Artificial Neural Networks and Its Applications in Chemical Industry
Asian Journal of Chemical Sciences,
Artificial neural networks, as an important part of artificial intelligence, have a wide scope of development to improve the traditional production technology of chemical processes with its inherent advantages of parallel structure and parallel processing, fault tolerance, full approximation of any complex nonlinear relationships, learnability and self-adaptability, etc. Thus, it has a wide scope of development to improve the problems of lagging diagnosis, difficult to optimize control, large errors in physical property estimation and inability to deal with nonlinear complex situations. This paper summarizes the theory of artificial neural network, including its structure and characteristics, and introduces its applications in different fields, especially in chemical industry.
- Artificial neural network
- chemical industry
How to Cite
Garcia-Pedrajas N, Hervas-Martinez C, Munoz-Perez J. COVNET: A cooperative coevolutionary model for evolving artificial neural networks [J]. IEEE Transaction on Neural Networks. 2003;14(3):575-596.
Wang L. Principles, classification and applications of artificial neural networks [J]. Science and Technology Information, 2014;03:240-241.
Wang HB, Zhang LY, Hu ZJ. Artificial neural network theory and its applications [J]. Shanxi Electronic Technology, 2006; 2:41-43.
Gao J. Game-theoretic approaches for generative modeling [D]. New York University, Tandon School of Engineering ProQuest Dissertations Publishing; 2020.
Zhao C. A review of artificial neural networks [J]. Shanxi Electronics Technology. 2020;03:94-96.
Gao J, Xu Y, Barreiro-Gomez J, Ndong M, Smyrnakis M, Tembine H. Distributionally Robust Optimization. In Jan Valdman, Optimization Algorithms, IntechOpen; 2018.
Wang H, Chen Y. Application of artificial neural networks in chemical process control[J]. Asian Journal of Research in Computer Science. 2022;14(1):22-37.
Pan BB. A review of algorithms for multi-objective path planning problems [J]. Journal of Chongqing University of Technology and Business: Natural Science Edition. 2012;29(5):78-84.
An T, Xu J M. Application of Artificial Neural Network in Rectification[J]. Asian Journal of Advanced Research and Reports. 2022;16(9):1-7.
Sun L, Liang F, Cui WT. Artificial neural network and its application research progress in chemical process[J]. Asian Journal of Research in Computer Science. 2021;12(4):177-185.
Shi F, Gao J, Huang X. An affine invariant approach for dense wide baseline image matching[J]. International Journal of Distributed Sensor Networks (IJDSN). 2016;12(12).
Zheng Z L, Qi Y. Study on the simulation control of neural network algorithm in thermally coupled distillation[J]. Asian Journal of Research in Computer Science. 2021;10(3):53-64.
Gao J, Shi F. A rotation and scale invariant approach for dense wide baseline matching[J]. Intelligent Computing Theory - 10th International Conference, ICIC. 2014; 1:345-356.
Ye C, Liao J, Mei F. Vehicle license plate character recognition system[J]. Computer System Applications. 1999;5:10-13.
Li J, Luan S, You M. Introduction to the principle of artificial neural network[J]. Modern Education Science. 2010;01: 98-99.
Jiang Z. Introduction to artificial neural networks [M]. Beijing: Higher Education Press; 2001.
Mikolajczyk K, Schmid C. Scale & affine invariant interest point detectors[J]. Int J Comput Vision. 2004;60(1):63–86.
Gao J, Chakraborty D, Tembine H, Olaleye O. Nonparallel emotional speech conversion. InterSpeech 2019, Graz, Austria; 2019.
Chang G, Zhang J, Li L. Neural networks and their applications [J]. Aviation Computing Technology. 1999;4:50-53.
Huang L. Overview of neural networks and their applications [J]. Jiangsu Electrical Engineering, 1994;1:11-38.
Mu Y, Sun L. Catalyst optimization design based on artificial neural network[J]. Asian Journal of Research in Computer Science. 2022;13(2):1-12.
Gao J, Tembine H. Distributionally robust games for deep generative learning; 2018.
