Register | Login

Asian Journal of Research in Computer Science

  • About
    • About the Journal
    • Submissions & Author Guideline
    • Accepted Papers
    • Editorial Policy
    • Editorial Board Members
    • Reviewers
    • Printed Hard copy
    • Subscription
    • Membership
    • Publication Ethics and Malpractice Statement
    • Digital Archiving
    • Contact
  • Archives
  • Indexing
  • Publication Charge
  • Submission
  • Testimonials
  • Announcements
Advanced Search
  1. Home
  2. Archives
  3. 2022 - Volume 14 [Issue 4]
  4. Review Article

Author Guidelines


Submit Manuscript


Editorial Board Member


Membership


Subscription


Application of Artificial Neural Network in Optimal Design of Reactor

  •   Bingqian He
  •   Yue Si

Asian Journal of Research in Computer Science, Volume 14, Issue 4, Page 1-7
DOI: 10.9734/ajrcos/2022/v14i4287
Published: 26 September 2022

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

Abstract


Reactor is widely used in biology, chemical industry, metallurgy, environmental protection and other fields, playing an irreplaceable role. With the development of science and technology and the concept of green development, the application of artificial neural network to optimize the reactor reaction conditions has become a trend. Artificial neural network plays an important role in reactor optimization because of its strong fault tolerance, the ability to express complex nonlinear relations and perform complex operations. This paper will briefly describe the basic principle and research progress of artificial neural network, and its application in reactor design.

Keywords:
  • Artificial neural network
  • chemical reactor
  • BP neural network
  • optimal design
  • Full Article - PDF
  • Review History

How to Cite

He, B., & Si, Y. (2022). Application of Artificial Neural Network in Optimal Design of Reactor. Asian Journal of Research in Computer Science, 14(4), 1–7. https://doi.org/10.9734/ajrcos/2022/v14i4287
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
  • Endnote/Zotero/Mendeley (RIS)
  • BibTeX

References

Zhao Z. Template preparation ceramic phase catalysis microchannel reactor and its application research [D]. Jinan University; 2021.

Huang Z, Li JH. Temperature and pressure prediction of runaway reaction in Tank reactor [J]. Journal of Safety and Environment. 2005(03):109-112.

Yang M, Wang PN, Zhao MY. Characteristics and design of continuous stirred tank reactor [J]. Petrochemical Industry. 1974;(05):452-464.

Cao Y. Continuous micro series stirred tank reactor used for solid-liquid separation and reaction research [D]. East China University of Science and Technology; 2021.

Shi TF, Wu J. Artificial neural network application in the field of hydrology [J]. Journal of Electronic Testing. 2022;4(02):38-40.

Xi N. Application Progress of Artificial Neural Network in Chemical Industry [J].Contemporary Chemical Research. 2017(03):41-42.

Wang L, Zhang M, Zhu H, Liu Y. Artificial neural network and its application in earth science [J]. World Nuclear Geology. 2021,38(01):15-26.

Jiang XD, Li WR. Application of artificial neural network theory to the study of potential source areas in the Yellow and Bohai Sea [J]. Acta Oceanologica Sinica (Chinese version). 1995(03):135-139.

Zhao CW. Review of Artificial Neural Network [J]. Shanxi Electronic Technology. 2020(03): 94-96.

Liu WH, Hong J, Li H, Sun Guixiang, Chen Yan. Application of artificial neural network in diagnosis informationization of traditional Chinese medicine [J]. Journal of Human University of Traditional Chinese Medicine. 2017;37(07):809-812.

Zhang YX, Zhao J. Power generation power prediction of photovoltaic system based on feedback neural network [J]. Protection and control of Power Systems. 2011;39(15): 96-101.

Li PZ, Yu J, Rong B. A review of the application of Artificial Neural networks in structural engineering [C]// Proceedings of the 13th National Symposium on Modern Structural Engineering. 2013: 1894-1898.

Miao JJ, Li DB, Li HJ. Based on artificial neural network of boiler heating surface dust prediction research present situation and prospects of [J]. Journal of Clean Coal Technology. 2021;27(S2):212-220. The

DOI: 10.13226 / j.i SSN. 1006-6772.21101101.

Yan YC. Application of artificial neural network in medical statistics and information processing [J]. Journal of Mathematical Medicine and Pharmacy. 2016;29(11):1676-1677.

Zhang C, Guo Y, Li M. Review on the development and application of artificial neural network model [J]. Computer Engineering and Applications. 2021;57(11): 57-69.

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.

Mikolajczyk K,Schmid C. A performance evaluation of local descriptors. IEEE T Pattern Anal. 2005;27(10):1615–1630.

Bloch G, Denoeux T. Neural networks for process control and optimization: Two industrial applications[J]. ISA Transactions. 2003;42(1): 39-51.

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):1-12.

Zhu B, Lin J. Principal Component Analysis and Neural Network Ensemble Based Economic Forecasting, 2006 Chinese Control Conference. 2006:1769-1772.

