Biodiesel Production with Supercritical Ethanol Estimation using RBF-ANN Approach

Rajabi-Kuyakhi, Hossein

doi:10.23880/ppej-16000211

Cited by 1 publication

(5 citation statements)

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“…To this aim, the relative deviation for both train and test data has been computed by Equation ) 10 :

RD = \frac{e_{i} - a_{i}}{a_{i}}

where RD is relative deviations, e i is estimated values, and a i is the actual value. The accumulation of relative values near to Y = 0 line represents the accuracy of the models 10 . Figure 3 indicates the relative deviation plot for test and train data.…”

Section: Resultsmentioning

confidence: 99%

“…Also, a relative deviation plot has been used to investigate the capability of created structure. To this aim, the relative deviation for both train and test data has been computed by Equation ) 10 :

RD = \frac{e_{i} - a_{i}}{a_{i}}

where RD is relative deviations, e i is estimated values, and a i is the actual value. The accumulation of relative values near to Y = 0 line represents the accuracy of the models 10 .…”

Section: Resultsmentioning

confidence: 99%

“…The accumulation of relative values near to Y = 0 line represents the accuracy of the models. 10 Figure 3 indicates the relative deviation plot for test and train data. As can be seen, the ANFIS-PSO model has high accuracy in predicting the adsorption percent of Cr(VI) values.…”

Section: Evaluation Of Anfis-psomentioning

confidence: 99%

“…To this aim, the relative deviation for both train and test data has been computed by Equation ( 7) 10 :…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…These substances are considered as removal pollutant sorbent because of the large specific area and lack of internal diffusion resistance. 7,8 In recent years, machine-learning approaches such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine have researchers' attention for the prediction of different processes in industries [9][10][11][12][13][14][15] that can be applied to an extra range of systems to predict the behavior of experimental systems. 9,[11][12][13][14] Tahani et al proposed an ANN model to estimate the thermal conductivity of nanofluid.…”

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confidence: 99%

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Developing an adaptive neuro‐fuzzy inference system based on particle swarm optimization model for forecasting Cr(VI) removal by NiO nanoparticles

Kuyakhi

Boldaji

2021

Env Prog and Sustain Energy

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The treatment of wastewater from heavy metal ions such as hexavalent chromium Cr(VI) is considered as an important issue in recent years, which is harmful to human health and environment. Since, in engineering, performing the experiments to solve problems is time-consuming and costly. In this study, adaptive neuro-fuzzy inference system (ANFIS) was coupled with particle swarm optimization (PSO) algorithm to develop a predictive model for modeling of Cr(VI) removal percent on NiO nanoparticle. To this end, the trace of four initial parameters containing contact time, Cr(VI) initial concentration, NiO adsorbent dosage, and pH on removing Cr(VI) was investigated. The performance of the developed algorithm was evaluated by statistical parameters such as mean absolute relative deviation mean squared error (MSE)

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“…To this aim, the relative deviation for both train and test data has been computed by Equation ) 10 :

RD = \frac{e_{i} - a_{i}}{a_{i}}

Section: Resultsmentioning

confidence: 99%

RD = \frac{e_{i} - a_{i}}{a_{i}}

where RD is relative deviations, e i is estimated values, and a i is the actual value. The accumulation of relative values near to Y = 0 line represents the accuracy of the models 10 .…”

Section: Resultsmentioning

confidence: 99%

Section: Evaluation Of Anfis-psomentioning

confidence: 99%

“…To this aim, the relative deviation for both train and test data has been computed by Equation ( 7) 10 :…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%