Asian Journal of Chemical Sciences

  • About
    • About the Journal
    • Submissions & Author Guideline
    • Accepted Papers
    • Editorial Policy
    • Editorial Board Members
    • Reviewers
    • Propose a Special Issue
    • Reprints
    • Subscription
    • Membership
    • Publication Ethics and Malpractice Statement
    • Digital Archiving Policy
    • Contact
  • Archives
  • Indexing
  • Publication Charge
  • Submission
  • Testimonials
  • Announcements
Advanced Search
  1. Home
  2. Archives
  3. 2021 - Volume 10 [Issue 4]
  4. Review Article

Submit Manuscript


Subscription



  • Home Page
  • Author Guidelines
  • Editorial Board Member
  • Editorial Policy
  • Propose a Special Issue
  • Membership

Research Progress in the Application of Artificial Neural Networks in Catalyst Optimization

  • Zhiqiang Liu
  • Wentao Zhou

Asian Journal of Chemical Sciences, Page 34-46
DOI: 10.9734/ajocs/2021/v10i419101
Published: 8 November 2021

  • View Article
  • Download
  • Cite
  • References
  • Statistics
  • Share

Abstract


The catalyst can speed up the chemical reaction and increase the selectivity of the target product, playing an important role in the chemical industry. By improving the performance of the catalyst, the economic benefits can be greatly improved. Artificial Neural Network (ANN), as one of the most popular machine learning algorithms, has parallel processing and self-learning capabilities as well as good fault tolerance, and has been widely used in various fields. By optimizing the catalyst through ANN, time and resource consumption can be greatly reduced, and greater economic benefits can be obtained. This article reviews how CNN technology can help people solve highly complex problems and accelerate progress in the catalytic world.


Keywords:
  • Artificial neural network (ANN)
  • catalyst
  • catalysis
  • Full Article – PDF
  • Review History

How to Cite

Liu, Z., & Zhou, W. (2021). Research Progress in the Application of Artificial Neural Networks in Catalyst Optimization. Asian Journal of Chemical Sciences, 10(4), 34-46. https://doi.org/10.9734/ajocs/2021/v10i419101
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver

References

Li H, Zhang Z, Liu Z. Application of Artificial Neural Networks for Catalysis: A Review. Catalysts 2017;7(10).
Avaialble:https://doi.org/10.3390/catal7100306.

Qi Y, Zheng Z. Neural Network Algorithm and Its Application in Supercritical Extraction Process, Asian Journal of Chemical Sciences. 2021;9(1):19-28.

Sheu BJ, Choi J. Back-Propagation Neural Networks. Neural Inf. Process. VLSI 1995:277–296. Avaialble:https://doi.org/10.1007/978-1-4615-2247-8_10.

Zhao N, Lu J. Review of Neural Network Algorithm and its Application in Temperature Control of Distillation Tower, Journal of Engineering Research and Reports. 2021;20(4):50-61.

Ding S, Chang XH, Wu QH. A Study on Approximation Performances of General Regression Neural Network. Appl. Mech. Mater. 2014;441:713–716.

Deng CW, Huang G B, Xu J, Tang JX. Extreme Learning Machines: New Trends and Applications. Sci. China Inf. Sci. 2015;58(2).
Avaialble:https://doi.org/10.1007/s11432-014-5269-3.

Yang Q. Study on Evaluation of Chemical Industry Safety Production Based on Artificial Neural Network. In Proceedings of 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC 2020; 2020;1272–1277.
Available:https://doi.org/10.1109/ITOEC49072.2020.9141777.

Xu X. The Development and Status of Artificial Neural Network. Microelectronics. 2017;47(2):239–242.

Wan D, Hu Y, Ren X. Applied Research of BP Neural Network with Feedback Input. Comput. Eng. Des. 2010;31 (2):398-400,405.

Yang Q, Wu X, Lu A, et al.. Research on Auxiliary System of Intelligent Assembly of Complex Products Based on Augmented Reality [J]. Mechanical Design and Manufacturing Engineering. 2017;46(11):33-37

Wang B, Lu P, Qian H. Research on Auxiliary Assembly System Based on Image Recognition, Changjiang Information &Communications. 2021;28(5):39-41.

Gao J, Tembine H. Distributed mean-fieldtype filters for traffic networks, IEEE Transactions on Intelligent Transportation Systems. 2019;20(2):507-521.

Kaur P, Sobti R. Scenario-Based Simulation of Intelligent Driving Functions Using Neural Networks, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2018:1-5.
DOI: 10.1109/ICCCNT.2018.8494086.

DANG W, WANG Y, WANG Y, WANG X. Condition Optimization of Reduction Roasting Magnetic Separation Technology for Laterite Nickel Ore by BP Neural Network Technique, Conservation and Utilization of Mineral Resources. 2020;40(5):128-133.

