Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume

Rahimzadeh, Amir; Ashtiani, Farzin Zokaee; Okhovat, Ahmad

doi:10.1016/j.jece.2015.12.011

Cited by 31 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, polymer membranes are being used widely and commonly due to their uses in many industries such as desalination, 1 oily wastewater treatment, 2,3 industrial waste water, 4 dairy industries, 5‐7 transportation industries, 8 dye removal 9 and biotechnology 10 . Moreover, their fabrication and their flexibility are easier than ceramic membranes 11 …”

Section: Introductionmentioning

confidence: 99%

Manipulating of polyacrylonitrile membrane porosity via SiO₂ and TiO₂ nanoparticles: Thermodynamic and experimental point of view

Hamta

Ashtiani

Karimi

et al. 2020

Polymers for Advanced Techs

View full text Add to dashboard Cite

In this work, the membrane formation of the polyacrylonitrile (PAN) was investigated in the presence of different solvents named N‐methyl‐2‐pyrrolidone (NMP) and dimethylformamide (DMF). In this way, two ternary systems water/NMP/PAN and water/DMF/PAN were studied and compared both experimentally and thermodynamically. According to the binodal data, changing the solvent from NMP to DMF could expand the two‐phase region, which was proved by Hansen solubility parameters. The binodal data were predicted and the results showed that the Flory‐Huggins results are in good agreement with experimental data with less than 2% deviation, which is important to have a rational design of porous membrane and appropriate prediction of its morphology. Furthermore, the effects of TiO2 and SiO2 nanoparticles were investigated on dope solution viscosity, thickness, porosity and contact angle of the membranes in both solvents. The results showed that the pattern of changes in characteristics was similar using each nanoparticle. Lower concentrations of nanoparticles enhance the porosity of the membranes and above the optimum amount, adding the NPs leads to decrement the porosity of the nanocomposite membranes. Finally, the fluxes of the manufactured membranes in both cases were examined using distilled water or oil‐in‐water emulsion as feed. The initial permeate flux of oil‐in‐water emulsion was increased from 62 to 73 L m−2 hr−1 using the membrane fabricated by DMF instead of NMP and this fact showed that the PAN membranes potentially can be used for oil/water separation due to the low fouling and hence, the low flux drop during the time.

show abstract

Section: Introductionmentioning

confidence: 99%

Manipulating of polyacrylonitrile membrane porosity via SiO₂ and TiO₂ nanoparticles: Thermodynamic and experimental point of view

Hamta

Ashtiani

Karimi

et al. 2020

Polymers for Advanced Techs

View full text Add to dashboard Cite

show abstract

“…ANFIS, as an adaptive multilayer feed-forward network, is a fuzzy interference system combined with the computational power of artificial neural network (ANN) [25]. ANFIS is a powerful approach to modeling/mapping the input and output relationship in complex and nonlinear systems [26,27].…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

2016

View full text Add to dashboard Cite

In the present paper, the adaptive neural fuzzy inference system (ANFIS) was used for modeling of magnetic chitosan adsorption performance for the methyl orange removal. The ANFIS network, which is the best in data predicting, was trained with back propagation optimum method. Our results revealed that the developed ANFIS models can effectively model the non-linearity behavior of the adsorptive performance and the predicted values were in good agreement with the experimental data.

show abstract

“…(Pourkiaei et al, 2016), neuro-fuzzy,e.g. (Rahimzadeh et al, 2016) or SVM (Kheirandish et al, 2016). Use of AI-based methods have now exploded with use of methods like artificial neural networks both feed-forward and recurrent (Jeppesen et al, 2017;Zhao et al, 2019;Ma et al, 2018), extreme and deep learning methods (Yang et al, 2019b;Liu et al, 2019), clustering methods including support vector and k-means (Liu et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Tsalapati

Johnson

Jackson

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven't reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform efficiently on-line at the same time. Furthermore, there is only limited research identifying both PEMFC stack faults and ancillary system faults simultaneously. While none of the existing tools can be interrogated by the end-user. In this research, we develop novel artificial intelligence-based technologies to overcome these existing barriers, i.e., i) a semantically enriched integrating schema (ontology) of the overall operation and structure of the PEMFC that allows automatic inference engines to automatically deduce fault detection; ii) a knowledgebased, light-weight, on-line fuel cell system diagnosis (FuCSyDi) platform. FuCSyDi detects and provides the location of failures by considering only the data from the reliable sensors. Additionally, it provides the reasons underpinning any forthcoming failures and enables the end-user to interrogate the platform for further information regarding its operation and structure. Our platform is validated by performing tests against common automotive stress conditions. This innovative approach enhances the reliability of the fuel cell system diagnosis and, hence, its lifetime performance.

show abstract

Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume

Cited by 31 publications

References 30 publications

Manipulating of polyacrylonitrile membrane porosity via SiO₂ and TiO₂ nanoparticles: Thermodynamic and experimental point of view

Manipulating of polyacrylonitrile membrane porosity via SiO₂ and TiO₂ nanoparticles: Thermodynamic and experimental point of view

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Contact Info

Product

Resources

About

Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume

Cited by 31 publications

References 30 publications

Manipulating of polyacrylonitrile membrane porosity via SiO2 and TiO2 nanoparticles: Thermodynamic and experimental point of view

Manipulating of polyacrylonitrile membrane porosity via SiO2 and TiO2 nanoparticles: Thermodynamic and experimental point of view

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Contact Info

Product

Resources

About

Manipulating of polyacrylonitrile membrane porosity via SiO₂ and TiO₂ nanoparticles: Thermodynamic and experimental point of view

Manipulating of polyacrylonitrile membrane porosity via SiO₂ and TiO₂ nanoparticles: Thermodynamic and experimental point of view