IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v241y2022ics0360544221031881.html
   My bibliography  Save this article

Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using Nonlinear Autoregressive Exogenous neural network and seagull algorithm

Author

Listed:
  • Abdel daiem, Mahmoud M.
  • Hatata, Ahmed
  • Said, Noha

Abstract

Anaerobic digestion of bio-wastes has a great potential to substitute fossil fuel consumption and reduce air pollution. In this study, semi-continuous anaerobic co-digestion of waste activated sludge and wheat straw using different mixing ratios under mesophilic conditions has been performed. Furthermore, modeling and optimization of anaerobic co-digestion process have been carried out using the Nonlinear Autoregressive Exogenous (NARX) neural network and Seagull optimization algorithm (SOA). The anaerobic co-digestion improved C/N ratio from 6.64 to 17.85 and enhanced the biogas production by 350% at 2% mixing ratio compared with the mono sludge digestion. Moreover, methane content of biogas ranged from 55% to 65%. The modeling results showed that the simulations of the proposed NARX neural network presented accurate results in predicting the digested sample characteristics and the biogas production. The correlation coefficient R for the data is close to 1 and the results show the stability of the optimum NARX neural network outputs for all predicted values. Moreover, it has high accuracy and effectiveness with minimum average root mean square error (RMSE) of 0.1518564. These findings can support the decision maker and stakeholders in renewable energy sector and sustainable biomass waste management.

Suggested Citation

  • Abdel daiem, Mahmoud M. & Hatata, Ahmed & Said, Noha, 2022. "Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using Nonlinear Autoregressive Exogenous neural network and seagull algorithm," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031881
    DOI: 10.1016/j.energy.2021.122939
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221031881
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122939?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gueguim Kana, E.B. & Oloke, J.K. & Lateef, A. & Adesiyan, M.O., 2012. "Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm," Renewable Energy, Elsevier, vol. 46(C), pages 276-281.
    2. Abdel daiem, Mahmoud M. & Hatata, Ahmed & Galal, Osama H. & Said, Noha & Ahmed, Dalia, 2021. "Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network," Renewable Energy, Elsevier, vol. 178(C), pages 226-240.
    3. Abu Qdais, H. & Bani Hani, K. & Shatnawi, N., 2010. "Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm," Resources, Conservation & Recycling, Elsevier, vol. 54(6), pages 359-363.
    4. Said, N. & El-Shatoury, S.A. & Díaz, L.F. & Zamorano, M., 2013. "Quantitative appraisal of biomass resources and their energy potential in Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 84-91.
    5. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Akhilesh Kumar Singh & Priti Pal & Saurabh Singh Rathore & Uttam Kumar Sahoo & Prakash Kumar Sarangi & Piotr Prus & Paweł Dziekański, 2023. "Sustainable Utilization of Biowaste Resources for Biogas Production to Meet Rural Bioenergy Requirements," Energies, MDPI, vol. 16(14), pages 1-22, July.
    2. Li, Pengfei & Cheng, Chongbo & Guo, Rui & Yu, Ran & Jiao, Youzhou & Shen, Dekui & He, Chao, 2022. "Interactions among the components of artificial biomass during their anaerobic digestion with and without sewage sludge," Energy, Elsevier, vol. 261(PB).
    3. Nahid Sultana & S. M. Zakir Hossain & Salma Hamad Almuhaini & Dilek Düştegör, 2022. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand," Energies, MDPI, vol. 15(9), pages 1-26, May.
    4. Campo, Giuseppe & Cerutti, Alberto & Zanetti, Mariachiara & De Ceglia, Margherita & Scibilia, Gerardo & Ruffino, Barbara, 2023. "A modelling approach for the assessment of energy recovery and impact on the water line of sludge pre-treatments," Energy, Elsevier, vol. 274(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abdel daiem, Mahmoud M. & Hatata, Ahmed & Galal, Osama H. & Said, Noha & Ahmed, Dalia, 2021. "Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network," Renewable Energy, Elsevier, vol. 178(C), pages 226-240.
    2. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    3. Peng Song & Zhisheng Zhang, 2023. "Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM," Energies, MDPI, vol. 16(9), pages 1-13, April.
    4. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    5. Kamel, Salah & El-Sattar, Hoda Abd & Vera, David & Jurado, Francisco, 2018. "Bioenergy potential from agriculture residues for energy generation in Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 28-37.
    6. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    7. Rambabu Muppidi & Ramakrishna S. S. Nuvvula & S. M. Muyeen & SK. A. Shezan & Md. Fatin Ishraque, 2022. "Optimization of a Fuel Cost and Enrichment of Line Loadability for a Transmission System by Using Rapid Voltage Stability Index and Grey Wolf Algorithm Technique," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    8. Ru Fang, Yan & Zhang, Silu & Zhou, Ziqiao & Shi, Wenjun & Hui Xie, Guang, 2022. "Sustainable development in China: Valuation of bioenergy potential and CO2 reduction from crop straw," Applied Energy, Elsevier, vol. 322(C).
    9. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    10. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    11. Berhanu, Mesfin & Jabasingh, S. Anuradha & Kifile, Zebene, 2017. "Expanding sustenance in Ethiopia based on renewable energy resources – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1035-1045.
    12. Hend Dakhel Alhassany & Safaa Malik Abbas & Marcos Tostado-Véliz & David Vera & Salah Kamel & Francisco Jurado, 2022. "Review of Bioenergy Potential from the Agriculture Sector in Iraq," Energies, MDPI, vol. 15(7), pages 1-17, April.
    13. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    14. Chinese, D. & Patrizio, P. & Nardin, G., 2014. "Effects of changes in Italian bioenergy promotion schemes for agricultural biogas projects: Insights from a regional optimization model," Energy Policy, Elsevier, vol. 75(C), pages 189-205.
    15. Sakiewicz, P. & Piotrowski, K. & Ober, J. & Karwot, J., 2020. "Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    16. Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
    17. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    18. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    19. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    20. Djatkov, Djordje & Effenberger, Mathias & Martinov, Milan, 2014. "Method for assessing and improving the efficiency of agricultural biogas plants based on fuzzy logic and expert systems," Applied Energy, Elsevier, vol. 134(C), pages 163-175.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031881. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.