Application Progress of Artificial Neural Network in Chemical Industry
Xindi Feng
School of Chemical Engineering, East China University of Science and Technology, Shanghai - 200237, China.
Li Sun *
School of Chemical Engineering, East China University of Science and Technology, Shanghai - 200237, China.
*Author to whom correspondence should be addressed.
Abstract
The design and optimization of chemical equipment and devices and the control of chemical processes involve many factors and are very complex. The traditional methods and technologies cannot obtain satisfactory results. Artificial neural network technology has the ability to deal with complex objects and has been widely used in various engineering fields including chemical industry. In this paper, the research progress of artificial neural network in chemical industry is reviewed. The application progress of artificial neural network in signal peak recognition, catalyst optimization, industrial process, reaction process, physical data and other aspects is summarized. The advantages and limitations of artificial neural network in chemical industry are analyzed. Finally, the development trend of its application in chemical industry is prospected.
Keywords: Artificial neural network, reaction process, catalyst selection, physical properties data
How to Cite
Downloads
References
Patel JL, Goyal RK. Applications of artificial neural networks in medical science. Curr Clin Pharmacol. 2007;2(3): 217-26.
Li XR. Detection of soil compactness based on neural network. J Gansu Agric Univ. 2015;4:175-80.
Shi F, Gao J, Huang X. An affine invariant approach for dense wide baseline image matching. Int J Distrib Sens Netw. 2016;12(12).
Mohapatra SK, Kamilla SK, Mohapatra SK. A pathway to hydrogen economy: artificial neural network an approach to prediction of population and number of registered vehicles in India. Adv Sci Lett. 2016;22(2):359-62.
Deville Y. Analysis of the convergence properties of self-normalized source separation neural networks. IEEE Trans Signal Process. 1999;47(5):1272-87.
Gao J, Chakraborty D, Tembine H, Olaleye O. Nonparallel emotional speech conversion. INTERSPEECH 2019, Graz, Austria, September 2019.
Vinyals O, Toshev A, Bengio S, Erhan D. Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):652-63.
Mishra V, Mishra RB. Approach of English to Sanskrit machine translation based on case-based reasoning, artificial neural networks and translation rules. IJKESDP. 2010;2(4):328-48.
Hu SC. Research on face recognition method based on deep learning. Electron Technol. 2019;32(6):82-6.
Gao J, Shi F. A rotation and scale invariant approach for dense wide baseline matching. Intelligent computing theory. ICIC - 10th International Conference. 2014;1:345-56.
Cao Q, Leggio KB, Schniederjans MJ. A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Comput Oper Res. 2005;32(10):2499-512.
Agrawal D, Schorling C. Market share forecasting: an empirical comparison of artificial neural networks and multinomial logit model. J Retailing. 1996;72(4): 383-407.
Li C, Wang C. Application of Artificial Neural Network. In: the Control and Optimization of Distillation Tower[J]; 2021.
An T, Xu JM. Application of artificial neural network in rectification, Asian. J Adv Res Rep. 2022;16(9):1-7.
Gao J. Game-theoretic approaches for generative modeling [D]. New York University, Tandon School of Engineering ProQuest Dissertations Publishing. 2020.27672221.
Tao H, Ebtehaj I, Bonakdari H, Heddam, Voyant, Al-Ansari, et al. Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme. Energies. 2019;12(7):1365.
Yan YP, Wang G, Jiang SJ, et al. Research progress of artificial neural networks in the field of environment. Appl Chem Ind. 2022;51(1):170-6.
Zhang C, Guo Y, Li M. Review of development and application of artificial neural network models. Comput Eng Appl. 2021;57(11):57-69.
Hao HM, Liang YG, Wu HB, et al. Infrared spectrum recognition method based on symmetrized dot patterns coupled with deep convolutional neural network. Spectrosc Spectral Anal. 2021;41(3):782-8.
Guo WC, Shang L, Zhu XH, Nelson SO. Nondestructive detection of soluble solids content of apples from dielectric spectra with ANN and chemometric methods. Food Bioprocess Technol. 2015;8(5):1126-38.
Ling ZG, Yao J, Zhuang K, et al. Process characteristics forecasting for SCR denitration catalyst based on PCA-LMBP neural network model. Therm Power Gener. 2019;48(11):108-14.
Huang K, Cheng Y, Mu ZW, et al. Catalyst design for production of hydrogen from methane based on artificial neural network and genetic algorithm. CIESC J. 2016;67(8):3481-90.
Lou Y. Application of PID control technology based on BP neural network in chlorination process control. Adhesion. 2020;43(9):50-3.
Li XT, Chen ZB, Wei ZQ, et al. Convolution neural network with attention mechanism of input data for quality prediction of fluorine chemical products. Chem Ind Eng Prog. 2022;41(2):8.
Abu-Hamdeh NH, Almitani KH, Gari AA, Alimoradi A, Ahmadian A, Baleanu D. Hydrodynamic analysis of a heat exchanger with crosscut twisted tapes and filled with thermal oil-based SWCNT nanofluid: applying ANN for prediction of objective parameters. J Therm Anal Calorim. 2021;145(4):2163-76.
Carvalho CB, Carvalho EP, Ravagnani MASS. Implementation of a neural network MPC for heat exchanger network temperature control. Braz J Chem Eng. 2020;37(4):729-44.
Mona MV, Aiswaria P, Teja RV, et al. Measurement of solids hold up in a gas–solid fluidized bed: an experimental, statistical and ANN approach. Braz J Chem Eng. 2022.
Abu-Hamdeh NH, Oztop HF, Alnefaie KA, Ahmadian A, Baleanu D. The efects of using corrugated booster refectors to improve the performance of a novel solar collector to apply in cooling PV cells-Navigating performance using ANN. J Therm Anal Calorim. 2021;145(4):2151-62.
