Keywords
Taylor diagram
Artificial neural network (ANN)
Adaptive neuro-fuzzy inference system (ANFIS)
Least squares support vector machine (LSSVM)
How to Cite
Abstract
Viscosity is an essential property in chemical engineering for different applications. If predicted accurately, the viscosity has a significant effect on chemical applications. In this paper, the capability of the three intelligence models, artificial neural network (ANN), least squares support vector machine (LSSVM), and adaptive neuro-fuzzy inference system (ANFIS), were evaluated to model the dynamic viscosity of n-alcohol at the different operational conditions. The models were improved based on a 237 data set collected from reliable articles. The used databank contains temperature (T), pressure (P), and carbon number of n-alcohols (n-C) were chosen as the input of models. The result of these models was studied by Statistical parameters such as Mean of the squares errors (MSE), Root mean of the squares errors (RMSE), Maximum absolute error (MAAE %), Mean absolute error (MEAE %), correlation coefficient and graphical technique like Taylor diagram and William plot. The proposed models are known to appropriately estimate the viscosity of n-alcohol at the different operational conditions. It was found that the ANN with R2= 0.999, MSE=0.000017, MAAE %= 1.6, MEAE %=0.32 for Test, and R2= 0.999, MSE=0.0000094, MAAE %= 0.83, MEAE %=0.23 for Train exhibited a high performance than LSSVM and ANFIS for predicting dynamic viscosity of n-alcohol at the operational conditions.
References
Cano-Gómez JJ, Iglesias-Silva GA, Ramos-Estrada M, Hall KR. Densities and viscosities for binary liquid mixtures of Ethanol + 1-Propanol, 1-Butanol, and 1-Pentanol from (293.15 to 328.15) K at 0.1 MPa. J Chem Eng Data. 2012; 57: 2560-7. https://doi.org/10.1021/je300632p
Kumagai A, Yokoyama C. Liquid viscosity of binary mixtures of methanol with Ethanol and 1-Propanol from 273.15 to 333.15 K. Int J Thermophys. 1998; 19: 3-13. https://doi.org/10.1023/A:1021438800094
Cano-Gómez JJ, Iglesias-Silva GA, Ramos-Estrada M. Correlations for the prediction of the density and viscosity of 1-alcohols at high pressures. Fluid Phase Equilib. 2015; 404:109-17. https://doi.org/10.1016/j.fluid.2015.06.042
Guzmán-López A, Iglesias-Silva GA, Reyes-García F, Estrada-Baltazar A, Ramos-Estrada M. Densities and viscosities for binary liquid mixtures of n-undecane + 1-Heptanol, 1-Octanol, 1-Nonanol, and 1-Decanol from 283.15 to 363.15 K at 0.1 MPa. J Chem Eng Data. 2017; 62: 780-95. https://doi.org/10.1021/acs.jced.6b00834
Matsuo S, Makita T. Viscosities of six 1-Alkanols at temperatures in the range 298? 348 K and pressures up to 200 MPa. Int J Thermophys. 1989; 10: 833-43. https://doi.org/10.1007/BF00514479
Abdi-Khanghah M, Bemani A, Naserzadeh Z, Zhang Z. Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN. J CO2 Util. 2018; 25: 108-19. https://doi.org/10.1016/j.jcou.2018.03.008
Eghtedaei R, Abdi-khanghah M, Najar BSA, Baghban A. Viscosity estimation of mixed oil using RBF-ANN approach. Pet Sci Technol. 2017; 35: 1731-6. https://doi.org/10.1080/10916466.2017.1365084
Baghban A, Namvarrechi S, Phung LTK, Lee M, Bahadori A, Kashiwao T. Phase equilibrium modelling of natural gas hydrate formation conditions using LSSVM approach. Pet Sci Technol. 2016; 34: 1431-8. https://doi.org/10.1080/10916466.2016.1202966
Baghban A, Ahmadi MA, Hashemi Shahraki B. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. J Supercrit Fluids. 2015; 98: 50-64. https://doi.org/10.1016/j.supflu.2015.01.002
Amin JS, Kuyakhi HR, Kashiwao T, Bahadori A. Development of ANFIS models for polycyclic aromatic hydrocarbons (PAHs) formation in sea sediment. Pet Sci Technol. 2019; 37: 679–86. https://doi.org/10.1080/10916466.2018.1563613
Heidari E, Sobati MA, Movahedirad S. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemom Intell Lab Sys. 2016; 155: 73-85. https://doi.org/10.1016/j.chemolab.2016.03.031
Toghraie D, Sina N, Jolfaei NA, Hajian M, Afrand M. Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles. Phys A: Stat Mech Appl. 2019; 534(15): 122142. https://doi.org/10.1016/j.physa.2019.122142
Chen H, Fu Q, Liao Q, Zhu X, Shah A. Applying artificial neural network to predict the viscosity of microalgae slurry in hydrothermal hydrolysis process. Energy AI. 2021; 4: 100053. https://doi.org/10.1016/j.egyai.2021.100053.
