CIESC Journal ›› 2024, Vol. 75 ›› Issue (9): 3231-3241.DOI: 10.11949/0438-1157.20240333
• Process system engineering • Previous Articles Next Articles
Ji LI1(), Jianlin WANG1(
), Rui HE1, Xinjie ZHOU1, Wen WANG2(
), Liqiang ZHAO1
Received:
2024-03-22
Revised:
2024-05-02
Online:
2024-10-10
Published:
2024-09-25
Contact:
Jianlin WANG, Wen WANG
李季1(), 王建林1(
), 何睿1, 周新杰1, 王雯2(
), 赵利强1
通讯作者:
王建林,王雯
作者简介:
李季(1987—),男,博士研究生,2021400224@mail.buct.edu.cn
基金资助:
CLC Number:
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.
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.
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) |
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) |
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 |
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 |
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 | [ | [43,50] | [51,137] | [138,186] | [187,400] |
DBSVDD | 1 | [ | [42,48] | [49,142] | [143,189] | [190,400] |
2 | [ | [44,50] | [51,150] | [151,185] | [186,400] | |
3 | [ | [41,48] | [49,150] | [151,192] | [193,400] | |
4 | [ | [41,48] | [49,150] | [151,192] | [193,400] | |
5 | [ | [42,48] | [49,137] | [138,189] | [190,400] |
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 | [ | [43,50] | [51,137] | [138,186] | [187,400] |
DBSVDD | 1 | [ | [42,48] | [49,142] | [143,189] | [190,400] |
2 | [ | [44,50] | [51,150] | [151,185] | [186,400] | |
3 | [ | [41,48] | [49,150] | [151,192] | [193,400] | |
4 | [ | [41,48] | [49,150] | [151,192] | [193,400] | |
5 | [ | [42,48] | [49,137] | [138,189] | [190,400] |
批次序号 | DS-RVR | TS-RVR | 本文方法 | |||
---|---|---|---|---|---|---|
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 |
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 | |||
---|---|---|---|---|---|---|
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 |
1 | Chang P, Lu R W. Process monitoring of batch process based on overcomplete broad learning network[J]. Engineering Applications of Artificial Intelligence, 2021, 99: 104139. |
2 | Qiu K P, Wang J L, Wang R T, et al. Soft sensor development based on kernel dynamic time warping and a relevant vector machine for unequal-length batch processes[J]. Expert Systems with Applications, 2021, 182: 115223. |
3 | 王雅琳, 潘雨晴, 刘晨亮. 基于GSA-LSTM动态结构特征提取的间歇过程监测方法[J]. 化工学报, 2022, 73(9): 3994-4002. |
Wang Y L, Pan Y Q, Liu C L. Intermittent process monitoring based on GSA-LSTM dynamic structurefeature extraction[J]. CIESC Journal, 2022, 73(9): 3994-4002. | |
4 | Zhu J L, Wang Y Q, Zhou D H, et al. Batch process modeling and monitoring with local outlier factor[J]. IEEE Transactions on Control Systems Technology, 2019, 27(4): 1552-1565. |
5 | Yuan X F, Huang B, Wang Y L, et al. Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE[J]. IEEE Transactions on Industrial Informatics, 2018, 14(7): 3235-3243. |
6 | Yamaguchi T, Yamashita Y. Quality prediction for multi-grade batch process using sparse flexible clustered multi-task learning[J]. Computers & Chemical Engineering, 2021, 150: 107320. |
7 | 褚菲, 彭闯, 贾润达, 等. 基于多尺度核JYMKPLS迁移模型的间歇过程产品质量的在线预测方法[J]. 化工学报, 2021, 72(4): 2178-2189. |
Chu F, Peng C, Jia R D, et al. Online prediction method of batch process product quality based on multi-scale kernel JYMKPLS transfer model[J]. CIESC Journal, 2021, 72(4): 2178-2189. | |
8 | Huo X, Hao K R, Chen L, et al. A dynamic soft sensor of industrial fuzzy time series with propositional linear temporal logic[J]. Expert Systems with Applications, 2022, 201: 117176. |
9 | 周新杰, 王建林, 艾兴聪, 等. 基于IDPC-RVM的多模态间歇过程质量变量在线预测[J]. 化工学报, 2022, 73(7): 3120-3130. |
Zhou X J, Wang J L, Ai X C, et al. IDPC-RVM based online prediction of quality variables for multimode batch processes[J]. CIESC Journal, 2022, 73(7): 3120-3130. | |
