Modeling of wax deposition produced in the pipelines using PSO-ANFIS approach

The surface tension (ST) of ionic liquids (ILs) and their accompanying mixtures allows engineers to accurately arrange new processes on the industrial scale. Without any doubt, experimental methods for the specification of the ST of every supposable IL and its mixtures with other compounds would be an arduous job. Also, experimental measurements are effortful and prohibitive; thus, a precise estimation of the property via a dependable method would be greatly desirable. For doing this task, a new modeling method according to artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA), has been suggested to estimate ST of the binary ILs mixtures. For training and testing the applied network, a set of 748 data points of binary ST of IL systems within the temperature range of 283.1-348.15 K was utilized. Furthermore, an outlier analysis was used to discover doubtful data points. Gained values of MSE & R 2 were 0.0000007 and 0.993, 0.0000002 and 0.998, 0.0000004 and 0.996 and 0.0000006 and 0.994 for the ICA-ANN, TLBO-ANN, PSO-ANN and GA-ANN, respectively. Results demonstrated that the experimental data and predicted values of the TLBO-ANN model for such target are wholly matched.

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“…(2) Normalization of the data such that ANN would be able to process the data, which is done using Eq. ( 9) (Chu, 2017):…”

Section: Prediction Of St Values By the Tlbo-ann Modelmentioning

confidence: 99%

Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence

Setiawan

Daneshfar

Rezvanjou

et al. 2021

Environ Dev Sustain

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“…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

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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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Modeling of wax deposition produced in the pipelines using PSO-ANFIS approach

Cited by 21 publications

References 23 publications

Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence

Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence

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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