Quality-related fault monitoring of multi-phase fermentation process based on joint canonical variable matrix

doi:10.11949/0438-1157.20211294

Abstract

Abstract:

In order to consider the influence of the quality variables and dynamic characteristics of the fermentation process on the stage division, a multi-phase fermentation process quality-related fault monitoring method based on the joint canonical variable matrix is?proposed. Firstly, the 3D data were unfolded along the batch direction. Canonical correlation analysis (CCA) was performed on each time slice matrix to obtain the joint canonical variable matrices, which were equipped with process variables and quality variables, and K-mean algorithm was used to realize first partition. Then slow feature analysis (SFA) was used to extract the slow characteristics of the process dynamics, and the K-mean algorithm was used for the second partition. Finally, the production process was divided into different stable phases and transition phases through the comprehensive analysis of the two-step division results. The CCA monitoring model was established in each phase after partition for quality-related fault detection. According to the changes of static and dynamic characteristics, this method can accurately distinguish the strong dynamics and the phase boundaries by a two-step partition, also e?ectively improve the accuracy of quality-related fault monitoring. The feasibility and effectiveness of the proposed algorithm were illustrated by a penicillin simulation platform and an industrial application of E. coli fermentation, respectively.

Key words: fermentation, batch-wise, multi-phase, joint canonical variable matrix, canonical correlation analysis, fault monitoring, algorithm

摘要：

为考虑发酵过程的质量变量和动态特征对于阶段划分的影响，提出了一种基于联合典型变量矩阵的多阶段发酵过程质量相关故障监测方法。首先，将历史三维数据沿批次方向展开，对每个时间片矩阵进行典型相关分析（canonical correlation analysis， CCA），得到融合过程变量和质量变量信息的联合典型变量矩阵，对其进行K均值聚类，实现基于静态特征的第1步划分；然后采用慢特征分析（slow feature analysis， SFA）算法提取表征过程动态性的慢特征，对其进行聚类实现第2步划分。最后综合分析两步划分结果，将生产过程划分为不同的稳定阶段和过渡阶段，并在划分的子阶段中分别建立CCA监测模型进行质量相关故障监测。该方法通过静态和动态特征的变化实现两步划分，准确区分强动态变化与阶段切换，有效提高质量相关的故障监测模型精度。青霉素仿真平台与大肠杆菌实际生产数据验证了所提方法的可行性和有效性。

关键词: 发酵, 间歇式, 多阶段, 联合典型变量矩阵, 典型相关分析, 故障监测, 算法

CLC Number:

TP 273

Xuejin GAO, Zihe HE, Huihui GAO, Yongsheng QI. Quality-related fault monitoring of multi-phase fermentation process based on joint canonical variable matrix[J]. CIESC Journal, 2022, 73(3): 1300-1314.

高学金, 何紫鹤, 高慧慧, 齐咏生. 基于联合典型变量矩阵的多阶段发酵过程质量相关故障监测[J]. 化工学报, 2022, 73(3): 1300-1314.

Figures/Tables 16

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参数	取值范围	参数	取值范围
初始值		控制参数
产生热/kcal	0	底物流温度/K	296~297
反应物体积/L	100~105	搅拌功率/W	29~31
青霉素浓度/(g/L)	0	pH	5~5.05
底物浓度/(g/L)	14~18	底物流加速率/ (L/h)	0.039~0.045
二氧化碳浓度/ (mmol/L)	0.5~1	通风速率/(L/h)	8~9
菌体浓度/(g/L)	0.1	反应器温度/K	298~299
溶解氧浓度/(mmol/L)	1~1.2
反应器温度/K	298~299
pH	4.5~5.5

参数	取值范围	参数	取值范围
初始值		控制参数
产生热/kcal	0	底物流温度/K	296~297
反应物体积/L	100~105	搅拌功率/W	29~31
青霉素浓度/(g/L)	0	pH	5~5.05
底物浓度/(g/L)	14~18	底物流加速率/ (L/h)	0.039~0.045
二氧化碳浓度/ (mmol/L)	0.5~1	通风速率/(L/h)	8~9
菌体浓度/(g/L)	0.1	反应器温度/K	298~299
溶解氧浓度/(mmol/L)	1~1.2
反应器温度/K	298~299
pH	4.5~5.5

变量编号	变量名称	变量单位	变量编号	变量名称	变量单位
x₁	通风速率	L/h	x₇	pH	—
x₂	搅拌功率	W	x₈	反应器温度	K
x₃	底物流温度	K	x₉	产生热	kcal/h
x₄	溶解氧浓度	%	x₁₀	冷水流加速率	L/h
x₅	反应物体积	L	y₁	菌体浓度	g/L
x₆	CO₂浓度	mmol/L	y₂	产物浓度	g/L

变量编号	变量名称	变量单位	变量编号	变量名称	变量单位
x₁	通风速率	L/h	x₇	pH	—
x₂	搅拌功率	W	x₈	反应器温度	K
x₃	底物流温度	K	x₉	产生热	kcal/h
x₄	溶解氧浓度	%	x₁₀	冷水流加速率	L/h
x₅	反应物体积	L	y₁	菌体浓度	g/L
x₆	CO₂浓度	mmol/L	y₂	产物浓度	g/L

故障批次	故障变量	故障类型	幅值/%	开始时刻/h	终止时刻/h
1	搅拌速率	阶跃故障	10	200	400
2	底物流加速率	斜坡故障	15	200	400