生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。

Dynamic Numerical Model for Predicting Air Input in Bio-oxidation Pretreatment

Accurate prediction of air input in bio-oxidation pretreatment is of great significance for improving the gold extraction rate , saving energy and reducing consumption. The continuity equation and motion equation of the gas pipe flow are used as governing equations, the Preissmann implicit scheme method is adopted as the difference method. At the same time, according to the principle of Ensemble Kalman filter (EnKF) algorithm, a state space model of air input and pressure is constructed. The results show that the predicted results of the air input model based on the gas pipe flow control equation are in ideal agreement with the observed values of the actual air input. Compared with the traditional static prediction method, the EnKF assimilation method introduces real-time observation values and model parameter updates, which effectively improves the prediction accuracy of the air input, and its mean absolute error, mean relative error and root mean square error are significantly reduced. It can be seen that the prediction model established based on the gas pipe flow control equation combined with the EnKF assimilation method is an effective method to improve the prediction accuracy of the air input of the bio-oxidation tank.