Approach of Different Properties of Alkylammonium Surfactants using Artificial Intelligence and Response Surface Methodology
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Gonzalo Astray
Abstract
Response surface methodology (RSM) and artificial neural networks (ANNs) architectures to predict the density, speed of sound, kinematic viscosity, and surface tension of aqueous solutions were developed. All models implemented using the root mean square error (RMSE) for training and validation phase were evaluated. The ANN models implemented show good values of R2 (upper than 0.974) and low errors in terms of average percentage deviation (APD) (lower than 2.92 %). Nevertheless, RSM models present low APD values for density and speed of sound prediction (lower than 0.31 %) and higher APD values around 5.18 % for kinematic viscosity and 14.73 % for surface tension. The results show that the different individual artificial neural networks implemented are a useful tool to predict the density, speed of sound, kinematic viscosity, and surface tension with reasonably accuracy.
Kurzfassung
Es wurden Response-Surface-Methoden (RSM) und künstliche neuronale Netzwerke (ANNs) zur Vorhersage der Dichte, der Schallgeschwindigkeit, der kinematischen Viskosität und der Oberflächenspannung von wässrigen Lösungen entwickelt. Alle umgesetzten Modelle wurden in der Trainings- und Validierungsphase mit der Methode der mittleren quadratischen Fehler (RMSE) bewertet. Die umgesetzten ANN-Modelle lieferten gute R2-Werte (größer als 0,974) und hinsichtlich der durchschnittlichen prozentualen Abweichung (APD) geringe Fehler (geringer als 2,92 %). Dennoch erreichten die RSM-Modelle niedrige APD-Werte für die Dichte und die Schallgeschwindigkeit (kleiner als 0,31 %) und höhere APD-Werte von etwa 5,18 % für die kinematische Viskosität und von 14,73 % für die Oberflächenspannung. Die Ergebnisse machen deutlich, dass die verschiedenen individuellen künstlichen neuronalen Netzwerke nützlich sind, um die Dichte, die Schallgeschwindigkeit, die kinematischen Viskosität und die Oberflächenspannung mit einer hinreichenden Genauigkeit vorherzusagen.
References
1. Gómez-Díaz D. , NavazaJ. M. and SanjurjoB.: Density, kinematic viscosity, speed of sound, and surface tension of hexyl, octyl, and decyl trimethyl ammonium bromide aqueous solutions, J Chem Eng Data52 (2007) 889–891. 10.1021/je060486kSuche in Google Scholar
2. Hörmann K. and ZimmerA.: Drug delivery and drug targeting with parenteral lipid nanoemulsions – A review, J Control Release223 (2016) 85–98. 10.1016/j.jconrel.2015.12.016Suche in Google Scholar
3. Pusapati M. R. , GuntukuG., YarlagaddaA. C., NagarajuG., SoumyaM. and LakshmiT. B. V.: An overview of classification, production and biomedical applications of biosurfactants, Res J Pharm Technol7 (2014) 608–617. DOI: not available.Suche in Google Scholar
4. Burnett C. L. , HeldrethB., BergfeldW. F., BelsitoD. V., HillR. A., KlaassenC. D., LieblerD. C., MarksJ. G., ShankR. C., SlagaT. J., SnyderP. W. and AndersenF. A.: Safety Assessment of PEGylated oils as used in cosmetics, Int J Toxicol.33 (2014) 13S–39S. 10.1177/1091581814546337Suche in Google Scholar
5. Polarz S. , OdendalJ. A., HermannS. and KlaiberA.: Amphiphilic hybrids containing inorganic constituent: More than soap, Curr Opin Colloid Interface Sci20 (2015) 151–160. 10.1016/j.cocis.2015.07.006Suche in Google Scholar
6. Gerster F. M. , VernezD., WildP. P. and HopfN. B.: Hazardous substances in frequently used professional cleaning products, Int J Occup Environ Health20 (2014) 46–60. 10.1179/2049396713Y.0000000052Suche in Google Scholar
7. Gómez-Díaz D. , NavazaJ. M. and SanjurjoB.: Density, kinematic viscosity, speed of sound, and surface tension of tetradecyl and octadecyl trimethyl ammonium bromide aqueous solutions, J Chem Eng Data52 (2007) 2091–2093. 10.1021/je7003148Suche in Google Scholar
