Neural Network Algorithm and Its Application in Supercritical Extraction Process

Main Article Content

Yu Qi
Zhaolan Zheng

Abstract

Artificial neural network (ANN)algorithms can be used for multi-parameter optimization and control by simulating the mechanisms of the human brain. Therefore, ANN is widely used in many fields such as signal processing, intelligent driving, face recognition, and optimization and control of chemical processes. As a green and efficient chemical separation process, supercritical extraction is especially suitable for the separation and purification of active ingredients in natural substances. Because there are many parameters that affect the separation efficiency of the process, the neural network algorithm can be used to quickly optimize the process parameters based on limited experimental data to determine the appropriate process conditions. In this work, the research progress of neural network algorithms and supercritical extraction are reviewed, and the application of neural network algorithms in supercritical extraction is discussed, aiming to provide references for researchers in related fields.

Keywords:
Artificial neural network algorithm, BP neural network, RBF neural network, supercritical fluid extraction.

Article Details

How to Cite
Qi, Y., & Zheng, Z. (2021). Neural Network Algorithm and Its Application in Supercritical Extraction Process. Asian Journal of Chemical Sciences, 9(1), 19-28. https://doi.org/10.9734/ajocs/2021/v9i119062
Section
Review Article

References

Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986; 323(6088):533-536.

Seshagiri S, Khalil HK. Output feedback control of nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks. 2000;11(1):69-79.

Gao J, Tembine H. Distributed mean-field filters, workshop on frontiers of networks: theory and algorithms, seventeenth international symposium on mobile ad hoc networking and computing, MobiHoc, Paderborn,Germany; 2016.

Zhang Q, Liu ZR, Guo H. Research on the application of neural network algorithm in artificial intelligence recognition. Jiangsu Communications. (In Chinese). 2019; 35(1):63-67

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

Moghaddam AH, Moein HM, Morteza E. Stock market index prediction using artificial neural network. Journal of Economics. Finance and Administrative Scienc. 2016;21(41):89-93.

Al-Shayea, Qeethara K. Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues. 2011;8(2):150-154.

Gao J, Chakraborty D, Tembine H, Olaleye O. Nonparallel emotional speech conversion. INTERSPEECH, Graz, Austria; 2019.

Gao J, Xu Y, Barreiro-Gomez J, Ndong M, Smyrnakis M, Tembine H. Distributionally Robust Optimization. In Jan Valdman, Optimization Algorithms, Intech Open; 2018. DOI: 10.5772/intechopen.76686 ISBN:978-1-78923-677-4

Mohamed AE. Antioxidative and cytotoxic activity of essential oils and extracts of Satureja montana L., Coriandrum sativum L. and Ocimum basilicum L. obtained by supercritical fluid extraction. The Journal of Supercritical Fluids. 2017;128:128-137.

Lang Q, Chien MW. Supercritical fluid extraction in herbal and natural product studies—A practical review. Talanta. 2001; 53(4):771-782.

Papamichail I, Louli V, Magoulas K. Supercritical fluid extraction of celery seed oil. The Journal of Supercritical Fluids. 2000;18(3):213-226.

Xue F, Wang J, Guo KL. Application research of supercritical CO2 fluid extraction technology. Chemical Industry Management. 2019;11:114-115.(in Chinese)

Wei HB. Research on optimization method of setting value parameters in supercritical extraction process. Changchun University of Technology; 2016. (in Chinese)

Karsoliya S. Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology. 2012;3(6):714-717.

Liu R. An overview of the basic principles of artificial neural networks. Computer Products and Circulation. 2020;(06):35-81. (in Chinese)

Wang L. The principle, classification and application of artificial neural Network. Technology Information. 2014;(03):240-241. (in Chinese)

Ghaleb FA, Zainal A, Rassam MA, Mohammed F. An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications. 2017 IEEE Conference on Application, Information and Network Security (AINS), Miri. 2017:13-18.

Zhu LY. Research and analysis of artificial neural network. Science and Technology Communication. 2019;11(12):120-122.(in Chinese)

Han P, Zhou HC, Zhou BW. Research and implementation of BP neural network principle. Radio and TV Information. 2018;(10):121-125. (in Chinese)

Gao J, Tembine H. Bregman Learning for Generative Adversarial Networks. Chinese Control and Decision Conference, Shenyang, China; 2018.

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.

Deng CJ, Dong Q, Zhang CS, Zhang L, Liu SC. Neural network optimization of papaya seed oil supercritical CO2 extraction process. Journal of the Chinese Cereals and Oils Association. 2012;27(2):47-51. (in Chinese)

Feng GH. Application of artificial neural network in the simulation of extractive distillation and reactive distillation. Tianjin University; 2007. (In Chinese)

Zhang YH, Zhao Y, Luo JJ. A review of research on supercritical CO2 extraction and molecular distillation technology. Gansu Agricultural Science and Technology. 2013;(05):44-47.(In Chinese)

Taylor LT. Supercritical fluid extraction. New York, Wiley; 1996.

Herrero Miguel JA, Cifuentes A, Ibáñez E. Supercritical fluid extraction: Recent advances and applications. Journal of Chromatography a. 2010;1217(16):2495-2511.

Reverchon Ernesto, Iolanda DM. Supercritical fluid extraction and fractionation of natural matter. The Journal of Supercritical Fluids. 2006;38(2):146-166.

Sabio E, Lozano M, Montero de Espinosa V, Mendes RL, Pereira AP, Palavra AF, Coelho JA. Lycopene and β-carotene extraction from tomato processing waste using supercritical CO2. Industrial & engineering chemistry research. 2003; 42(25):6641-6646.

Martinez JL. Supercritical fluid extraction of nutraceuticals and bioactive compounds. CRC Press; 2007.

Silva D, Rui PFF, Teresa AP Rocha-Santos, Armando C. Duarte. Supercritical fluid extraction of bioactive compounds. TrAC Trends in Analytical Chemistry. 2016;76: 40-51.

Yousefi M, Rahimi-Nasrabadi M, Pourmortazavi SM, et al. Supercritical fluid extraction of essential oils. TrAC Trends in Analytical Chemistry. 2019;118:182-193.

Molino, A, Mehariya, S, Iovine, A., Larocca, V, Di Sanzo G, Martino M, et al. Extraction of astaxanthin and lutein from microalga Haematococcus pluvialis in the red phase using CO2 supercritical fluid extraction technology with ethanol as co-solvent. Marine drugs. 2018;16(11):432.

Lu ZX, Fan LW, Zheng DY, Liao YQ, Huang B. Simulation of supercritical CO2 extraction of camellia seed oil by BP neural network. Forest Products Chemistry and Industry. 2010;30(5):12-18. (in Chinese)

Tang WD, Zhu HT. Application of genetic BP neural network in orthogonal experiment optimization. Information technology and information technology. 2004;(6):44-46. (in Chinese)

Song JF, Li DJ, Liu CQ. Prediction of cordycepin supercritical CO2 extraction based on orthogonal test and neural network. Journal of Jiangsu Agriculture. 2010;26(4):833-837. (in Chinese)

Liu HM. BP neural network model to predict the results of supercritical extraction of Angelica dahurica. Lishizhen Medicine and Materia Medica. 2006; 17(2):176-177. (in Chinese)

Sodeifian G, Sajadian SA, Ardestani NS. Evaluation of the response surface and hybrid artificial neural network-genetic algorithm methodologies to determine extraction yield of Ferulago angulata through supercritical fluid. Journal of the Taiwan Institute of Chemical Engineers. 2016;60:165-173.