The ANFIS-PSO strategy as a novel method to predict interfacial tension of hydrocarbons and brine

Darvish, Houman; Rahmani, Siamak; Sadeghi, Ashkan Maleki; Baghdadi, Mohammad Hosein Emami

doi:10.1080/10916466.2018.1442852

Cited by 10 publications

(11 citation statements)

References 20 publications

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“…The total membership in the conclusion component comprises each chromosome equal to (N + 1) S, where N is the total data input and S is the number of rule base. So, the value of the fitness expression has been estimated with root mean square error (RMSE) [21], [22], [24].…”

Section: Hybrid Anfis-pso-based Mppt Controllermentioning

confidence: 99%

“…6 is 0.5 for 100 epochs of training data set. The mathematical relations present in [21], [22], and [24] have been used to calculate the maximum error value.…”

Section: Hybrid Anfis-pso-based Mppt Controllermentioning

confidence: 99%

“…However, the gas density has been estimated using a graphical and statistical approach. In [22], the hydrocarbons' interfacial tension is predicted using the ANFIS-PSO approach. However, the proposed algorithm with experimental estimation is not addressed.…”

Section: Introductionmentioning

confidence: 99%

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An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance

Priyadarshi¹,

Padmanaban

Holm‐Nielsen

et al. 2020

IEEE Systems Journal

299

103

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To enhance the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the foremost constituent. This article introduces an adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking. The inverter control strategy is implemented by a space vector modulation hysteresis current controller to get quality inverter current by tracking accurate reference sine-shaped current. The ANFIS-PSO-based MPPT method has no extra sensor requirement for measurement of irradiance and temperature variables. The employed methodology delivers remarkable driving control to enhance PV potential extraction. An ANFIS-PSO-controlled Zeta converter is also modeled as an impedance matching interface with zero output harmonic agreement and kept between PV modules and load regulator power circuit to perform MPPT action. The attainment of recommended hybrid ANFIS-PSO design is equated with perturb and observe, PSO, ant colony optimization, and artificial bee colony MPPT methods for the PV system. The practical validation of the proposed grid-integrated PV system is done through MATLAB interfaced dSPACE interface and the obtained responses accurately justify the proper design of control algorithms employed with superior performance.Index Terms-adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), fuzzy logic control (FLC), maximum power point tracking (MPPT), photovoltaic (PV) system, space vector modulation hysteresis current controller (SVMHCC), Zeta converter. NOMENCLATURE I photPhotocurrent. I RSC Reversed saturating current. V Thm Thermal voltage. G S Sun insolation.

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Section: Hybrid Anfis-pso-based Mppt Controllermentioning

confidence: 99%

“…6 is 0.5 for 100 epochs of training data set. The mathematical relations present in [21], [22], and [24] have been used to calculate the maximum error value.…”

Section: Hybrid Anfis-pso-based Mppt Controllermentioning

confidence: 99%

See 1 more Smart Citation

An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance

Priyadarshi¹,

Padmanaban

Holm‐Nielsen

et al. 2020

IEEE Systems Journal

299

103

View full text Add to dashboard Cite

show abstract

“…The hybrid model of the ANFIS-PSO (also known as PSO-ANFIS) appeared in the works of Catalao et al [79,80] in early 2011 for the prediction of wind energy and electricity pricing. Since then, this method has been used in various applications, e.g., load shedding, electricity prices forecasting, hydrofoil, travel time estimation, prediction of viscosity of mixed oils, matrix membranes modeling, wax deposition, electric power forecasting, asphaltene precipitation, prediction of the density of bitumen diluted with solvents, heating value of biomass, predicted interfacial tension of hydrocarbons and brine, prediction of gas density, forecasting oil flocculated asphaltene, biodiesel efficiency, biomass heating modeling, prediction of property damage, and solar radiation forecasting [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97]. The ability to generalize, higher accuracy, speed, and ease of use have been reported as the main characteristics of the ANFIS-PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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“…The hybrid model of ANFIS-PSO (also known as PSO-ANFIS) has been appeared in the works of Catalao et al [79,80], in early 2011 for prediction of wind energy and electricity pricing prediction. Since then this method has been used in various applications, e.g., load shedding, electricity prices forecasting, hydrofoil, travel time estimation, prediction of viscosity of mixed oils, matrix membranes modeling, wax deposition, electric power forecasting, asphaltene precipitation, prediction of density of bitumen diluted with solvents, heating value of biomass, predict interfacial tension of hydrocarbons and brine, prediction of gas density, forecasting oil flocculated asphaltene, biodiesel efficiency, Biomass higher heating modeling, prediction of property damage, and solar radiation forecasting [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97]. The generalization ability, higher accuracy, speed, and ease of use have been reported as the main characteristics of ANFIS-PSO.…”

Section: Previous Investigationsmentioning

confidence: 99%

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

View full text Add to dashboard Cite

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations' boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively.Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

show abstract

The ANFIS-PSO strategy as a novel method to predict interfacial tension of hydrocarbons and brine

Cited by 10 publications

References 20 publications

An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance

An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

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