Gill J, Singh J, Ohunakin OS, Adelekan DS. Energy analysis of a domestic refrigerator system with ANN using LPG/TiO2-lubricant as replacement for R134a. J Therm Anal Calorim. 2019;135(1):475-88.
Zhu YH, Fu Y, Li XL, et al. Flame image classification method of ceramic shuttle kiln based on improved convolution neural network. J Ceram. 2022;43(2):302-9.
Kumar A, Singh AP. Transistor level fault diagnosis in digital circuits using artificial neural network. Measurement. 2016;82:384-90.
Alonso M, Cuadra J, González-Hernández JL. Ann-matopt hybrid a1gorithm detennination of kinetic and non kinetic params in different reaction mechanisms[J]. J Math Chem. 2010; 1-28.
Banerjee A, Varshney D, Kumar S, Chaudhary P, Gupta VK. Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation. Int J Ind Chem. 2017;8(3):253-62.
Kadiri O, Gbadamosi SO, Akanbi CT. Extraction kinetics, modelling and optimization of phenolic antioxidants from sweet potato peel vis-a-vis RSM, ANN-GA and application in functional noodles. J Food Meas Char. 2019;13(4):3267-84.
Leo GML, Sekar S, Arivazhagan S. Experimental investigation and ANN modelling of the effects of diesel/gasoline premixing in a waste cooking oil-fuelled HCCI-DI engine. J Therm Anal Calorim. 2020;141(6):2311-24.
Berkani M, Bouchareb MK, Bouhelassa M, Kadmi Y. Photocatalytic degradation of industrial dye in semi-pilot scale prototype solar photoreactor: optimization and modeling using ANN and RSM based on box–Wilson approach. Top Catal. 2020;63(11-14):964-75.
Airemlou L, Behnajady MA, Mahanpoor K. Photocatalytic removal of RhB by ag and MG Co-doped ZnO nanoparticles: modeling of operational parameters using ANN based on RSM data. Russ J Phys Chem. 2019;93(9):1769-77.
Wu WJ, Zhang ZH, Li XC, et al. Analysis of process conditions for the preparation of C4 olefins by ethanol coupling based on RBF neural network model. J Jianghan Univ （Nat Sci Educ）. 2022;38(J):50(3):7.
Igwegbe CA, Adeniyi AG, Ighalo JO. ANN modelling of the steam reforming of naphthalene based on non-stoichiometric thermodynamic analysis. Chem Pap. 2021;75(7):3363-72.
Mats RR, Jose RH, Kalle A, et al. ANN modeling applied to NOx reduction with octane in a new microreactor. Topics in catalysis;2007(5):195-8.
Rasmi RB, Ranjan KG, Kanak K, et al. Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN. Int J Plast Technol; 2016.
Nasiri M, Hojjat M, Etemad SG, Bagheri R. Cooling performance of Newtonian and non-Newtonian nanofluids in a square channel: experimental investigation and ANN modeling. J Therm Anal Calorim. 2020;142(6):2189-202.
Esfe MH, Rejvani M, Karimpour R, Abbasian Arani AA. Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data. J Therm Anal Calorim. 2017;128(3):1359-71.
Belayadi A, Mougari A, Zabat M. Modeling of electrochemical properties of potential-induced defects in butane-thiol SAMs by using artificial neural network and impedance spectroscopy data. J Solid State Electrochem. 2019;23(1):195-204.
Deeb O, Khadikar PV, Goodarzi M. Prediction of gas/particle partitioning coefficients of semi volatile organic compounds via QSPR methods: PC-ANN and PLS analysis. J Iran Chem Soc. 2011;8(1):176-92.
Mozafari Z, Arab Chamjangali M, Arashi M, Goudarzi N. QSRR models for predicting the retention indices of VOCs in different datasets using an efficient variable selection method coupled with artificial neural network modeling: ANN-based QSPR modeling. J Iran Chem Soc. 2022;19(6):2617-30.
Nagaraj A, Gopalakrishnan S. A study on mechanical and tribological properties of aluminium 1100 alloys 6% of RHAp, BAp, CSAp, ZnOp and egg Shellp composites by ANN. Silicon. 2021;13(10):3367-76.
Maddah H, Aghayari R, Ahmadi MH, Rahimzadeh M, Ghasemi N. Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM). J Therm Anal Calorim. 2018;134(3):2275-86.
Tatar F, Cengiz A, Kahyaoglu T. Effect of hemicellulose as a coating material on water sorption thermodynamics of the microencapsulated fish oil and artificial neural network (ANN) modeling of isotherms. Food Bioprocess Technol. 2014;7(10):2793-802.
Kim B, Choi Y, Choi J, Shin Y, Lee S. Effect of surfactant on wetting due to fouling in membrane distillation membrane: application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Korean J Chem Eng. 2020;37(1):1-10.
Adeniyi AG, Igwegbe CA, Ighalo JO. ANN modelling of the adsorption of herbicides and pesticides based on sorbate-sorbent interphase. Chem Afr. 2021;4(2):443-9.
Mao J, Zhao HD, Yao Q. Application and prospect of Artificial Neural Network. Electron Des Eng. 2011;19(24):62-5.
Wiecha PR, Muskens OL. Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures. Nano Lett. 2020;20(1):329-38.
Wang S, Fan K, Luo N, Cao Y, Wu F, Zhang C, et al. Massive computational acceleration by using neural networks to emulate mechanism-based biological models. Nat Commun. 2019;10(1):4354
Xue W. Construction of low carbon city economic security management system based on BP artificial neural network. Sustain Energy Technol Assess; 2022.