Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modelling for nickel adsorption onto agro-wastes and commercial activated carbon

Souza, P.R.; Dotto, Guilherme L.; Salau, Nina Paula Gonçalves

doi:10.1016/j.jece.2018.11.013

Cited by 75 publications

(22 citation statements)

References 41 publications

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“…Kaan Yetilmezsoy and Sevgi Demirel established an ANN approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio; they found that a simulation based on the ANN model can provide further contribution to developing a better understanding of the dynamic behavior of the process 19 . Souza et al studied ANN and ANFIS modeling for nickel adsorption on activated carbon 20 …”

Section: Introductionmentioning

confidence: 99%

“…19 Souza et al studied ANN and ANFIS modeling for nickel adsorption on activated carbon. 20 The purpose of this article is to provide a high-accuracy model in predicting the amount of removal % of Cr(VI) by NiO nanoparticle due to the importance of the estimation of contaminants in wastewater using artificial intelligence methods. For this aim, an ANFIS is coupled with particle swarm optimization (PSO) algorithm.…”

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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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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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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“…Thus, the recovery of waste such as waste fiber is very important to mitigate its effects on the environment, and guarantee a sustainable process. Among the various potential applications that could be given to residual palm fiber, its use as an adsorbent in the treatment of water contaminated with heavy metals, among a wide range of applications, seems attractive taking into account the reported removal yields for similar residues [15]. In addition, Colombia was ranked among the 12 countries with the highest yam production worldwide and ranked first in terms of product yield with 28.3 tons per planted hectare, generating after harvesting around 5000 tons of waste per year [16].…”

Section: Introductionmentioning

confidence: 99%

Equilibrium, Kinetics and Thermodynamics of Chromium (VI) Adsorption on Inert Biomasses of Dioscorea rotundata and Elaeis guineensis

Villabona-Ortíz

González-Delgado

2022

Water

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Adsorption equilibrium and kinetics on lignocellulosic base adsorbents from oil palm bagasse (OPB) and yam peels (YP) were studied for the removal of hexavalent chromium present in aqueous solution, in a batch system, evaluating the effect of temperature, adsorbent dose and particle size on the process. Isotherms were fitted to Langmuir, Freundlich and Dubinin–Radushkevich isothermal models. Kinetic data were adjusted to the pseudo-first-order, pseudo-second-order and Elovich models. Thermodynamic parameters were estimated by the van’t Hoff method. From characterization of adsorbents, the presence of a porous surface typical of lignocellulosic materials was found, with hydroxyl, amine and carboxyl functional groups. It was also found that the highest adsorption capacity was obtained at 0.03 g of adsorbent, 55 °C and 0.5 mm, reporting an adsorption capacity of 325.88 and 159 mg/g using OPB and YP, respectively. The equilibrium of adsorption on OPB is described by Langmuir and Freundlich isotherms, while that of YP is described by Dubinin–Radushkevich’s model, indicating that the adsorption is given by the ion exchange between the active centers and the metallic ions. A maximum adsorption capacity was obtained of 63.83 mg/g with OPB and 59.16 mg/g using YP, according to the Langmuir model. A kinetic study demonstrated that equilibrium time was 200 min for both materials; kinetic data were described by pseudo-second-order and Elovich models, thus the mechanism of Cr (VI) adsorption onto the evaluated materials is dominated by a chemical reaction. The thermodynamic study determined that the elimination of YP is endothermic, irreversible and not spontaneous, while for OPB it is exothermic, spontaneous at low temperatures and irreversible.

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“…ANN has been used in several studies concerning the prediction of different properties of textile materials such as physical properties (Rahbar and Vadood (2015)). Moreover, many studies have been published on the prediction of dye and ion removal from wastewaters using ANN and ANFIS (Ghaedi and Vafaei, 2017; Souza et al , 2018). However, no studies have been reported on the prediction of the effect of plasma treatment parameters on the dyeability of textile fibers using these systems.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN and ANFIS in prediction of color strength of plasma-treated wool yarns dyed with a natural colorant

Haji

Payvandy

2020

PRT

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Purpose Despite the increasing popularity of natural dyeing of textiles, the low substantivity between the fibers and the natural dyes is a problem. Several methods have been used to overcome this problem. In this study, wool fibers were pretreated with oxygen plasma under different conditions and dyed with the extract of grape leaves. The purpose of this study is to investigate the effects of plasma treatment parameters on the color strength of the dyed samples using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) and evaluate the ability of these methods for predicting the color strength. Design/methodology/approach Woolen yarns were modified under different conditions of oxygen plasma treatment. Oxygen flow rate, power and time were considered as the treatment variable factors. Plasma-treated samples were dyed under constant conditions with the extract of grape leaves as a natural dye. ANN and ANFIS were applied to model and analyze the effect of plasma treatment parameters on the color strength of the dyed samples. Findings The results showed that increasing all the plasma treatment process variables, including oxygen flow rate, power and time increased the color strength of the dyed samples. The results showed that the developed ANN and ANFIS could accurately predict the experimental data with correlation coefficients of 0.986 and 0.997, respectively. According to the obtained correlation coefficients, ANFIS had a higher accuracy in prediction of the results of this study compared with the ANN and RSM models (correlation coefficient = 0.902, from our previous study). Originality/value This study uses ANN and ANFIS for predicting color strength of naturally dyed textiles for the first time. The use of computational intelligence for the optimization and prediction of the effects plasma treatment for the improvement of natural dyeing of wool is another novelty of this study.

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Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modelling for nickel adsorption onto agro-wastes and commercial activated carbon

Cited by 75 publications

References 41 publications

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

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

Equilibrium, Kinetics and Thermodynamics of Chromium (VI) Adsorption on Inert Biomasses of Dioscorea rotundata and Elaeis guineensis

Application of ANN and ANFIS in prediction of color strength of plasma-treated wool yarns dyed with a natural colorant

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