According to the characteristics of sintering process, a sintering end-point prediction system based on gradient boosting decision tree (GBDT) algorithm and decision rules is proposed in this paper. The on-line parameters of the sintering machine, which can characterize the change of the properties of the sintered raw materials in real time, were selected as the input of the model. The soft measurement results of the burn-through point position and temperature were selected as the output. The problem of establishing a system model based on the data collected in the sintering process to dynamically predict the state of burn through point (BTP) was solved. With the combination of process knowledge and several feature selection methods, the important characteristic variables related to the BTP were screened out. the algorithm of GBDT was used to establish the prediction model of BTP and burn through temperature (BTT). The parameters of the ensemble algorithm were optimized by using the methods of grid search and cross-validation, and the system model based on training data was established. On this basis, the corresponding decision model was added to the output of the prediction model, and the prediction accuracy of the system was improved. The establishment process of system model is introduced in detail. The operation results show that the system has better performance.
Stable production processes, high efficiency and good product quality are the main objectives of the sintering industry. Taking the whole sintering production process as the research object, cutting-edge intelligent algorithms and classical sintering theories are applied to establish a sintering batch optimization model, a sinter layer permeability prediction model, a sintering end point optimization control model and a sinter quality prediction and evaluation model respectively, supported by a large amount of historical data, so that the model has reasonably accurate results as well as strong practicality. At the same time, a sintering whole process intelligent manufacturing system based on the above four models is constructed using Java and Python programming languages. The system realizes the functions of real-time analysis of key parameters, intelligent early warning, decision optimization and fault tracing, which provides reasonable line optimization suggestions for field operators, and significantly improves the intelligence and production efficiency of sintering process.
In the long process of iron and steel, the sintering process has the largest amount of flue gas emissions, many types of pollutants and high concentrations. The source control of SO 2 and NOx in sintering flue gas through digital technology has become a new emission reduction technology. In this study, the BP neural network model (BP-NN) is optimized by using the particle swarm algorithm (PSO) to form the PSO-BPNN model, which effectively improves the characteristics of BP-NN with slow convergence speed and easily falls into local minima, and improves the learning ability and generalization. The test results show that the PSO-BP-NN algorithm not only has fast convergence speed and high prediction accuracy, but also has smaller training and inspection errors. In addition, this model combines process theory and feature engineering selection of parameters, which effectively improves the accuracy of the model and the interpretability of the results based on the linkage of process knowledge, and has certain analytical significance for the source management and post-treatment of sintered flue gas.
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