Pimentel-Rodas A, Galicia-Luna LA, Castro-Arellano JJ. Viscosity and density of n-Alcohols at Temperatures between (298.15 and 323.15) K and Pressures up to 30 MPa. J Chem Eng Data. 2018; 64: 324-36. https://doi.org/10.1021/acs.jced.8b00812
Kuyakhi HR, Boldaji RT. Developing an adaptive neuro‐fuzzy inference system based on particle swarm optimization model for forecasting Cr (VI) removal by NiO nanoparticles. Environ Prog Sustain Energy. 2021; 40(4): e13597. https://doi.org/10.1002/ep.13597
Taghavi H, Taghavi H. Proposing a novel and accurate model for forecasting dynamic viscosity of n -alkane in operational conditions. Pet Sci Technol. 2018; 36: 1531-6. https://doi.org/10.1080/10916466.2018.1476537
Kuyakhi HR, Boldaji RT. A novel ANFIS model to prediction of the density of n-alkane in different operational condition. Pet Sci Technol. 2019; 37: 2429-34. https://doi.org/10.1080/10916466.2019.1616756
Soroush E, Shahsavari S, Mesbah M, Rezakazemi M. A robust predictive tool for estimating CO2 solubility in potassium based amino acid salt solutions. Chin J Chem Eng. 2018; 26 (4): 740-6. https://doi.org/10.1016/j.cjche.2017.10.002
Baghban A, Ahmadi MA, Shahraki BH. Prediction carbon dioxide solubility in the presence of various ionic liquids using computational intelligence approaches. J Supercrit Fluids. 2015; 98: 50-64. https://doi.org/10.1016/j.supflu.2015.01.002
Amin JS, Kuyakhi HR, Bahadori A. Prediction of formation of polycyclic aromatic hydrocarbon (PAHs) on sediment of Caspian Sea using artificial neural networks. Pet Sci Technol. 2019; 37: 1987-2000. https://doi.org/10.1080/10916466.2018.1496111
Alimohammadi S, Amin JS, Nikooee E. Estimation of asphaltene precipitation in light, medium and heavy oils: experimental study and neural network modeling. Neural Comput Appl. 2017; 28: 679-94. https://doi.org/10.1007/s00521-015-2097-3
Khosravi A, Machado L, Nunes RO. Estimation of density and compressibility factor of natural gas using artificial intelligence approach. J Pet Sci Eng. 2018; 168: 201-16. https://doi.org/10.1016/j.petrol.2018.05.023
Rajabi Kuyakhi H, Boldaji RT, Azadian M. Light hydrocarbons solvents solubility modeling in bitumen using learning approaches. Pet Sci Technol. 2021; 39: 115-31. https://doi.org/10.1080/10916466.2020.1863986
Ghiasi MM, Mohammadi AH, Zendehboudi S. Modeling stability conditions of methane Clathrate hydrate in ionic liquid aqueous solutions. J Mol Liq. 2021; 325, 114804. https://doi.org/10.1016/j.molliq.2020.114804
Ghiasi MM, Yarveicy H, Arabloo M, Mohammadi AH, Behbahani RM. Modeling of stability conditions of natural gas clathrate hydrates using least squares support vector machine approach. J Mol Liq. 2016; 223: 1081-92. https://doi.org/10.1016/j.molliq.2016.09.009
Ghiasi MM, Mohammadi AH. Rigorous modeling of CO2 equilibrium absorption in MEA, DEA, and TEA aqueous solutions. J Nat Gas Sci Eng. 2014; 18: 39-46. https://doi.org/10.1016/j.jngse.2014.01.005
Kardani N, Bardhan A, Roy B, Samui P, Nazem M, Armaghani DJ, et al. A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates. Eng Comput. 2022; 38: 4323-46. https://doi.org/10.1007/s00366-021-01466-9
Bi J, Bennett KP. Regression Error Characteristic Curves. In: Fawcett T, Mishra N, Eds. Proceedings of the twentieth international conference on machine learning, August 21-24, 2003, Washington, DC, Menlo Park, California: The AAAI Press; 2003, pp. 43-50.
Amin JS, Nikkhah S, Veiskarami M. A statistical method for assessment of the existing correlations of hydrate forming conditions. J Energy Chem. 2015; 24: 93-100. https://doi.org/10.1016/S2095-4956(15)60289-3
Soroush E, Mesbah M, Hajilary N, Rezakazemi M. ANFIS modeling for prediction of CO2 solubility in potassium and sodium based amino acid Salt solutions. J Environ Chem Eng. 2019; 7(1), 102925. https://doi.org/10.1016/j.jece.2019.102925
Kuyakhi HR, Zarenia O, Boldaji RT. Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen. Heliyon 2020; 6(9): e04936. https://doi.org/10.1016/j.heliyon.2020.e04936
Sayyad Amin J, Alimohammadi S, Zendehboudi S. Systematic investigation of asphaltene precipitation by experimental and reliable deterministic tools. Can J Chem Eng. 2017; 95: 1388-98. https://doi.org/10.1002/cjce.22820
Golsefatan A, Shahbazi K. Predicting the effect of nanocomposites on asphaltene removal using a comprehensive approach. Pet Sci Technol. 2020; 38: 64-73. https://doi.org/10.1080/10916466.2019.1656241
Pan W. Akaike’s information criterion in generalized estimating equations. Biometrics. 2001; 57: 12055. https://doi.org/10.1111/j.0006-341X.2001.00120.x
Bonakdari H, Binns AD, Gharabaghi B. A comparative study of linear stochastic with nonlinear daily river discharge forecast models. Water Resour Manag. 2020; 34: 3689-708. https://doi.org/10.1007/s11269-020-02644-y
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