Song H, Sun X, Xiang S. Advances in the application of artificial neural networks in chemical processes[J]. Advances in Chemical Engineering. 2016;35(12): 3755-3762.
Huang D, Song X. Application of neural networks in chemical process fault diagnosis[J]. Control Engineering. 2006; 13(1): 6-9.
Wang H, Chen Y. Research on application of artificial neural network in fault diagnosis of chemical process[J]. Asian Journal of Chemical Sciences. 2021;10(4):90-97.
Downs JJ, Vogel EF. A plant-wide industrial process control problem [J]. Computers & Chemical Engineering. 1993; 17(3):245-255.
Hagan M T, Demuth HB, Deale MH. Neural network design [M]. Beijing: Machinery Industry Press. 2002;5-8.
Zhang W, Wu C. Neural network-based fault diagnosis for chemical processes [J]. Computers and Applied Chemistry. 2010; 27(7):987-991.
Feng X, Sun L. Application progress of artificial neural network in chemical industry [J]. Journal of Engineering Research and Reports. 2022;23(7):26-36.
Airikka P. Advanced control methods for industrial process control [J]. Computing and Control Engineering Journal. 2004; 15(3):18-23.
Piovoso MJ. Industrial process control[J]. IEEE Control Systems. 2000;20(3):22-24.
Ran Q, Liu Y. Artificial neural networks and their applications in the chemical industry[J]. Guangdong Chemical Industry. 2001;29(2):32-34.
James L, McClelland. Parallel distributed processing[M]. The PDP Research Group: The MIT Press. 1987;45-623.
Guo K, Guo Q, Li D, et al. Fuzzy neural network PID controller design and application in normal decompression device[J]. Automation Applications. 2016; 2:14-15.
Tsen YD, Shi SJ, Wong DSH, et al. Predictive control of quality in batch polymerization using hybrid ANN models [J]. AIChE Journal. 1996;42(2):455-465.
Zheng W. Research on intelligent system and method for process quality control[D]. Xi'an: School of Management, Northwestern Polytechnic University. 2006; 28-77.
Zhu P, Xia L, Pan H. Quality control of intermittent polymerization process based on soft measurement technology[J]. Computers and Applied Chemistry. 2015;32(8):959-963.
Dong M, Li G, Li Y, Liu C. The research on slab thermal properties estimated by bp artificial neural networks[C]. International Conference on Electrical and Control Engineering, IEEE Computer Society, Washington D C, USA. 2010;2425-2427.
Dong X F, Fang LG, Chen TO. Principles of physical properties estimation and computer calculations[M]. Beijing: Chemical Industry Press. 2006;25-30.
Zhang X, Zhao, L, Zhang G. Artificial neural network method for predicting the basic properties of organic compounds[J]. Journal of Chemical Engineering. 1995; 46(1):66-74.
Pan Y, Jiang JC. Prediction of organic flash points by artificial neural network group contribution method[J]. Natural Gas Chemical. 2007;32(2):67-71.
Pan Y, Jiang JC, Wang ZR. Prediction of alkane flash points by artificial neural network group bond contribution[J]. Chemical Engineering. 2007;35(4): 38-41.
Khazaiepoul A, Soleimani M, Salahi S. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chinese Journal of Chemical Engineering. 2015;24(4):491-498.
Wei QY, Li Q. Dynamic simulation of distillation tower based on neural network[J]. Journal of Jilin Institute of Chemical Technology. 2004;21(1): 10-15.
Zhang Y, Wu L N, Wei G. A particle swarm neural network-based method for cell image segmentation[J]. Journal of Electronic Measurement and Instrumentation. 2009;23(7):56-62.
Zhang Y, Wu L N, Wei G. Research on neural network generalization enhancement techniques[J]. Science, Technology and Engineering. 2009; 9(17):4997-5002.
Zhang Y, Wu L N, Wu H. A comparison of neural networks and evolutionary algorithms in engineering optimization problems [J]. Computer Engineering and Applications. 2009;45(3):1-6.
Abstract View: 53 times
PDF Download: 24 times