运用趋势混沌预测模型预测硫化矿石堆自热升温过程

发布时间：2018-12-26 07:09

【摘要】：为准确预测硫化矿石堆自热过程中的温度变化,根据硫化矿石堆的氧化升温特征建立温度趋势预测模型,在对趋势预测结果的差值序列进行混沌识别后,采用加权一阶局域法建立混沌预测模型。再将两者叠加构成趋势-混沌组合预测模型,并应用该模型对模拟矿石堆温度进行预测。研究结果表明:硫化矿石堆温度与时间的关系符合指数模型特征,利用此模型可对矿石堆温度变化趋势进行预测;趋势预测差值序列的关联维数为分数,Kolmogorov熵大于0,表明趋势预测差值序列中含有混沌项,可对其进行混沌预测;预测结果证实趋势-混沌组合预测模型具有很高的预测精度,能够很好地适用于硫化矿石堆自燃火灾的早期预测。
[Abstract]:In order to accurately predict the temperature change in the self-heating process of sulphide ore heap, a temperature trend prediction model was established according to the oxidation heating characteristics of sulfide ore heap. After chaotic identification of the difference sequence of the trend prediction result, the temperature trend prediction model was established. The weighted first order local method is used to establish the chaotic prediction model. Then the two models are superimposed to form a trend-chaotic combination prediction model, and the model is used to predict the simulated ore heap temperature. The results show that the relationship between temperature and time of sulphide ore heap accords with the characteristics of exponential model, and the change trend of ore heap temperature can be predicted by using this model. The correlation dimension of the trend prediction difference sequence is fraction, and the Kolmogorov entropy is greater than 0, which indicates that the trend prediction difference sequence contains chaotic terms, which can be used for chaotic prediction. The prediction results show that the trend-chaotic combination prediction model has a high prediction accuracy and can be applied to early prediction of spontaneous combustion of sulfide ore heap.
【作者单位】：中南大学资源与安全工程学院;辽宁工程技术大学矿山热动力灾害与防治教育部重点实验室;
【基金】：国家自然科学基金资助项目(51074181,51304238) 矿山热动力灾害与防治教育部重点实验室基金资助项目(JSK200206)~~
【分类号】：TD752.2

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