Research on Application of Artificial Neural Network in Fault Diagnosis of Chemical Process
Asian Journal of Chemical Sciences,
Chemical processes are usually toxic, corrosive, flammable and explosive. If the process fails, the danger is extremely high. Traditional model-based fault diagnosis methods need to establish an accurate mathematical model of the system, while modern engineering processes are usually large in scale and complex, and it is difficult to establish an accurate mathematical model. Artificial neural network has been widely used in chemical process because of its advantages of parallel processing, self-adaptation, robustness, learnability and fault tolerance. Artificial neural networks based on "deep learning" have been successfully applied to fault diagnosis in various chemical processes. This article summarizes the principle and development process of artificial neural networks, and analyzes the research progress and application status of deep neural networks in chemical process fault diagnosis based on cases. Finally, it is pointed out that deep neural network in the field of chemical process fault diagnosis is of great significance in solving the impact of less fault data and system state changes on the fault detection rate, and promoting the industrial application of fault diagnosis models.
- Neural network
- chemical process
- fault diagnosis
- deep learning.
How to Cite
Sun J, Tang Q. Review of artificial neural network and its application research in distillation. Journal of Engineering Research and Reports. 2021;21(3): 44-54.
Gao J, Xu Y, Barreiro-Gomez J, Ndong M, Smyrnakis M, Tembine H. (September 5th 2018) Distributionally Robust Optimization. In Jan Valdman, Optimization Algorithms, IntechOpen.
DOI: 10.5772/intechopen.76686. ISBN: 978-1-78923-677-4.
Li C, Wang C. Application of artificial neural network in distillation system: A critical review of recent progress. Asian Journal of Research in Computer Science. 2021; 11(1): 8-16.
Li J. The application and development trend of artificial neural network in petrochemical enterprises [J]. Petrochemical Design. 2020;37(2):53-58.
Rohman BPA,Ahbb CH, Tridianto E, et al. Power generation forecasting of dual-axis solar tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks[J]. EMITTER International Journal of Engineering Technology. 2018;6(2):275.
Gao J, Chongfuangprinya P, Ye Y, Yang B. A three-layer hybrid model for wind power prediction. 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, 2020;1-5.
Sperati S, Alessandrini S, Monache LD. An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting[J]. Solar Energy. 2016; 133(aug.):437-450.
Alexander J H. Preventing atrial fibrillation after cardiac surgery: What matters Most[J] Journal of the American College of Cardiology. 2021;77(1): 68-70.
Chen X, Cheng Z, Wang S, et al. Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals, Computer Methods and Programs in Biomedicine. 2021;202:106009.
Gao J, Tembine H. Correlative Mean-Field Filter for Sequential and Spatial Data Processing, in the Proceedings of IEEE International Conference on Computer as a Tool (EUROCON), Ohrid, Macedonia, July 2017.
Zhang J, Qian J, Yang T, et al. Analysis and recognition of characteristics of digitized tongue pictures and tongue coating texture based on fractal theory in traditional Chinese medicine[J]. Comput Assist Surg (Abingdon). 2019;24(sup1): 62-71.
Chen MZ, Cen YG, Cui LT, et al. Study on observation diagnosis automatic complexion recognition based on image processing. Chinese Journal of Information on Traditional Chinese Medicine, 2018; 25(12): 97-101.
Yang Y, Zhang J, Zhuo L, et al. Cheek region extraction method for face diagnosis of traditional Chinese medicine[C]//IEEE, International Conference on Signal Processing. IEEE. 2013;1663- 1667.
Gao J, Tembine H. Distributed mean-fieldtype filters for traffic networks, IEEE Transactions on Intelligent Transportation Systems. 2019;20(2):507-521.
Li F, Wang W, Feng G, et al. Driving intention inference based on dynamic bayesian networks[J]. Advances in Intelligent Systems & Computing. 2014; 279:1109-1119.
Zhang W, Li S, Wu B. Safety Evaluation of traffic accident scene based on artificial neural network. IEEE Computer Society; 2009.
Bloch G, Denoeux T. Neural networks for process control and optimization: Two industrial applications[J]. ISA Transactions, 2003;42(1):39-51.
Chen J, Huang T-C. Applying neural networks to on-line updated PID controllers for nonlinear process control[J]. 2004; 14(2):211-230.
Maciej L. Online set-point optimisation cooperating with predictive control of a yeast fermentation process: A neural network approach [J] Engineering Applications of Artificial Intelligence. 2011; 24(6):968-982.