10 | Yu W K, Zhao C H, Huang B. Stationary subspace analysis-based hierarchical model for batch processes monitoring[J]. IEEE Transactions on Control Systems Technology, 2020, 29(1): 444-453. |
11 | 赵春晖, 余万科, 高福荣. 非平稳间歇过程数据解析与状态监控—回顾与展望[J]. 自动化学报, 2020, 46(10): 2072-2091. |
Zhao C H, Yu W K, Gao F R. Data analytics and condition monitoring methods for nonstationary batch processes—current status and future[J]. Acta AutomaticaSinica, 2020, 46(10): 2072-2091. | |
12 | Qiu K P, Wang J L, Zhou X J, et al. Soft sensor framework based on semisupervised just-in-time relevance vector regression for multiphase batch processes with unlabeled data[J]. Industrial & Engineering Chemistry Research, 2020, 59(44): 19633-19642. |
13 | Jin H P, Chen X G, Yang J W, et al. Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes[J]. Computers & Chemical Engineering, 2014, 71: 77-93. |
14 | Zhu J L, Gao F R. Improved nonlinear quality estimation for multiphase batch processes based on relevance vector machine with neighborhood component variable selection[J]. Industrial & Engineering Chemistry Research, 2018, 57(2): 666-676. |
15 | Wang J L, Qiu K P, Wang R T, et al. Development of soft sensor based on sequential kernel fuzzy partitioning and just-in-time relevance vector machine for multiphase batch processes[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-10. |
16 | Zhao C H, Sun Y X. Step-wise sequential phase partition (SSPP) algorithm based statistical modeling and online process monitoring[J]. Chemometrics & Intelligent Laboratory Systems, 2013, 125: 109-120. |
17 | Zhang S M, Zhao C H, Wang S, et al. Pseudo time-slice construction using a variable moving window K nearest neighbor rule for sequential uneven phase division and batch process monitoring[J]. Industrial & Engineering Chemistry Research, 2017, 56(3): 728-740. |
18 | Dong W W, Yao Y, Gao F R. Phase analysis and identification method for multiphase batch processes with partitioning multi-way principal component analysis (MPCA) model[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1121-1127. |
19 | Luo L J. Trajectory-based phase partition and multiphase multilinear models for monitoring and quality prediction of multiphase batch processes[J]. Journal of Chemometrics, 2018, 32(7): e3013. |
20 | Peng K X, Zhang K, You B, et al. A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes[J]. IEEE Transactions on Industrial Electronics, 2016, 63(4): 2615-2624. |
21 | Hui Y Y, Zhao X Q. Multi-phase batch process monitoring based on multiway weighted global neighborhood preserving embedding method[J]. Journal of Process Control, 2018, 69: 44-57. |
22 | Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496. |
23 | 谭帅, 常玉清, 王福利, 等. 基于GMM的多模态过程模态识别与过程监测[J]. 控制与决策, 2015, 30(1): 53-58. |
Tan S, Chang Y Q, Wang F L, et al. Mode identification and process monitoring for multiple mode processes based on GMM[J]. Control and Decision, 2015, 30(1): 53-58. | |
24 | Zheng Y, Wang Y, Yan H L, et al. Density peaks clustering‐based steady/transition mode identification and monitoring of multimode processes[J]. The Canadian Journal of Chemical Engineering, 2020, 98: 2137-2149. |
25 | Luo L J, Bao S Y, Mao J F, et al. Fuzzy phase partition and hybrid modeling based quality prediction and process monitoring methods for multiphase batch processes[J]. Industrial & Engineering Chemistry Research, 2016, 55(14): 4045-4058. |
26 | Zhang S M, Bao X L. Phase partition and online monitoring for batch processes based on Harris hawks optimization[J]. Control Engineering Practice, 2023, 138: 105554. |
27 | 周阅昇, 熊伟丽. 基于迁移成分分析的发酵过程集成软测量建模[J]. 系统仿真学报, 2023, 35(3): 623-631. |
Zhou Y S, Xiong W L. Integrated soft sensor for fermentation process based on transfer component analysis[J]. Journal of System Simulation, 2023, 35(3): 623-631. | |
