Abstract
It is important for quality prediction and monitoring to ensure the safe operation of the process. When constructing a prediction model, it is crucial to choose appropriate input variables to influence the online prediction performance and quality monitoring. Data-driven techniques have been widely used for prediction and monitoring of quality variables, but there are some difficulties in the application of batch processes, three-dimensional characteristics of data, different initial conditions, and multi-stage characteristics within batches. Therefore, we propose a quality prediction model of multi-stage batch process based on integrated ConvBiGRU with attention mechanism (MI-ConvBiGRU-AM). Firstly, Firstly, the original 3D data are expanded into 2D time slices by the batch-variable expansion method. Secondly, the 2D time slices are clustered to complete stage identification using the improved affine propagation clustering method based on the design of the Markov chain similarity matrix. At each stage, we select product quality-related modeling variables using the Maximum Relevance Minimum Redundancy (mRMR). Then, the selected variables are used to train a convolutional bi-directional gated recurrent unit with an attention mechanism (ConvBiGRU-AM). Finally, ConvBiGRU-AM model for each stage is integrated together a whole prediction model for the entire process to accomplish quality prediction, and the prediction residuals are utilized for quality monitoring. The validity of the proposed method was verified by Industrial-scale fed-batch fermentation (IFBF) process and the Hot strip mill (HSM) process. For the IFBF process, the model achieved an FDR of 99.73%, FAR of 0.54%, MAE of 0.0043, RMSE of 0.0396, MAPE of 0.0121, and R2 of 0.9971. For the HSM process, the results were an FDR of 99.95%, FAR of 0.25%, MAE of 0.0053, RMSE of 0.0111, MAPE of 0.1539, and R2 of 0.9990. These results demonstrate that the proposed method significantly improves prediction accuracy and achieves better quality monitoring compared to existing methods, highlighting its effectiveness for industrial applications.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Fujita H, Fournier-Viger P, Sasaki J, Ali M (2021) Advances in theory and applications of artificial intelligence. AI Mag 42(1):86–87
Chandrasekar A, Radhika T, Zhu Q (2022) Further results on input-to-state stability of stochastic Cohen–Grossberg BAM neural networks with probabilistic time-varying delays. Neural Process Lett 1–23
Radhika T, Chandrasekar A, Vijayakumar V, Zhu Q (2023) Analysis of Markovian jump stochastic Cohen-Grossberg BAM neural networks with time delays for exponential input-to-state stability. Neural Process Lett 55(8):11055–11072
Tamil Thendral M, Ganesh Babu TR, Chandrasekar A, Cao Y (2022) Synchronization of Markovian jump neural networks for sampled data control systems with additive delay components: analysis of image encryption technique, Mathematical methods in the applied sciences
Ji C, Ma F, Wang J, Sun W (2023) Profitability related industrial-scale batch processes monitoring via deep learning based soft sensor development. Comput Chem Eng 170:108125
Peng C, ChunHao D (2022) Monitoring multi-domain batch process state based on fuzzy broad learning system. Expert Syst Appl 187:115851
Sansana J, Rendall R, Joswiak MN, Castillo I, Miller G, Chiang LH, Reis MS (2023) a functional data-driven approach to monitor and analyze equipment degradation in multiproduct batch processes. Process Safety Environ Protect
Zhang Y, Cao J, Zhao X, Hui Y (2023) Nonlinear multiphase batch process monitoring and quality prediction using multi-way concurrent locally weighted projection regression. Chemom Intell Lab Syst 240:104922
Yu Y (2012) Intelligent quality prediction using weighted least square support vector regression. Phys Procedia 24:1392–1399
Yuan X, Ge Z, Song Z (2014) Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes. Ind Eng Chem Res 53(35):13736–13749
Yu J (2012) Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes. Ind Eng Chem Res 51(40):13227–13237
Rong M, Shi H, Tan S (2019) Large-scale supervised process monitoring based on distributed modified principal component regression. Ind Eng Chem Res 58(39):18223–18240
Gins G, Van Impe JF, Reis MS (2018) Finding the optimal time resolution for batch-end quality prediction: MRQP–A framework for multi-resolution quality prediction. Chemom Intell Lab Syst 172:150–158
Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629
