基于注意力LSTM的多阶段发酵过程集成质量预测

考虑到发酵过程的动态特征对阶段划分的影响,为提高模型预测精度,提出一种基于注意力LSTM的多阶段发酵过程质量预测方法.首先,将原始三维数据沿批次展开,对每个时间片矩阵进行偏最小二乘(PLS)分析得到表征过程变量的得分矩阵和表征质量变量的得分矩阵,采用仿射传播(AP)聚类算法将联合得分矩阵进行聚类,实现第1步划分;然后,...

Integrated quality prediction of multi-stage fermentation process based on attention LSTM

In order to consider the impact of dynamic features of the fermentation process on stage division and improve the prediction accuracy, a quality prediction method based on attention long short-term memory(LSTM) is proposed. Firstly, the original 3D data are unfolded along the batch direction. Partial least square(PLS) analysis is performed on each time slice matrix to obtain the score matrix of process variables and quality variables. The joint score matrices are clustered using the affinity propagation(AP) algorithm. Then the encoder-decoder model is used to extract the dynamic characteristics of the process dynamics, and the AP algorithm is used for the second division. Finally, the production process is divided into different stable phases and transition phases through the comprehensive analysis of the two-step division results. The LSTM integrated quality prediction model is established in each stage after the division. Penicillin fermentation simulation data and E. coli production data are tested, and the results demonstrate the feasibility and effectiveness of the proposed method.