基于DBSVDD-RVR的多模态间歇过程质量变量在线软测量

doi:10.11949/0438-1157.20240333

摘要/Abstract

摘要：

现有的多模态间歇过程软测量未考虑过程数据的批次差异及过渡模态的复杂时变特性，影响了间歇过程模态识别的合理性及质量变量在线软测量的准确性。提出了一种基于双边界支持向量数据描述-相关向量回归（double boundary support vector data description-relevance vector regression，DBSVDD-RVR）的间歇过程质量变量在线软测量方法。依据间歇过程离线模态划分获得的各稳定及过渡模态历史数据，建立DBSVDD在线模态识别模型，并引入滑动窗，构建间歇过程在线模态识别策略，利用DBSVDD模型实现在线测量数据的模态识别；在此基础上，构建了基于超球体距离的数据相似度计算方法，选择过渡模态在线数据的相似建模数据集，建立过渡模态的即时学习RVR软测量模型，并依据历史数据建立各稳定模态的RVR软测量模型，实现间歇过程质量变量的在线软测量。青霉素发酵过程的实验结果表明，所提方法有效地提高了间歇过程模态识别的合理性和质量变量在线软测量的准确性。

关键词: 间歇式, 双边界支持向量数据描述, 在线模态识别, 模型, 预测

Abstract:

The existing multimode batch process soft sensor does not consider the batch difference of process data and the complex time-varying characteristics of transition modes, which affects the rationality of batch process mode identification and the accuracy of online soft sensing of quality variables. This paper proposes an online soft sensing method for batch process quality variables based on double boundary support vector data description-relevance vector regression (DBSVDD-RVR). According to the historical data of stable and transition modes obtained by offline mode partition of batch processes, the online mode identification model of DBSVDD was established. Then, the sliding window was introduced to construct the online mode identification strategy, and the online mode identification of batch data was realized by using DBSVDD model. On this basis, the data similarity calculation method based on hypersphere distance was constructed, and the similarity modeling data set of online data in transition mode was selected to establish the just-in-time learning RVR soft sensing model of transition mode. The RVR soft sensing model of each stable mode was established according to the historical data, and the online soft sensing of batch process quality variables was realized. The experimental results of penicillin fermentation process show that the proposed method effectively improves the rationality of mode identification and the accuracy of online soft sensing for quality variables in batch processes.

Key words: batchwise, double boundary support vector data description, online mode identification, model, prediction

中图分类号:

TP 274

李季, 王建林, 何睿, 周新杰, 王雯, 赵利强. 基于DBSVDD-RVR的多模态间歇过程质量变量在线软测量[J]. 化工学报, 2024, 75(9): 3231-3241.

Ji LI, Jianlin WANG, Rui HE, Xinjie ZHOU, Wen WANG, Liqiang ZHAO. DBSVDD-RVR based online soft sensing for quality variables in multimode batch processes[J]. CIESC Journal, 2024, 75(9): 3231-3241.

图/表 10

图1 基于DBSVDD的间歇过程在线模态识别模型构建示意图

Fig.1 Constructing of mode identification model for batch processes based on DBSVDD

图2 基于DBSVDD的间歇过程模态识别方法流程图

Fig.2 The flow chart of mode identification method for batch processes based on DBSVDD

图3 基于DBSVDD-RVR的多模态间歇过程质量变量在线软测量流程图

Fig.3 Flow chart of online soft sensing of quality variables for multimode batch processes based on DBSVDD-RVR

表1 青霉素发酵过程变量

Table 1 Process variables of penicillin fermentation

No.	过程变量（单位）	No.	过程变量（单位）
1	通风率（L/h）	7	二氧化碳浓度（mmol/L）
2	搅拌功率（W）	8	产热量（kcal/h）
3	底物流加速率（L/h）	9	加酸流速（ml/h）
4	底物流温度（K）	10	加碱流速（ml/h）
5	溶解氧浓度（mol/L）	11	青霉素浓度（g/L）
6	反应器体积（L）

表2 各DBSVDD模型的内外边界

Table 2 The inner and outer boundaries of each DBSVDD model

DBSVDD 模型序号	内边界	外边界	构建模型所用模态数据序号
1	0.9283	1.0591	1、2
2	0.9513	1.0334	2、3
3	0.9513	1.0055	3、4
4	0.9840	0.9951	4、5

图4 不同方法对第1个测试批次的在线模态识别结果

Fig.4 Mode identification results of the first online batch

表3 不同方法对5个测试批次的在线模态识别结果

Table 3 Mode identification results for 5 test batches by different methods

方法	测试批次序号	第1模态	第2模态	第3模态	第4模态	第5模态
时间标签	1~5	[1,42]	[43,50]	[51,137]	[138,186]	[187,400]
DBSVDD	1	[1,41]	[42,48]	[49,142]	[143,189]	[190,400]
	2	[1,43]	[44,50]	[51,150]	[151,185]	[186,400]
	3	[1,40]	[41,48]	[49,150]	[151,192]	[193,400]
	4	[1,40]	[41,48]	[49,150]	[151,192]	[193,400]
	5	[1,41]	[42,48]	[49,137]	[138,189]	[190,400]

图5 第1个测试批次不同方法的青霉素浓度在线软测量结果

Fig.5 Online soft sensing results of penicillin concentration for different methods in the first test batch

图6 第1个测试批次不同方法的青霉素浓度在线软测量误差

Fig.6 Online soft sensing error of penicillin concentration for different methods in the first test batch

表4 不同方法对5个测试批次青霉素浓度软测量结果的RMSE和R2

Table 4 RMSE and R2 of penicillin concentration soft sensing results for 5 test batches by different methods

批次序号	DS-RVR		TS-RVR		本文方法
批次序号	RMSE	R²	RMSE	R²	RMSE	R²
1	0.1143	0.9114	0.0194	0.9981	0.0136	0.9991
2	0.1729	0.8715	0.0137	0.9990	0.0078	0.9997
3	0.1451	0.9084	0.0142	0.9990	0.0090	0.9996
4	0.1447	0.9095	0.0239	0.9971	0.0186	0.9982
5	0.1447	0.9056	0.0204	0.9978	0.0152	0.9988
average	0.1518	0.8988	0.0181	0.9982	0.0126	0.9991

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