Soft sensor of wet ball mill load parameter based on domain adaptation with manifold regularization

doi:10.11949/j.issn.0438-1157.20170918

Abstract

Abstract:

Aiming at the challenging problems such as the measurement of key load parameters of ball mill under multi-operating conditions, a soft sensor model based on domain adaptation with manifold regularization (domain adaptation with manifold regularization,DAMR) for measuring wet ball mill load parameters is proposed. Firstly, the feature transformation matrix is found by using the integrated manifold constraints, the maximum variance and the maximum mean discrepancy. Then, the feature information of the source domain and the target domain are projected into the common subspace. Finally, the model established in the subspace is used to predict critical load parameters. The results show that the proposed method can predict the critical load parameters of wet ball mill under the unknown condition with high precision, and this method has some reference value for the soft sensing of mlti-operating conditions in process industry and process monitoring.

Key words: transfer learning, manifold regularization, maximum mean discrepancy, wet ball mill load parameters, integration, process control, prediction

摘要：

针对多工况条件下球磨机关键负荷参数测量面临的复杂性问题，提出基于流形正则化域适应（domain adaptation with manifold regularization，DAMR）湿式球磨机负荷参数软测量的方法。该方法首先采用集成流形约束、最大方差及最大均值差异寻找特征变换矩阵，然后，将源建模领域和未建模领域的特征信息投射到公共子空间，最后，在子空间建立模型得到球磨机关键负荷参数的预测值。实验结果表明该方法能以较高的精度实现未知工况下湿式球磨机关键负荷参数的预测，且该方法对于流程工业多工况软测量和过程监控研究有一定的参考价值。

关键词: 迁移学习, 流形正则化, 最大均值差异, 湿式球磨机负荷参数, 集成, 过程控制, 预测

CLC Number:

TP29

DU Yonggui, LI Sisi, YAN Gaowei, CHENG Lan. Soft sensor of wet ball mill load parameter based on domain adaptation with manifold regularization[J]. CIESC Journal, 2018, 69(3): 1244-1251.

杜永贵, 李思思, 阎高伟, 程兰. 基于流形正则化域适应湿式球磨机负荷参数软测量[J]. 化工学报, 2018, 69(3): 1244-1251.

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