8. Akintunde A. M. , AjalaS. O. and BetikuE.: Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate, Ind Crops Prod67 (2015) 387–394. 10.1016/j.indcrop.2015.01.056Suche in Google Scholar
9. Bas D. and BoyaciI. H.: Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction, J Food Eng78 (2007) 846–854. 10.1016/j.jfoodeng.2005.11.025Suche in Google Scholar
10. Box G. E. P. and WilsonK. B.: On the experimental attainment of optimum conditions. J R Stat Soc B13 (1951) 1–45. DOI: not available.Suche in Google Scholar
11. Fang S. E. and PereraR.: A response surface methodology based damage identification technique, Smart Mater. Struct.18, 6 (2009). 10.1088/0964-1726/18/6/065009Suche in Google Scholar
12. Carvalho R. A. , MariaT. M. C., MoraesI. C. F., BergoP. V. A., KamimuraE. S., HabitanteA. M. Q. B. and SobralP. J. A.: Study of some physical properties of biodegradable films based on blends of gelatin and poly(vinyl alcohol) using a response-surface methodology, Materials Science and Engineering: C29 (2009) 485–491. 10.1016/j.msec.2008.08.030Suche in Google Scholar
13. Madamba P. S. : The Response Surface Methodology: An Application to Optimize Dehydration Operations of Selected Agricultural Crops, LWT – Food Science and Technology35 (2002) 584–592. 10.1006/fstl.2002.0914Suche in Google Scholar
14. Martínez M. , GullónB., YáñezR., AlonsoJ. L. and ParajóJ. C.: Direct enzymatic production of oligosaccharide mixtures from sugar beet pulp: Experimental evaluation and mathematical modeling, J Agric Food Chem57 (2009) 5510–5517. 10.1021/jf900654 gSuche in Google Scholar
15. Jung E. and JooN.: Roselle (Hibiscus sabdariffa l.) and soybean oil effects on quality characteristics of pork patties studied by response surface methodology, Meat Sci94 (2013) 391–401. 10.1016/j.meatsci.2013.02.008Suche in Google Scholar
16. Desai K. M. , SurvaseS. A., SaudagarP. S., LeleS. S. and SinghalR. S.: Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan, Biochem Eng J41 (2008) 266–273. 10.1016/j.bej.2008.05.009Suche in Google Scholar
17. Ren Y. , SuganthanP. N. and SrikanthN.: A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods, IEEE Trans Sustainable Energy6 (2015) 236–244. 10.1109/TSTE.2014.2365580Suche in Google Scholar
18. Dowell J. A. , HussainA., DevaneJ. and YoungD.: Artificial neural networks applied to the in vitro-in vivo correlation of an extended-release formulation: Initial trials and experience, J Pharm Sci88 (1999) 154–160. 10.1021/js970148pSuche in Google Scholar
19. Sánchez-Mesa J. A. , GalanC., Martínez-HerasJ. A. and Hervás-Martínez,C.: The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: The southern part of the Iberian Peninsula, Clinical and Experimental Allergy32 (2002) 1606–1612. 10.1046/j.1365-2222.2002.01510.xSuche in Google Scholar
20. Chandwani V. , AgrawalV. and NagarR.: Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks, Expert Syst Appl42 (2015) 885–893. 10.1016/j.eswa.2014.08.048Suche in Google Scholar
21. Nastos P. T. , PaliatsosA. G., KoukouletsosK. V., LarissiI. K. and MoustrisK. P.: Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece, Atmos Res144 (2014) 141–150. 10.1016/j.atmosres.2013.11.013Suche in Google Scholar
22. Astray G. , CadernoP. V., Ferreiro-LageJ. A., GalvezJ. F. and MejutoJ. C.: Prediction of ethene + oct-1-ene copolymerization ideal conditions using artificial neuron networks, J Chem Eng Data55 (2010) 3542–3547. 10.1021/je1001973Suche in Google Scholar
23. Cheng J. , ChenX., ZhaoS. and ZhangY.: Antioxidant-capacity-based models for the prediction of acrylamide reduction by flavonoids, Food Chem168 (2015) 90–99. 10.1016/j.foodchem.2014.07.008Suche in Google Scholar
24. Astray G. , Iglesias-OteroM. A., MoldesO. A. and MejutoJ. C.: Predicting critical micelle concentration values of non-ionic surfactants by using artificial neural networks, Tenside Surfactants Deterg50 (2013) 118–124. 10.3139/113.110242Suche in Google Scholar