Jiang K, Han Q, Du X, Ni P (2021) A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders. Computer-Aided Civil Infrastruct Eng 36(6):711–732
Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235–1270
Zhao R, Wang D, Yan R, Mao K, Shen F, Wang J (2017) Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Industr Electron 65(2):1539–1548
Yao L, Ge Z (2023) Causal variable selection for industrial process quality prediction via attention-based GRU network. Eng Appl Artif Intell 118:105658
Ma L, Wang M, Peng K (2022) A novel bidirectional gated recurrent unit-based soft sensor modeling framework for quality prediction in manufacturing processes. IEEE Sens J 22(19):18610–18619
Li J, Yang C, Li Y, Xie S (2021) A context-aware enhanced GRU network with feature-temporal attention for prediction of silicon content in hot metal. IEEE Trans Industr Inf 18(10):6631–6641
Sun K, Liu J, Kang J-L, Jang S-S, Wong DS-H, Chen D-S (2014) Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote. J Process Control 24(7):1068–1075
Fujiwara K, Kano M (2015) Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso. ISA Trans 58:367–379
Yao L, Ge Z (2018) Variable selection for nonlinear soft sensor development with enhanced binary differential evolution algorithm. Control Eng Practice 72:68–82
Zhao C (2014) Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring. AIChE J 60(2):559–573
Luo L, Bao S, Mao J, Tang D (2016) Phase partition and phase-based process monitoring methods for multiphase batch processes with uneven durations. Ind Eng Chem Res 55(7):2035–2048
Peng K, Li Q, Zhang K, Dong J (2016) Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method. Neurocomputing 214:317–328
Liu J, Liu T, Chen J (2018) Sequential local-based Gaussian mixture model for monitoring multiphase batch processes. Chem Eng Sci 181:101–113
Peng C, Lu R, Kang O, Kai W (2020) Batch process fault detection for multi-stage broad learning system. Neural Netw 129:298–312
Zhao X, Liu K, Hui Y (2023) Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm. Trans Inst Meas Control 45(1):89–103
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976
Lerm S, Saeedi A, Rahm E (2021) Extended affinity propagation clustering for multi-source entity resolution
Wei Z, He D, Jin Z, Liu B, Shan S, Chen Y, Miao J (2023) Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing. IEEE Trans Intell Transp Syst 24(6):6053–6064
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder-decoder approaches, arXiv preprint arXiv:1409.1259
Zhang X, Tang L, Chen J (2021) Fault diagnosis for electro-mechanical actuators based on STL-HSTA-GRU and SM. IEEE Trans Instrum Meas 70:1–16
Xia M, Shao H, Ma X, De Silva CW (2021) A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans Industr Inf 17(10):7050–7059
Zhao H (2018) Dynamic graph embedding for fault detection. Comput Chem Eng 117:359–371
Gu X, Guo J, Xiao L, Li C (2022) Conditional mutual information-based feature selection algorithm for maximal relevance minimal redundancy. Appl Intell 52(2):1436–1447
Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48–62
Goldrick S, Ştefan A, Lovett D, Montague G, Lennox B (2015) The development of an industrial-scale fed-batch fermentation simulation. J Biotechnol 193:70–82
Ding SX, Yin S, Peng K, Hao H, Shen B (2012) A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill. IEEE Trans Industr Inf 9(4):2239–2247
Mears L, Stocks SM, Sin G, Gernaey KV (2017) A review of control strategies for manipulating the feed rate in fed-batch fermentation processes. J Biotechnol 245:34–46
Nadal-Rey G, McClure DD, Kavanagh JM, Cassells B, Cornelissen S, Fletcher DF, Gernaey KV (2021) Development of dynamic compartment models for industrial aerobic fed-batch fermentation processes. Chem Eng J 420:130402
Mourchid Y, Slama R (2023) D-STGCNT: a dense spatio-temporal graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation. Comput Biol Med 165:107420
Acknowledgements
This research work has been awarded by the National Natural Science Foundation of China (62263021, 62163023), Industrial Support Project of Education Department of Gansu Province (2023CYZC-24), the Open Fund project of Gansu Provincial Key Laboratory of Advanced Control for Industrial Process (2022KX07).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, K., Zhao, X., Mou, M. et al. Quality prediction of multi-stage batch process based on integrated ConvBiGRU with attention mechanism. Appl Intell 55, 123 (2025). https://doi.org/10.1007/s10489-024-06002-y
Accepted:
Published:
DOI: https://doi.org/10.1007/s10489-024-06002-y