Membrane and Water Treatment

  • 제9권2호
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  • Pages.115-121
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  • 2018
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  • 2005-8624(pISSN)
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  • 2092-7037(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Modelling of starch industry wastewater microfiltration parameters by neural network

  • Jokic, Aleksandar I. (University of Novi Sad, Faculty of Technology) ;
  • Seres, Laslo L. (University of Novi Sad, Faculty of Economics in Subotica) ;
  • Milovic, Nemanja R. (University of Novi Sad, Faculty of Technology) ;
  • Seres, Zita I. (University of Novi Sad, Faculty of Technology) ;
  • Maravic, Nikola R. (University of Novi Sad, Faculty of Technology) ;
  • Saranovic, Zana (Institute of Economics ad, Ulica Kralja Milana 16) ;
  • Dokic, Ljubica P. (University of Novi Sad, Faculty of Technology)
  • 투고 : 2017.03.10
  • 심사 : 2017.10.21
  • 발행 : 2018.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

키워드

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피인용 문헌

  1. Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter vol.10, pp.12, 2018, https://doi.org/10.3390/membranes10120372