Hossein Rajabi Kuyakhi
University of Gilan
ParticleAlgorithmApproximation errorPolynomial and rational function modelingDiesel fuelNuclear chemistryMean squared errorResponse surface methodologyFirefly algorithmSolubilitySoil scienceInterference (wave propagation)Quadratic functionStatistical parameterPlot (graphics)AsphaltTest dataMultilayer perceptronAqueous solutionChemistryBiological systemAdaptive neuro fuzzy inference systemMean absolute errorMaterials scienceSupercritical fluid extractionPhosphoric acidButanolKetonePolycyclic aromatic hydrocarbonCorrelation coefficientSolvent1-PentanolAlkaneSedimentGaussian membership functionFeed forward artificial neural networkInference systemLight hydrocarbonsLiquid liquidNio nanoparticlesMathematicsThermal diffusivityEnvironmental scienceChemical engineeringExtraction (chemistry)Artificial neural networkHeat capacityPerceptronTernary operationFossil fuelPollutantParticle swarm optimizationGenetic algorithm
10Publications
3H-index
24Citations
Publications 10
Newest
#1Seyed Ali Torabi (University of Gilan)
#2Hossein Ghanadzadeh Gilani (University of Gilan)H-Index: 5
Last. Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
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#1Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
#2Ramin Tahmasebi Boldaji (UI: University of Isfahan)H-Index: 1
Last. Meysam Azadian (University of Gilan)H-Index: 1
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In this paper, two types of machine learning, namely neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP), have been studied to model light hydrocarbons’ solubility solvent in bitum...
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#1Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
#2Ramin Tahmasebi Boldaji (UI: University of Isfahan)H-Index: 1
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In this study, the applicability of the adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) was evaluated to forecasting the supercritical extraction of St. John’s Wort. In this case, the ANFIS model was optimized by the firefly algorithm (FFA) to develop the performance of the model. The accuracy of the models was investigated by comparing the result of the models with experimental data. The precision of the RSM model has been investigated using statistical anal...
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#1Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
#2Omid Zarenia (University of Gilan)H-Index: 2
Last. Ramin Tahmasebi Boldaji (UI: University of Isfahan)H-Index: 1
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Abstract The solvent diffusivity is considered as a key factor in the design of solvent assisted processes in the bitumen field. In this study, a novel Adaptive neuro-fuzzy interference system (ANFIS) is employed to evaluate the diffusivity of the light hydrocarbons in the bitumen system. The particle swarm optimization (PSO) and genetic algorithm (GA) are adopted to promote ANFIS efficiency. The proposed models are established by a prepared dataset from multiple papers in the literature. Temper...
2 CitationsSource
#1Ramin Tahmasebi Boldaji (UI: University of Isfahan)H-Index: 1
#2Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
Last. Nasir Tahmasebi Boldaji (IUT: Isfahan University of Technology)
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In this study, using the response surface methodology (RSM) method, 1-butanol and diesel fuel blends heat capacity is predicted. The study of heat capacity was performed with three factors of molar...
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#1Javad Sayyad Amin (University of Gilan)H-Index: 9
#2Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
Last. Alireza Bahadori (SCU: Southern Cross University)H-Index: 40
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AbstractIn this paper an intelligent model is proposed to predict the amount of organic pollutants in Caspian Sea sediment based on a feed forward artificial neural network (ANN) optimized by parti...
4 CitationsSource
#1Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
#2Ramin Tahmasbi Boldaji (UI: University of Isfahan)H-Index: 1
AbstractThe density has an important role in the oil and gas industries calculation. In this study, an adaptive neuro-fuzzy interference system (ANFIS) model was employed to predict the density of ...
3 CitationsSource
#1Javad Sayyad Amin (University of Gilan)H-Index: 9
#2Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
Last. Alireza Bahadori (SCU: Southern Cross University)H-Index: 40
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AbstractIn this article the amount of polycyclic aromatic hydrocarbon (PAHs) on the sediment of Caspian Sea predicted by artificial neural networks multi-layer perceptron (MLP) and generalized regr...
8 CitationsSource
#1Javad Sayyad Amin (University of Gilan)H-Index: 9
#2Hossein Rajabi Kuyakhi (University of Gilan)H-Index: 3
Last. Alireza Bahadori (SCU: Southern Cross University)H-Index: 40
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AbstractPolycyclic aromatic hydrocarbons (PAHs) are Canceriogenic and mutagenic substances. These compounds are released in the environment by anthropogenic and natural sources. In this work, an adaptive neuro-fuzzy inference system (ANFIS) was employed to model the PAHs formation in sea sediment. Development of ANFIS model is on the basis of the Gaussian membership function. The result obtained by ANFIS model was analyzed with the statistical parameters such as mean squared error (MSE), mean ab...
6 CitationsSource