Wang Y, Lin Y, Zhong R Y, Xu X. IoT-enabled cloud-based additive manufacturing platform to support rapid product development, International Journal of Production Research. 2019;57(12):3975-3991.

Liu B. Product Appearance Design and Concept Innovation Based on Neural Network[J] Journal of Physics: Conference Series. 2021;1852:042091.

Shi F, Gao J, Huang X. An affine invariant approach for dense wide baseline image matching. International Journal of Distributed Sensor Networks (IJDSN). 2016;12(12).

Akay A, Dragomir A, Erlandsson B. Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care, in IEEE Journal of Biomedical and Health Informatics. 2015; 19(1): 210-218.

Ozyilmaz L, Yildirim T. Artificial neural networks for diagnosis of hepatitis disease," Proceedings of the International Joint Conference on Neural Networks, 2003., 2003;(1):586-589.
DOI: 10.1109/IJCNN.2003.1223422.

Gong K, Guan J, Kim K, et al. Iterative PET Image Reconstruction Using Convolutional Neural Network Representation[J]. IEEE Transactions on Medical Imaging, 2019;38(3):675-685.

Kappeler A, Yoo S, Dai Q, et al. Video Super-Resolution With Convolutional Neural Networks[J]. IEEE Transactions on Computational Imaging, 2016;2(2):109-122.

Gao J, Shi F. A Rotation and Scale Invariant Approach for Dense Wide Baseline Matching. Intelligent Computing Theory - 10th International Conference, ICIC (1) 2014:345-356.

Chidambaram IA, Velusami S. Decentralized Biased Controllers for Load — Frequency Control of Interconnected Power Systems Considering Governor Dead Band Non-Linearity[C]// Indicon. IEEE; 2006.

Francis R. Control Performance Standard based Load Frequency Control of a two area Reheat Interconnected Power System considering Governor Dead Band nonlinearity using Fuzzy Neural Network[J]. International Journal of Computer Applications. 2012;46(15):41-48.

Velusami S, Chidambaram I A. Decentralized biased dual mode controllers for load frequency control of interconnected power systems considering GDB and GRC non-linearities[J]. Energy Conversion & Management, 2007;48(5):1691-1702.

Gao J, Chakraborty D, Tembine H and Olaleye O. Nonparallel Emotional Speech Conversion. INTERSPEECH 2019, Graz, Austria; 2019.

Hinton GE, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition, Signal Process. Mag. 2012;29(6):82-97

Mandic D P. The use of Mobius transformations in neural networks and signal processing, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501), 2000:185-194 vol.1,
DOI: 10.1109/NNSP.2000.889409.

Lu J, Zhao N. Application of Neural Network Algorithm in Propylene Distillation, arXiv:2104.01774 [physics.chem-ph]; 2021.

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.

Wang H, Mo R. Review of Neural Network Algorithm and Its Application in Reactive Distillation. Asian Journal of Chemical Sciences. 2021;9(3): 20-29.

Wold S. Method and device for monitoring and fault detection in industrial processes[J]. 2005.

Alkaya A, Eker I . A new threshold algorithm based PCA method for fault detection in transient state processes[C]// International Conference on Electrical & Electronics Engineering. IEEE; 2012.

Purmonen S. Predicting Game Level Difficulty Using Deep Neural Networks; 2017.

Bauso D, Gao J, Tembine H. Distributionally Robust Games: f-Divergence and Learning, 11th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS), Venice, Italy; 2017.

Schollhorn WI. Applications of Artificial Neural Nets in Clinical Biomechanics. Clin. Biomech. 2004;19 (9):876–898.

Kumar R, Aggarwal RK, Sharma JD. Comparison of Regression and Artificial Neural Network Models for Estimation of Global Solar Radiations. Renew. Sustain. Energy Rev. 2015;52:1294–1299.

Han S-H, Kim KW, Kim S, Youn YC. Artificial Neural Network: Understanding the Basic Concepts without Mathematics. Dement. neurocognitive Disord. 2018;17 (3): 83–89.

Ding SF, Li H, Su CY, et al. Evolutionary Artificial Neural Networks: A Review. Artif. Intell. Rev. 2013;39 (3):251–260.

Li H, Liu ZJ, Liu KJ, Zhang ZE. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening. Int. J. Photoenergy 2017, 2017. https://doi.org/10.1155/2017/4194251.

Abdolghader P, Haghighat F, Bahloul A. Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN). Aerosol Sci. Eng. 2018 24 2018;2 (4): 197–205.

Othman AHA, Kassim S, Rosman RB, et al. Correction to: Prediction Accuracy Improvement for Bitcoin Market Prices Based on Symmetric Volatility Information Using Artificial Neural Network Approach. J. Revenue Pricing Manag. 2020 195. 2020;19 (5):331–331.

