基于卷积和长短期记忆网络的地浸开采铀浓度预测研究

Uranium Concentration Prediction for In-situ Leaching Based on Convolution and Long Short-term Memory Networks

文章通过集成经验模态分解(Empirical Mode Decomposition,EMD)、卷积神经网络(Convolutional Neural Networks,CNN)、长短期记忆网络(Long Short-Term Memory,LSTM)、傅里叶变换,提出了一种新型地浸单元浸出液铀浓度预测方法。该方法将浸出液铀浓度监测值时间序列使用EMD进行分解,分解为趋势项、周期项和随机项。通过构建CNN+LSTM网络,并结合傅里叶变换和多项式拟合对铀浓度趋势项、周期项和随机项进行预测,3者预测之和作为铀浓度预测结果。实证结果表明:EMD能够有效分解铀浓度时间序列,模型拟合度比未进行EMD分解的模型提升超50%;基于EMD、CNN+LSTM和傅里叶变换的集成方法预测精度良好,预测的平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)为0.348,与LSTM、反向传播(Back Propagation,BP)和门控循环网络(Gate Recurrent Unit,GRU)等模型相比最高提升超80%。文章提出的集成方法能够准确预测浸采单元铀浓度变化,解决了原有方法和模型无法对非线性、非平稳铀浓度序列进行准确预测的问题,从而为地浸矿山生产规划提供技术支持,并有助于提升中国铀矿山的数字化、信息化程度。

This paper presents a new method for predicting uranium concentration in leachate from in-situ leaching units by integrating empirical mode decomposition(EMD),convolutional neural network(CNN),long short-term memory(LSTM)and Fourier transform.The method decomposes the time series of leachate uranium concentration monitoring values into trend,periodic and random terms by EMD.By constructing CNN+LSTM neural network and combining Fourier transform and polynomial fitting to predict the trend terms,periodic terms and random terms of uranium concentration,the sum of the three predictions act as the result of uranium concentration prediction.The empirical results show that:1)EMD can effectively decompose the uranium concentration time series,and the model fit is over 50%better than the model without EMD decomposition;2)The integrated method based on EMD,CNN+LSTM,and Fourier transform has good prediction accuracy,with an average absolute percentage error(MAPE)of 0.348,which is the highest improvement of over 80%compared to models such as LSTM,back propagation(BP)and Gate Recurrent Unit(GRU).The integrated method proposed in this paper can accurately predict the uranium concentration variation in the leaching unit,solving the problem that the original method and model cannot accurately predict the nonlinear,non-stationary uranium concentration series,thus providing technical support for production planning of ground leaching mines and it helps to enhance the digitization and informatization level of China's uranium industry.