ANFIS modeling of rhamnolipid breakthrough curves on activated carbon

Abstract Owning to interesting properties of biosurfactants such as biodegradability and lower toxicity, they have broad application in the food industry, healthy products, and bioremediation as well as for oil recovery. The present study was aimed to develop a GA-ANFIS model for predicting the breakthrough curves for rhamnolipid adsorption over activated carbon. To that end, a set of 296 adsorption data points were utilized to train the proposed FIS structure. Different graphical and statistical methods were also used to evaluate the model’s accuracy and reliability. Results were then compared to those of the previously reported Artificial Neural Network (ANN) and Group Method Data Handling (GMDH) models. Absolute average deviation percentage (%AAD) for the proposed model was 1.71% which demonstrates lower value compared to those of ANN and GMDH models. The present ANFIS model can be of immense value for investigating breakthrough curve of rhamnolipid and also it can help chemist who dealing with biosurfactants.

[1]  Alireza Baghban,et al.  Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique , 2015 .

[2]  Farhad Gharagheizi,et al.  Evaluation of Thermal Conductivity of Gases at Atmospheric Pressure through a Corresponding States Method , 2012 .

[3]  M. Mazutti,et al.  Mathematical modeling and simulation of inulinase adsorption in expanded bed column. , 2009, Journal of chromatography. A.

[4]  Catherine N Mulligan,et al.  Environmental applications for biosurfactants. , 2005, Environmental pollution.

[5]  A. Bodour,et al.  Application of a modified drop-collapse technique for surfactant quantitation and screening of biosurfactant-producing microorganisms , 1998 .

[6]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Mark E. Johnson.  Wiley Series in Probability and Mathematical Statistics , 2013 .

[8]  M. Moraveji,et al.  Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures , 2017, Korean Journal of Chemical Engineering.

[9]  Colin R. Goodall,et al.  13 Computation using the QR decomposition , 1993, Computational Statistics.

[10]  Adrian Bonilla-Petriciolet,et al.  Modeling of fixed-bed adsorption of fluoride on bone char using a hybrid neural network approach , 2013 .

[11]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[12]  Miquel Casals,et al.  Physicochemical and Antimicrobial Properties of New Rhamnolipids Produced by Pseudomonas aeruginosa AT10 from Soybean Oil Refinery Wastes , 2001 .

[13]  Sudheer Kumar Singh,et al.  Adsorption—;Desorption Process Using Wood‐Based Activated Carbon for Recovery of Biosurfactant from Fermented Distillery Wastewater , 2008, Biotechnology progress.

[14]  Gloria Soberón-Chávez,et al.  Biosurfactants: A General Overview , 2011 .

[15]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[16]  Carlos Eduardo de Araújo Padilha,et al.  Recovery of Rhamnolipids Produced by Pseudomonas aeruginosa Using Acidic Precipitation, Extraction, and Adsorption on Activated Carbon , 2013 .

[17]  Ali Abbas,et al.  Estimation of air dew point temperature using computational intelligence schemes , 2016 .

[18]  Abdolhossein Hemmati-Sarapardeh,et al.  Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature , 2015, Korean Journal of Chemical Engineering.

[19]  S. Ayatollahi,et al.  Accurate determination of the CO2‐crude oil minimum miscibility pressure of pure and impure CO2 streams: A robust modelling approach , 2016 .

[20]  J. C. Bellot,et al.  Liquid chromatography modelling: a review , 1991 .

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  B. Dabir,et al.  Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system , 2017 .

[23]  Alireza Baghban,et al.  Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches , 2015 .

[24]  I. Banat,et al.  Microbial biosurfactants production, applications and future potential , 2010, Applied Microbiology and Biotechnology.

[25]  G. Pazuki,et al.  Modeling the Thermal Conductivity of Ionic Liquids and Ionanofluids Based on a Group Method of Data Handling and Modified Maxwell Model , 2015 .

[26]  C. Padilha,et al.  Prediction of rhamnolipid breakthrough curves on activated carbon and Amberlite XAD-2 using Artificial Neural Network and Group Method Data Handling models , 2015 .

[27]  Junxian Yun,et al.  Predictive modeling of protein adsorption along the bed height by taking into account the axial nonuniform liquid dispersion and particle classification in expanded beds. , 2005, Journal of chromatography. A.

[28]  Jo‐Shu Chang,et al.  Rhamnolipid production with indigenous Pseudomonas aeruginosa EM1 isolated from oil-contaminated site. , 2008, Bioresource technology.

[29]  A. Mohammadi,et al.  Rigorous modeling of CO2 equilibrium absorption in ionic liquids , 2017 .

[30]  A. Mohammadi,et al.  Prediction of CO2 loading capacities of aqueous solutions ofabsorbents using different computational schemes , 2017 .

[31]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[32]  Ali Naseri,et al.  Asphaltene precipitation due to natural depletion of reservoir: Determination using a SARA fraction based intelligent model , 2013 .