Kito S, Hattori T, Murakami Y. Estimation of Catalytic Performance by Neural Network -Product Distribution in Oxidative Dehydrogenation of Ethylbenzene. Appl. Catal. A 1994:114.

Sasaki M, Hamada H, Kintaichi Y. Application of a Neural Network to the Analysis of Catalytic Reaction of NO Decomposition over Cu/ZSM-5 Zeolite. Appl. Catal. A 1995;132:261–279.

Mohammed ML, Patel D, Mbelec R, et al. Optimisation of Alkene Epoxidation Catalysed by Polymer Supported Mo(VI) Complexes and Application of Artificial Neural Network for the Prediction of Catalytic Performances. Appl. Catal. A Gen. 2013;466:142–152.

Frontistis Z, Daskalaki VM, Hapeshi E, et al. Photocatalytic (UV-A/TiO2) Degradation of 17 Alpha-Ethynylestradiol in Environmental Matrices: Experimental Studies and Artificial Neural Network Modeling. J. Photochem. Photobiol. a-Chemistry 2012, 240: 33–41.

Rahman MBA, Chaibakhsh N, Basri M, et al. Application of Artificial Neural Network for Yield Prediction of Lipase-Catalyzed Synthesis of Dioctyl Adipate. Appl. Biochem. Biotechnol. 2009; 158 (3): 722–735.

Gunay ME, Yildirim R. Neural Network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature. Ind. Eng. Chem. Res. 2011;50 (22): 12488–12500.

Raccuglia P, Elbert KC, Adler PDF, et al. Machine-Learning-Assisted Materials Discovery Using Failed Experiments. Nature 2016;533 (7601): 73

Corma A, Serra JM, Argente E, et al. Application of Artificial Neural Networks to Combinatorial Catalysis: Modeling and Predicting ODHE Catalysts. Chemphyschem 2002;3 (11):939–945.

Omata K, Yamada M. Prediction of Effective Additives to a Ni/Active Carbon Catalyst for Vapor-Phase Carbonylation of Methanol by an Artificial Neural Network. Ind. Eng. Chem. Res. 2004; 43 (20):6622–6625.
Avaialble:https://doi.org/10.1021/ie049609p.

Hou ZY, Dai Q, Wu XQ , et al. Artificial neural network aided design of catalyst for propane ammoxidation[J]. Chinese Journal of Catalysis, 1997, 161(1):183-190.

Cundari TR, Deng J, Zhao Y. Design of a Propane Ammoxidation Catalyst Using Artificial Neural Networks and Genetic Algorithms. Ind. Eng. Chem. Res. 2001;40(23):5475–5480. Avaialble:https://doi.org/10.1021/ie010316v.

Umegaki T, Watanabe Y, Nukui N, et al. Optimization of Catalyst for Methanol Synthesis by a Combinatorial Approach Using a Parallel Activity Test and Genetic Algorithm Assisted by a Neural Network. Energy & Fuels 2003;17 (4):850–856.
Avaialble:https://doi.org/10.1021/ef020241n.

Rodemerck U, Baerns M, Holena M, Wolf D. Application of a Genetic Algorithm and a Neural Network for the Discovery and Optimization of New Solid Catalytic Materials. Appl. Surf. Sci. 2004;223 (1–3):168–174.
Avaialble:https://doi.org/10.1016/s0169-4332(03)00919-x.

Baumes L, Farrusseng D, Lengliz M, Mirodatos C. Using Artificial Neural Networks to Boost High-Throughput Discovery in Heterogeneous Catalysis. Qsar Comb. Sci. 2004;23 (9):767–778.
Avaialable:https://doi.org/10.1002/qsar.200430900.

Baroi C, Dalai AK. Esterification of free fatty acids (FFA) of Green Seed Canola (GSC) oil using H-Y zeolite supported 12-Tungstophosphoric acid (TPA)[J]. Applied Catalysis A General, 2014;485: 99-107.

Hadi N, Niaei A, Nabavi SR, et al. An Intelligent Approach to Design and Optimization of M-Mn/H-ZSM-5 (M: Ce, Cr, Fe, Ni) Catalysts in Conversion of Methanol to Propylene. J. Taiwan Inst. Chem. Eng. 2016;59:173–185.
Avaialble:https://doi.org/10.1016/j.jtice.2015.09.017.
  • Abstract View: 263 times
    PDF Download: 113 times

Download Statistics

Downloads

Download data is not yet available.
  • Linkedin
  • Twitter
  • Facebook
  • WhatsApp
  • Telegram
Make a Submission / Login
Information
  • For Readers
  • For Authors
  • For Librarians
Current Issue
  • Atom logo
  • RSS2 logo
  • RSS1 logo


© Copyright 2010-Till Date, Asian Journal of Chemical Sciences. All rights reserved.