25. Moldes ÓA. , MoralesJ., CidA., AstrayG., MontoyaI. A. and MejutoJ. C.: Electrical percolation of AOT-based microemulsions with n-alcohols, J Mol Liq215 (2016) 18–23. 10.1016/j.molliq.2015.12.021Suche in Google Scholar
26. Moldes Ó. A. , CidA., MontoyaI. A. and MejutoJ. C.: Linear polyethers as additives for AOT-based microemulsions: Prediction of percolation temperature changes using artificial neural networks, Tenside Surfactants Deterg52 (2015) 264–270. 10.3139/113.110374Suche in Google Scholar
27. Hernández Suárez M. , Astray DopazoG., Larios LópezD. and EspinosaF.: Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs), PLoS ONE10 (2015). 10.1371/journal.pone.0128566Suche in Google Scholar
28. Astray G. , Fernández-GonzálezM., Rodríguez-RajoF. J., LópezD. and MejutoJ. C.: Airborne castanea pollen forecasting model for ecological and allergological implementation, Sci Total Environ548–549 (2016) 110–121. 10.1016/j.scitotenv.2016.01.035Suche in Google Scholar
29. Cybenko G. : Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems2 (1989) 303–314. 10.1007/BF02551274Suche in Google Scholar
© 2017, Carl Hanser Publisher, Munich
Artikel in diesem Heft
- Contents/Inhalt
- Contents
- Review
- Origin, Properties, Production and Purification of Microbial Surfactants as Molecules with Immense Commercial Potential
- Novel Surfactants
- Determination of Surface-Active Characteristics of a Natural Surfactant Extracted from Sapindus Saponaria
- Environmental Chemistry
- Effect of Contaminated Water with Laundry Detergent on Foxtail Millet Root and Physiological Traits
- Washing Technology
- Influence of Water Circulation in Household Washing Machines on Cleaning Performance
- Physical Chemistry
- Approach of Different Properties of Alkylammonium Surfactants using Artificial Intelligence and Response Surface Methodology
- Micellar Parameters of Cationic Surfactant Cetylpyridinium Bromide in Aqueous Solutions of Amino Acids at Different Temperatures: Conductometric, Surface Tension, Volumetric and Viscosity Study
- Cloud Point of Mixed Ionic-Nonionic Surfactant Solutions in the Presence of Inorganic Salts
- Surface/Interfacial Tension, Wettability and Foaming Properties of Bi-Component Nonylphenol Alkyl Sulfonates based on Linear Alpha Olefin
- Application
- Characterization and Surface Active Properties of Aliphatic Glycerol Acetal Disodium Sulfosuccinates
- A Comparative Study on the Cloud Point Extraction Behavior of Copper(II) from Sulphate Medium by N,N′-Bis(Salicylidene)Ethylenediamine using Triton X-100 and Tergitol 15-S-7 as Non-ionic Surfactants
Artikel in diesem Heft
- Contents/Inhalt
- Contents
- Review
- Origin, Properties, Production and Purification of Microbial Surfactants as Molecules with Immense Commercial Potential
- Novel Surfactants
- Determination of Surface-Active Characteristics of a Natural Surfactant Extracted from Sapindus Saponaria
- Environmental Chemistry
- Effect of Contaminated Water with Laundry Detergent on Foxtail Millet Root and Physiological Traits
- Washing Technology
- Influence of Water Circulation in Household Washing Machines on Cleaning Performance
- Physical Chemistry
- Approach of Different Properties of Alkylammonium Surfactants using Artificial Intelligence and Response Surface Methodology
- Micellar Parameters of Cationic Surfactant Cetylpyridinium Bromide in Aqueous Solutions of Amino Acids at Different Temperatures: Conductometric, Surface Tension, Volumetric and Viscosity Study
- Cloud Point of Mixed Ionic-Nonionic Surfactant Solutions in the Presence of Inorganic Salts
- Surface/Interfacial Tension, Wettability and Foaming Properties of Bi-Component Nonylphenol Alkyl Sulfonates based on Linear Alpha Olefin
- Application
- Characterization and Surface Active Properties of Aliphatic Glycerol Acetal Disodium Sulfosuccinates
- A Comparative Study on the Cloud Point Extraction Behavior of Copper(II) from Sulphate Medium by N,N′-Bis(Salicylidene)Ethylenediamine using Triton X-100 and Tergitol 15-S-7 as Non-ionic Surfactants