Wang H, Mo R. Neural network algorithm and its application in reactive distillation, arXiv: 2011.09969 [cs.NE], 2021. Available:https://arxiv.org/abs/2011.09969
Wu XJ, Xu MD, Li CD, et al. Research on image reconstruction algorithms based on autoencoder neural network of Restricted Boltzmann Machine (RBM), Flow Measurement and Instrumentation. 2021; 80:102009.
Cao F, Yao K, Liang J. Deconvolutional neural network for image super-resolution, Neural Networks. 2020, 132:394-404.
Gao J, Shi F. A rotation and scale invariant approach for dense wide baseline matching. Intelligent Computing Theory - 10th International Conference, ICIC. 2014; (1):345-356.
Bharati S, Podder P, Rubaiyat Hossain Mondal M. Artificial neural network based breast cancer screening: A comprehensive review. arXiv:2006.01767, 2020.
Bharati S, Podder P, Rubaiyat Hossain Mondal M, et al. Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review, arXiv:2107.09602 [eess.IV], 2021.
Podder P, Bharati S, Rubaiyat Hossain Mondal M, et al. Artificial neural network for cybersecurity: A comprehensive review, arXiv:2107.01185 [cs.CR],2021.
Song HY, Sun XY, Xiang SG. Application progress of artificial neural network in chemical process [J].Chemical progress, 2016;35(12): 3755-3762.
Zhu DQ, Shi H. Principle and application of artificial neural network [M].Beijing: Science Press. 2006;8-11.
Han LQ. Artificial neural network theory, design and application (second edition)[M]. Beijing: Chemical Industry Press. 2007; 14-16.
ROSENBLATT F. The perceptron: A probabilistic model for information storage and organization in the brain [J]. Psychological Review. 1958;6(65):386-408.
Minsky M, Papert S. Perceptions [M]. Oxford: MIT Press. 1969;57-89.
Werbos P. Beyond regression: new tools for prediction and analysisin the behavioral sciences [D]. Boston: Harvard University. 1974;89-135.
Hopfield J J. Neural networks and physical systems with emergent collective computational abilities [J]. Proceedings of the National Academy of Science. 1982; 79(8): 2254-2558.
WERBOS P J. Backpropagation through time: what it does and how to do it[J]. Proceedings of the IEEE. 1990;78(10): 1550-1560.
Liu F, Xu L, Ma XX. The development of BP neural network and its application in chemical industry [J]. Chemical progress, 2019;38(6):2560-2562.
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks [J]. Science. 2006; 313(5786):504-507.
Krizhevsky A, Sutskever I, Hinton GE. Image net classification with deep convolutional neural networks[C]. Advances in Neural Information Processing Systems. 2012,25(2):1105-1114.
Xia XM. Fault diagnosis of chemical process based on deep residual network [D]. East China Jiaotong University. 2020; 7-8.
Du HY, Zhao YL. Comparison of typical artificial neural network models [J]. Computer Technology and Development, 2006; 16(5): 97-99.
Gao XH. Development and application of ART neural network [J]. Computer Knowledge and Technology. 2007;509-526.
Wang D, Fang L. Research progress of neural networks in chemical applications [J].Guangdong Chemical Industry. 2007, 34(10): 52-55.
Yang H. The current research on fault diagnosis of chemical process [J]. Chemical Design Communication. 2017; 43(3):161-62.
Su K. Fault diagnosis of chemical processes based on convolutional neural networks [D]. Guangdong: South China University of Technology. 2019;3-14.
Huang D, Song X. Application of neural network in chemical process fault diagnosis [J]. Control Engineering. 2006; 13(1):6-9.
Kuang T. Fault detection of complex chemical processes based on deep neural network [D]. South China University of Technology. 2018;16-19.
Ngiam J, Pang WK, Chen Z. Sparse filtering [A]. International Conference on Neural Information Processing Systems [C], 2011:1125-1133.
Downs JJ, Vogel EF. A Plant-wide industrial process control problem [J]. Computers & Chemical Engineering. 1993;17(3):245-255.
Bathelt A, Ricker N L, Jelali M, Revision of the tennessee eastman process model [J]. IFAC-PapersOnLine, 2015; 48 (8):309-314.
Wu H, Zhao JS. Deep convolutional neural network model based chemical process fault diagnosis [J]. Computers & Chemical Engineering. 2018;115: 185-197.
Yao YM, Luo WJ, Dai YY. Research progress of data-driven method in fault diagnosis of chemical process [J]. Chemical progress. 2021;(4):1761-1763.
He K, Zhang X, Ren S, Sun J, Identity mappings in deep residual networks [C]. European conference on computer vision. Springer, Cham. 2016;630-645.
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