基于ISTA-LSTM模型的间歇过程质量预测
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作者单位:

兰州理工大学

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中图分类号:

TP277

基金项目:

国家自然科学基金(62263021);甘肃省科技计划资助(21YF5GA072, 21JR7RA206)


Batch process quality prediction based on ISTA-LSTM model
Author:
Affiliation:

Lanzhou University of Technology

Fund Project:

the National Natural Science Foundation of China (No.62263021);the Science and Technology Project of Gansu Province(21YF5GA072, 21JR7RA206)

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    摘要:

    为了考虑过程变量与质量变量的相关性,解决间歇过程的时序性和动态特性导致预测精度不高的问题,提出一种基于改进时空注意力-长短时记忆神经网络(Improved Spatial and Temporal Attention Long Short-Term Memory, ISTA-LSTM)的模型进行间歇过程质量预测。首先对间歇过程的三维数据按批次-变量法展开成二维矩阵,对二维数据采用Min-max方法标准化;然后使用偏最小二乘(PLS)方法对原始数据降维,提取数据的特征信息,基于时间注意力和空间注意力构建了编码-解码器结构的双层LSTM网络,利用注意力机制自适应地学习神经网络参数,关注每一个过程变量对质量变量的重要性并分配相应的注意值,从而保留所有输入的必要信息,采用带交叉验证的网格搜索算法对预测模型进行超参数寻优,并建立了预测模型;最后在青霉素发酵仿真平台上进行了实验验证,实验结果表明本文所提模型对间歇过程质量预测的可行性和有效性。

    Abstract:

    In order to consider the correlation between process variables and quality variables, and solve the problem that the timing and dynamic characteristics of batch process lead to low prediction accuracy, a model based on Improved Spatial and Temporal Attention Long short-term Memory (ISTA-LSTM) is proposed to predict intermittent process quality. First, three-dimensional batch process data are unfolded into two-dimensional matrix according to batch-variable method. The two-dimensional data are normalized by min-max method. Secondly, partial least squares (PLS) method is used to reduce the dimension of the original data and extract the features of the data. Based on temporal attention and spatial attention, a double-layer LSTM network with encoder–decoder structure is constructed. In order to retain all the necessary input information, the neural network parameters are learned adaptively by the attention mechanism. Pay attention to the importance of each process variable to the quality variable and assign the corresponding attention value. Then, grid search algorithm with cross verification is used to optimize the prediction model by hyperparameters, and the prediction model is established. Finally, the proposed algorithm model is verified on the penicillin fermentation simulation experimental platform. The experimental results show that the model proposed in this paper is feasible and effective for batch process quality prediction.

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  • 收稿日期:2022-05-20
  • 最后修改日期:2023-06-20
  • 录用日期:2022-09-20
  • 在线发布日期: 2022-09-23
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