Estimation of natural gases water content using adaptive neuro-fuzzy inference system

Baghban, Alireza Akbarzadeh; Kashiwao, Tomoaki; Bahadori, Meysam; Ahmad, Zainal Arifin; Bahadori, Alireza

doi:10.1080/10916466.2016.1176039

Cited by 39 publications

(11 citation statements)

References 25 publications

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“…Nonetheless, the quality and accuracy of the modeling can still be significantly boosted by optimizing the parameters of the model 49 . Therefore, in order to improve the parameters and the solutions provided by the ANFIS system, a large number of various optimizations methods, including PSO, have been proposed 61 . In comparison to other optimization methods, the PSO method provides remarkable results.…”

Section: Elmmentioning

confidence: 99%

Insights into the Prediction of Condensate Viscosity Near the Wellbore by ELM and ANFIS-PSO Strategies

Naseri¹

2020

Preprint

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By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate prediction of production and optimization in these reservoirs requires specific properties such as liquid viscosity. Empirical models have already developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered in a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a big data bank including reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.755 and 0.15, respectively, while these values are 0.889 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software.

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Section: Elmmentioning

confidence: 99%

Insights into the Prediction of Condensate Viscosity Near the Wellbore by ELM and ANFIS-PSO Strategies

Naseri¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, optimization of the model parameters can dramatically improve the quality and accuracy of modeling [78]. For that matter, a huge number of optimization methodologies, such as PSO, are available to reinforce the parameters and outputs of the ANFIS system [109]. Particle swarm optimization is extraordinary compared to other approaches with the end goal of optimization.…”

Section: Model Developmentmentioning

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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“…However, optimization of the model parameters can dramatically improve the quality and accuracy of modeling [78]. For that matter, a huge number of optimization methodologies, such as PSO, are available to reinforce the parameters and answers of the ANFIS system [109]. PSO is extraordinary compared to other approaches with the end goal of optimization.…”

Section: Model Developmentmentioning

confidence: 99%

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

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

Self Cite

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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

Estimation of natural gases water content using adaptive neuro-fuzzy inference system

Cited by 39 publications

References 25 publications

Insights into the Prediction of Condensate Viscosity Near the Wellbore by ELM and ANFIS-PSO Strategies

Insights into the Prediction of Condensate Viscosity Near the Wellbore by ELM and ANFIS-PSO Strategies

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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