28 | Jiang X Y, Ge Z Q. Improving the performance of just-in-time learning-based soft sensor through data augmentation[J]. IEEE Transactions on Industrial Electronics, 2022, 69(12): 13716-13726. |
29 | Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004, 54(1): 45-66. |
30 | Tipping M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of machine learning research, 2001, 1(6): 211-244. |
31 | Birol G, Ündey C, Çinar A. A modular simulation package for fed-batch fermentation: penicillin production[J]. Computers & Chemical Engineering, 2002, 26(11): 1553-1565. |
32 | 张雷, 张小刚, 陈华. 基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量[J]. 化工学报, 2018, 69(6): 2576-2585. |
Zhang L, Zhang X G, Chen H. Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine[J]. CIESC Journal, 2018, 69(6): 2576-2585. |
[1] | Wuling ZHAO, Yi MAN. Research on framework of nanocellulose molecular structure prediction model based on variational encoder [J]. CIESC Journal, 2024, 75(9): 3221-3230. |
[2] | He ZHU, Yi ZHANG, Nana QI, Kai ZHANG. Effect of particle viscosity in two-fluid model on homogeneous liquid-solid fluidization under Euler-Euler framework [J]. CIESC Journal, 2024, 75(9): 3103-3112. |
[3] | Xin GUO, Wenjing LI, Junfei QIAO. Prediction of effluent parameters in wastewater treatment process using self-organizing modular neural network [J]. CIESC Journal, 2024, 75(9): 3242-3254. |
[4] | Qianqian WANG, Bing LI, Weibo ZHENG, Guomin CUI, Bingtao ZHAO, Pingwen MING. Three-dimensional modeling of local dynamic characteristics in hydrogen fuel cells [J]. CIESC Journal, 2024, 75(8): 2812-2820. |
[5] | Jiaqi DING, Haitao LIU, Pu ZHAO, Xiangning ZHU, Xiaofang WANG, Rong XIE. Study on intelligent rolling prediction of the multiphase flows in coal-supercritical water fluidized bed reactor for hydrogen production [J]. CIESC Journal, 2024, 75(8): 2886-2896. |
[6] | Hu JIN, Fan YANG, Mengyao DAI. The motion process of a droplet on a circular cylinder based on the lattice Boltzmann method [J]. CIESC Journal, 2024, 75(8): 2897-2908. |
[7] | Mingjun YANG, Guangjun GONG, Jianan ZHENG, Yongchen SONG. Production characteristics and model of muddy hydrates with low permeability by depressurization [J]. CIESC Journal, 2024, 75(8): 2909-2916. |
[8] | Yongqi TONG, Jie CHENG, Hai LIN, Xi CHEN, Haibo ZHAO. CPFD simulation of a 10 MWth chemical looping combustion reactor [J]. CIESC Journal, 2024, 75(8): 2949-2959. |
[9] | Jingru HUANG, Jiaxuan CHEN, Qunfeng ZHANG, Jin RUAN, Lai ZHU, Guanghua YE, Xinggui ZHOU. Effect of ZSM-5 zeolite structure on the reaction performance of benzene alkylation: a computational study [J]. CIESC Journal, 2024, 75(7): 2544-2555. |
[10] | Junxia MA, Lintao LI, Weili XIONG. A semi-supervised soft sensor modeling method based on the Tri-training GPR [J]. CIESC Journal, 2024, 75(7): 2613-2623. |
[11] | Jiayu XU, Feiguo CHEN, Ji XU, Wei GE. Multiscale mixing index for granular systems [J]. CIESC Journal, 2024, 75(6): 2214-2221. |
[12] | Yanling CHEN, Bingzhi YUAN, Liwei WANG, Chen ZHANG, Hanyu ZHU. Study on the adsorption kinetics of metal chloride-ammonia working fluid pair under non-equilibrium conditions [J]. CIESC Journal, 2024, 75(6): 2252-2261. |
[13] | Hongtao LI, Zhenlei WANG, Xin WANG. Improved conditional Gaussian regression soft sensor based on just-in-time learning [J]. CIESC Journal, 2024, 75(6): 2299-2312. |
[14] | Han ZHANG, Shuning ZHANG, Ke LIU, Guanlong DENG. Particle size prediction of cobalt oxalate synthesis process based on slow feature analysis and least squares support vector regression [J]. CIESC Journal, 2024, 75(6): 2313-2321. |
[15] | Chenggong CHANG, Haonan SONG, Feixia LEI, Zichen DI, Fangqin CHENG. Study on the carbon reduction potential of blast furnace injection process using reformed coke oven gas [J]. CIESC Journal, 2024, 75(6): 2344-2352. |
Viewed | ||||||
Full text 137
|
|
|||||
Abstract |
|
|||||