ANFIS pattern for molecular membranes separation optimization

Rezakazemi, Mashallah; Mosavi, Amir; Shirazian, Saeed

doi:10.1016/j.molliq.2018.11.017

Cited by 120 publications

(52 citation statements)

References 61 publications

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“…The ANFIS has shown promising results in a wide range of applications for developing prediction models [106][107][108]. However, optimization of the model parameters can dramatically improve the quality and accuracy of modeling [78].…”

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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Section: Model Developmentmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…These parameters will be optimized with respect to the output errors which must be minimized using the back propagation method [105]. ANFIS has shown promising results in a wide range of applications for developing prediction models [106][107][108]. However, optimization of the model parameters can dramatically improve the quality and accuracy of modeling [78].…”

Section: Model Developmentmentioning

confidence: 99%

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

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

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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.

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“…Where ̅̅̅ denotes the normalized firing strengths. In the fourth level integration algorithm, the calculation of a consequence if-then rule is applied that was suggested by previous study [102][103][104][105][106][107][108][109][110][111][112][113].…”

Section: ̅̅̅ = ∑( )mentioning

confidence: 99%

Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

Mosavi¹,

Shamshirband²,

Chau³

2019

Preprint

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Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the ANFIS method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to  almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs.

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ANFIS pattern for molecular membranes separation optimization

Abstract: Research Highlights • Molecular separation using microporous membranes • Developing hybrid model based on ANFIS-CFD for the separation • process Optimization of ANFIS structure for prediction of separation process

Cited by 120 publications

References 61 publications

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

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

Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

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