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Research on Endpoint Prediction Model of Basic Oxygen Furnace Steelmaking Based on Relevance Vector Machine

Author: ZhaoYao
Tutor: HanMin
School: Dalian University of Technology
Course: Control Theory and Control Engineering
Keywords: BOF steelmaking Endpoint prediction Relevance vector machine Robustness Particle Swarm Optimization
CLC: O242.1
Type: Master's thesis
Year: 2010
Downloads: 229
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BOF endpoint carbon content and temperature forecast is an important part in the converter steelmaking process control , the forecast accuracy is directly related to the ability to finally get to meet the production procedures required of molten steel . Due to the the converter steelmaking is a complex nonlinear production process , contains a large number of physical and chemical reactions , it is difficult to adopt traditional mechanistic model accurately describe them . In order to improve the forecast accuracy of the end of this paper, the characteristics of the steelmaking actual production data , the use of the improved relevance vector machine, and particle swarm optimization smart BOF endpoint prediction model . Steelmaking complex field conditions , more disturbance , so the actual production will inevitably contain noise or outliers in the data , while the traditional vector machine model is susceptible to outliers , poor robustness . To solve this problem , we propose a robust vector machine , the introduction of independent noise variance coefficient for each training sample model hyper parameters iteration formula is derived based on the Bayesian evidence procedure . Outliers corresponding noise variance coefficient model training process with the prediction error increases gradually reduced , enabling the detection of outliers and removed to improve the robustness of the model and calculation accuracy . In addition , for the relevance vector machine regression calculation results were affected by the nuclear parameters affect the larger problem , the article also proposed a nuclear parameter vector machine based on particle swarm algorithm adaptive optimization method . Kernel function corresponding to the different input characteristic nuclear parameter variable , every certain number of iteration steps using the particle swarm algorithm to optimize the nuclear parameters in the training process , thereby improving the accuracy of the calculation of the relevance vector machine , according to the parameter optimization results can also analyze the correlation between each of the input variable and output . On this basis , the paper proposes the use of robust relevance vector machine model with the nuclear parameters adaptive optimization method established the BOF steelmaking endpoint carbon content and temperature prediction model of dynamic phase of molten steel , and simulation using actual production data of the steel mills . The results show that the model has a better hit rate and accuracy , and have certain guiding significance on the the BOF steelmaking actual production .

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CLC: > Mathematical sciences and chemical > Mathematics > Computational Mathematics > Mathematical modeling, approximate calculation > Mathematical modeling
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