Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF)

Rahmanian, Bashir; Pakizeh, Majid; Esfandyari, Morteza; Heshmatnezhad, Fazlollah; Maskooki, Abdolmajid

doi:10.1016/j.jhazmat.2011.05.051

Cited by 58 publications

(17 citation statements)

References 45 publications

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“…Rahmanian et al [30] conducted a fuzzy modeling to predict lead removal from aqueous solution using micellar-enhanced ultrafiltration (MEUF). Therefore, fuzzy model can be used for environmental fields specifically for the prediction of adsorption process.…”

Section: Several Methods Have Been Used For Separation Of Heavy Metalmentioning

confidence: 99%

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

Javadian

Ghasemi

Ruiz

et al. 2018

Ultrasonics Sonochemistry

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In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200W) as an efficient method and characterized by FE-SEM, FTIR and N adsorption-desorption isotherm. The effect of different parameters such as contact time (0-120min), initial metal ion concentration (25-200mg/L), temperature (298, 318 and 333K), amount of adsorbent (0.002-0.007g) and the solution's initial pH (1-7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R>0.99). The maximum monolayer adsorption capacity (q) of NiO/ACNC was 1428.57mg/L. Thermodynamic parameters (ΔG°, ΔH° and ΔS°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R>0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.

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Section: Several Methods Have Been Used For Separation Of Heavy Metalmentioning

confidence: 99%

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

Javadian

Ghasemi

Ruiz

et al. 2018

Ultrasonics Sonochemistry

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“…A fuzzy control model for improving nitrogen removal, in order to optimize the anoxic/oxic step feeding activated sludge process, has been proposed [32]. Fuzzy modeling and simulation to lead removal using micellar-enhanced ultrafiltration have been performed [33]. The pressure swing adsorption process of propylene/propane using neuro-fuzzy modeling has been optimized [34].…”

Section: Desalination and Water Treatmentmentioning

confidence: 99%

Neuro-fuzzy modeling of Cu(II) and Cr(VI) adsorption from aqueous solution by wheat straw

Rebouh

Bouhedda

Hanini

2016

Desalination and Water Treatment

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In this study, an intelligent architecture-based neuro-fuzzy technique is used for the prediction of removal capacity of Cu(II) and Cr(VI) from aqueous solution using wheat straw as a biosorbent. The effect of operating parameters such as initial pH, temperature, contact time, straw particle size, and biosorbent's chemical treatment are studied to optimize the conditions for maximum removal capacity. A large database is collected from the different carried out experiments to allow the modelization of the adsorption process. This process could be described using classical mathematical models such as Langmuir, Freundlich, or a hybrid model. However, the learnt neuro-fuzzy architecture with the obtained experimental data presents good correlation (R 2 ¼ 0:9999) and shows that the intelligent model is able to predict accurately the removal amount of Cu(II) and Cr(VI) from wheat straw under different conditions.

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“…MEUF has been developed which combines high flux of ultrafiltration and high rejection percentage of nanofiltration and RO, simultaneously. This method has already been used to remove heavy metals [8][9][10][11][12][13][14][15][16][17], organic materials [14,[18][19][20][21], and treat industrial wastewaters like soft drink [22], raisin [23], olive oil [24] and edible oil [25].…”

Section: Desalination and Water Treatmentmentioning

confidence: 99%

Micellar-enhanced ultrafiltration of dairy wastewater with anionic and nonionic surfactants

Salmani

2016

Desalination and Water Treatment

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To cite this article: Hossein Jalaei salmani (2015): Micellar-enhanced ultrafiltration of dairy wastewater with anionic and nonionic surfactants, Desalination and Water Treatment, In this study, the potential of MEUF process has been analyzed to treat a real dairy wastewater. For this purpose, linear alkylbenzene sulfonate (LAS) and Triton X-100 (TX-100) have been used as anionic and nonionic surfactants, respectively. The ranges of 23.5 bars and 0-4 mM are selected for transmembrane pressure (TMP) and surfactants concentration, respectively, to investigate their effects on the permeate flux as a function of operating time. Moreover, it is revealed experimentally that rejection of pollution indices including chemical oxygen demand (COD) and total dissolved solid (TDS) is affected by TMP and feed surfactant concentration, while turbidity is remained relatively unchanged. According to the experimental results analysis, the increase of feed surfactant concentration has negative and positive effects on the permeate flux and rejection efficiency, respectively. TMP enhancement also leads to the higher flux values but it does not necessarily improve the rejection. Furthermore, the effectiveness of anionic surfactant in pollution indices elimination is more than the nonionic one. In the treatment combination assessed for the factors, the best rejection values of COD, TDS, and turbidity using LAS surfactant with polyacrylonitrile (PAN-350) 20-kDa poly(ethylene glycol) membrane are almost 93, 52, and 99.9%, respectively.

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Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF)

Cited by 58 publications

References 45 publications

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

Neuro-fuzzy modeling of Cu(II) and Cr(VI) adsorption from aqueous solution by wheat straw

Micellar-enhanced ultrafiltration of dairy wastewater with anionic and nonionic surfactants

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