草酸钴合成过程是钴湿法冶炼的关键单元操作,其粒度分布是重要的质量指标,然而难以在线实时测量.同时,草酸钴合成过程通常存在非线性、多约束和慢时变特征.因此,提出一种集成慢特征分析(slow feature analysis,SFA)与最小二乘支持向量回归(least square support vector regression,LSSVR)的草酸钴粒度预报模型对草酸钴合成过程质量指标实现在线测量.在该方法中,首先,SFA方法可以有效地捕获过程的慢特征向量,解决慢时变问题;然后,利用LSSVR方法建立慢特征与粒度之间的非线性关系模型,进而实现质量指标在线预报.最后,应用非线性的数值案例以及草酸钴合成过程数据,验证该方法的有效性.实验结果显示:相较于单一的径向基函数神经网络(radial basis function neural network,RBFNN)、LSSVR预测模型以及SFA与NN相结合的预报模型,所提方法在数值案例中的预测精度分别提升了13.31%、2.26%、1.72%;在草酸钴合成过程中的预测精度分别提升了13.27%、9.96%、8.92%.
Particle size prediction of cobalt oxalate synthesis process based on slow feature analysis and least squares support vector regression
Cobalt oxalate synthesis process is a key unit operation of cobalt hydrometallurgy,and its particle size distribution is an important quality index.However,it is difficult to measure online in real-time.At the same time,the synthesis process of cobalt oxalate usually has the characteristics of nonlinearity,multiple constraints and slow time variation.In this paper,a cobalt oxalate particle size prediction model integrating slow feature analysis and least square support vector regression was proposed to achieve online measurement of particle size cobalt oxalate synthesis process.In this method,first,the SFA method can effectively capture the slow feature vector of the process and solve the slow time-varying problem.Then,the LSSVR method is used to establish a nonlinear relationship model between slow features and particle size,and then realize the online prediction of quality indicators.Finally,a nonlinear numerical case and data on the synthesis process of cobalt oxalate are used to verify the effectiveness of the proposed method.The experimental results show that compared with the single radial basis function neural network(RBFNN),LSSVR prediction model and the combined prediction model of SFA and NN,the prediction accuracy of the proposed method in numerical cases is improved by 13.31%,2.26%and 1.72%respectively.The prediction accuracy of cobalt oxalate synthesis is improved by 13.27%,9.96%and 8.92%,respectively.
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