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The conversion of poultry slaughterhouse wastewater sludge into biodiesel: Process modeling and optimization

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  • Pirmoradi, Neda
  • Ghaneian, Mohammad Taghi
  • Ehrampoush, Mohammad Hassan
  • Salmani, Mohammad Hossein
  • Hatami, Behnam

Abstract

Wastewater sludge from a poultry slaughterhouse treatment plant was recovered through conversion into biodiesel by ultrasound-assisted in situ transesterification. The main effects of the process parameters were investigated at three levels, and their empirical relationship was modeled using artificial neural network (ANN) and response surface methodology (RSM). The developed models predicted the process behavior with excellent accuracy. Although both models had similar prediction performances, ANN marginally outperformed RSM. The capability of the genetic algorithm (GA) combined with the RSM (RSM-GA) and ANN (ANN-GA) models was evaluated for optimizing the process variables. The maximum biodiesel yield (21.45% w/w) was obtained using the ANN-GA model under optimized conditions, i.e., at the reaction time of 39.69 min, H2SO4 concentration of 3.34% (v/v), methanol-to-sludge relative content of 14.91:1 (mL/g), and ultrasound power of 104.87 W. Consequently, a combination of ANN and GA was proposed to model and optimize the transesterification process. The biodiesel yield obtained in this study was higher than the previously reported values from tannery (10.98%), dairy (13.46%), and municipal (18.58%) sewage sludge. This study specified biodiesel with a fatty acid methyl esters content of 96.86% using gas chromatography-mass spectrometry, Fourier transform infrared, and nuclear magnetic resonance spectroscopy.

Suggested Citation

  • Pirmoradi, Neda & Ghaneian, Mohammad Taghi & Ehrampoush, Mohammad Hassan & Salmani, Mohammad Hossein & Hatami, Behnam, 2021. "The conversion of poultry slaughterhouse wastewater sludge into biodiesel: Process modeling and optimization," Renewable Energy, Elsevier, vol. 178(C), pages 1236-1249.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1236-1249
    DOI: 10.1016/j.renene.2021.07.016
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    1. Salehiyoun, Ahmad Reza & Di Maria, Francesco & Sharifi, Mohammad & Norouzi, Omid & Zilouei, Hamid & Aghbashlo, Mortaza, 2020. "Anaerobic co-digestion of sewage sludge and slaughterhouse waste in existing wastewater digesters," Renewable Energy, Elsevier, vol. 145(C), pages 2503-2509.
    2. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    3. Seffati, Kambiz & Esmaeili, Hossein & Honarvar, Bizhan & Esfandiari, Nadia, 2020. "AC/CuFe2O4@CaO as a novel nanocatalyst to produce biodiesel from chicken fat," Renewable Energy, Elsevier, vol. 147(P1), pages 25-34.
    4. Hatami, Behnam & Ebrahimi, Aliasghar & Ehrampoush, Mohammad Hassan & Salmani, Mohammad Hossein & Dalvand, Arash & Pirmoradi, Neda & Angelidaki, Irini & Fotidis, Ioannis A. & Mokhtari, Mehdi, 2021. "Recovery of intermittent cycle extended aeration system sludge through conversion into biodiesel by in-situ transesterification," Renewable Energy, Elsevier, vol. 163(C), pages 56-65.
    5. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    6. Meher, L.C. & Vidya Sagar, D. & Naik, S.N., 2006. "Technical aspects of biodiesel production by transesterification--a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(3), pages 248-268, June.
    7. Zhang, Xiaolei & Yan, Song & Tyagi, Rajeshwar Dayal & Drogui, Patrick & Surampalli, Rao Y., 2016. "Ultrasonication aided biodiesel production from one-step and two-step transesterification of sludge derived lipid," Energy, Elsevier, vol. 94(C), pages 401-408.
    8. Betiku, Eriola & Okunsolawo, Samuel S. & Ajala, Sheriff O. & Odedele, Olatunde S., 2015. "Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tr," Renewable Energy, Elsevier, vol. 76(C), pages 408-417.
    9. Mostafaei, Mostafa & Javadikia, Hossein & Naderloo, Leila, 2016. "Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy," Energy, Elsevier, vol. 115(P1), pages 626-636.
    10. Borah, Manash Jyoti & Devi, Anuchaya & Saikia, Raktim Abha & Deka, Dhanapati, 2018. "Biodiesel production from waste cooking oil catalyzed by in-situ decorated TiO2 on reduced graphene oxide nanocomposite," Energy, Elsevier, vol. 158(C), pages 881-889.
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