Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network

Asoodeh, Mojtaba; Gholami, Amin; Bagheripour, Parisa

doi:10.1016/j.fluid.2013.12.016

Cited by 34 publications

(12 citation statements)

References 31 publications

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“…However, when applied to regression problems, NN suffers from trap into local optima and cause the premature convergence phenomenon, this may lead to great prediction error from the constructed model on testing data. To remedy this fault, different researchers try to embed optimization algorithm in NN formulation for optimize its parameters and achieve acceptable improvement (Zargar et al 2015a;Gholami et al 2014bGholami et al , 2015aAsoodeh et al 2014b). In current study, BA algorithm is adopted for optimization of neural network parameters.…”

Section: Optimized Neural Networkmentioning

confidence: 99%

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Gholami

Bodaghi

2016

Model. Earth Syst. Environ.

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Owing to high dependency of landslide stability to residual shear strength (RSS) of clay, provide a sophisticated strategy for modeling of this parameter is advantageous. This paper present strategy based upon fusing of optimized intelligence models for estimation of RSS of clay as a function of readily available data. The developed model is achieved through implementing two following steps. In the first step, two optimized models including optimized neural network, and optimized fuzzy logic are developed for estimation of RSS of clay. Optimizing method which implanted in predictive models for improving those performance is bat-inspired algorithm. In second step, committee machine (CM) is employed for combining outputs of aforementioned optimized models. Bat-inspired is incorporated in CM for determining optimal contribution of optimized elements in final prediction. The superior performance of CM rather than its elements is ascertain through those evaluation based on statistical criteria. Results of this study infer that proposed methodology provide an alternative way for making quantitative formulation between RSS of clay and its index properties.Keywords Residual shear strength (RSS) Á Optimized neural network (ONN) Á Optimized fuzzy logic (OFL) Á Committee machine (CM) Á Bat-inspired algorithm (BA)

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Section: Optimized Neural Networkmentioning

confidence: 99%

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Gholami

Bodaghi

2016

Model. Earth Syst. Environ.

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“…The GA uses three main rules including selection, crossover and mutation rules to converge individuals into global minimum. Finally, the GA terminates when a stop condition such as maximum number of generation is satisfied [53][54][55].…”

Section: Genetic Algorithmmentioning

confidence: 99%

An improved support vector regression model for estimation of saturation pressure of crude oils

Ansari

Gholami

2015

Fluid Phase Equilibria

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“…Manshad et al applied fuzzy logic, genetic algorithm, and artificial neural network approaches for modelling the amount of asphaltene precipitation in live and stock tank crude oil systems. Asoodeh et al combined fuzzy logic and neural network approaches with a genetic algorithm and proposed an accurate model for estimating asphaltene precipitation titration data. Fattahi et al developed a hybrid support vector regression along with harmony search for estimating asphaltene precipitation titration data.…”

Section: Introductionmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Asphaltene precipitation of titration data modeling through committee machine with stochastically optimized fuzzy logic and optimized neural network

Cited by 34 publications

References 31 publications

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

An improved support vector regression model for estimation of saturation pressure of crude oils

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

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