbersome and time- and money-consuming. When the assay results come out and the ratios are found to be faulty, the ratios cannot be changed in time, which will produce sintered ore of substandard quality, resulting in a waste of resources and environmental pollution. For the problem of lagging sinter detection results, Long Short-Term Memory and Genetic Algorithm-Recurrent Neural Networks prediction algorithms were used for comparative analysis, and the article used GA-RNN quality prediction model for prediction. Through correlation analysis, the chemical composition of the sintered raw material was determined as the input parameter and the physical and metallurgical properties of the sintered ore were determined as the output parameters, thus successfully establishing a GA-RNN-based sinter quality prediction model. Based on 150 sets of original data, 105 sets of data were selected as the training sample set and 45 sets of data were selected as the test sample set. The results obtained were compared to the real value with an average prediction error of 1.24% for the drum index, 0.92% for the low-temperature reduction chalking index (RDI), 0.95% for the reduction index (RI), 0.40% for the load softening temperature T, and 0.43% for the load softening temperature T, with all within the running time thresholds. The study of this model enables the prediction of the quality of sintered ore prior to sintering, thus improving the yield of sintered ore, increasing corporate efficiency, saving energy, and reducing environmental pollution.
烧结矿质量控制过程繁琐，费时费力。当化验结果出来，发现比例有误时，无法及时调整比例，会生产出质量不达标的烧结矿，造成资源浪费和环境污染。针对烧结检测结果滞后的问题，采用长短时记忆和遗传算法-递归神经网络预测算法进行对比分析，本文采用 GA-RNN 质量预测模型进行预测。通过相关分析，确定烧结原料的化学成分作为输入参数，烧结矿的物理和冶金性能作为输出参数，成功建立了基于 GA-RNN 的烧结矿质量预测模型。基于 150 组原始数据，选择 105 组数据作为训练样本集，45 组数据作为测试样本集。将得到的结果与实际值进行比较，平均预测误差为：转鼓指数 1.24%，低温还原粉化指数（RDI）0.92%，还原指数（RI）0.95%，软化开始温度 T 0.40%，软化终了温度 T 0.43%，均在运行时间阈值内。该模型的研究实现了烧结矿质量的提前预测，提高了烧结矿的产量，提高了企业效率，节约了能源，减少了环境污染。