Hansen JV, Nelson RD. Neural networks and traditional time series methods: A synergistic combination in state economic. IEEE Transactions on Neural Networks. 1997;8(4):863-873.

Sun L, Liang F, Cui W. Artificial Neural Network and Its Application Research Progress in Chemical Process, Asian Journal of Research in Computer Science. 2021;12(4):177-185.

Liang F, Zhang T. Prediction of Chemical Production Based on Neural Network, Asian Journal of Applied Chemistry Research. 2021;10(3-4):51-65.

Zheng Z, Qi Y. Study on the Simulation Control of Neural Network Algorithm in Thermally Coupled Distillation. Asian Journal of Research in Computer Science. 2021;10(3):53-64.

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.

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.

Ozyilmaz L, Yildirim T. Artificial neural networks for diagnosis of hepatitis disease, Proceedings of the International Joint Conference on Neural Networks. 2003; 1:586-589.

DOI: 10.1109/IJCNN.2003.1223422.

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.

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

Pelenis D, Barauskas D, Vanagas G et al. CMUT-based biosensor with convolutional neural network signal processing [J]. Ultrasonics. 2019;99:105956.

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

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.

Wold S. Method and Device for Monitoring and Fault Detection in Industrial Processes [J]; 2005.

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,

DOI:10.1109/PESGM41954.2020.9281489.

Rohman B P A , Ahbb C H , 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.

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.

Tang J. Study on prediction of IC reactor wastewater treatment system based on BP neural network [D]. Southwest Jiaotong University; 2018.

Wang ZK. Artificial neural network application in the chemical engineering [J]. Journal of modern Chemical Industry. 1996(9):17-21.

DOI: 10.16606 / j.carol carroll nki issn0253-4320.1996.09.004.

Liu M. Application of Artificial neural network in fault diagnosis of chemical reactor [J]. Liaocheng teachers college journal (natural science edition). 2000;(01):51-54 + 65.

DOI: 10.19728 / j.i ssn1672-6634.2000.01.014.

Steyer JP, Rolland D, Bouvier JC, et al. Hybrid fuzzy neural network for diagnosis - Application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor[J]. Water Science and Technology. 1997;36(6/7):209-217.

Chen YF, Shen LG, Lin HG. Artificial neural network in the application of quantitative membrane fouling interface reaction [J]. Journal of Membrane Science and Technology. 2020;40(3):47-54.

DOI: 10.16159 / j.carol carroll nki issn1007-8924.2020.03.007.

Shi BQ, Zhang HM, Yang FL, Meng FG, Zhang XW. Application prospect of artificial neural network in membrane fouling prediction of membrane bioreactor [C]// Proceedings of the Second Annual Conference of Water Treatment Chemicals Industry of China Fine Chemical Association. [Publisher unknown]. 2006: 207-214.

Fan JL, Liu HB, Xue ZY, Wang JX, Wang CN, Zhang RS. Based on artificial neural network [J]. Journal of MBR Membrane Fouling Research Status of Membrane Science and Technology. 2021;9(4): 154-159.

DOI: 10.16159 / j.carol carroll nki issn1007-8924.2021.04.020.

Shetty GR, Chellam S. Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks[J]. Journal of Membrane Science. 2003,217(1/2):69-86.

Hu YF, Yang CZ, Dan JF, Pu WH, Yang JK. Application of artificial neural network in expanded granular sludge bed reactor [C]// Environmental Engineering. 2017Supplement1:187-191+34.

Yin JZ, Cheng SJ, Jia LY, Yin JW. Stirring reactor simulation and optimization design [J]. Chemical Equipment Technology. 2009;30(4):1-5.

DOI: 10.16759 / j.carol carroll nki. Issn 1007-7251.2009.04.003.

Anna Witek-Krowiak, Katarzyna Chojnacka, Daria Podstawczyk, Anna Dawiec, Karol Pokomeda. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process [J]. Bioresource Technology; 2014.

Yang ZK, Liu H, Li JW, Li CY. Artificial Neural network simulation of the performance of coal direct liquefaction batch reactor [J]. Coal Chemical Industry. 2007(02):27-30.

Men ZW, Que GH. Prediction of gas holdup and volumetric gas-liquid mass transfer coefficient in slurry bed reactor using neural networks [J]. Petroleum Planning and Design. 2009,20(06):17-20+50.

Wang JH, Xie HW. Research on prediction model of anaerobic sequential batch reactor based on BP network [J]. Computer Development and Application. 2008;21(12):13-15.

  • Abstract View: 159 times
    PDF Download: 70 times

Download Statistics

Downloads

Download data is not yet available.
  • Linkedin
  • Twitter
  • Facebook
  • WhatsApp
  • Telegram
Make a Submission

Information

  • For Readers
  • For Authors
  • For Librarians

Current Issue

  • Atom logo
  • RSS2 logo
  • RSS1 logo


Copyright © 2010 - 2023 Asian Journal of Research in Computer Science. All rights reserved.