IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v266y2023ics0360544222033357.html
   My bibliography  Save this article

Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production from palm oil mill effluent (POME)

Author

Listed:
  • Chong, Daniel Jia Sheng
  • Chan, Yi Jing
  • Arumugasamy, Senthil Kumar
  • Yazdi, Sara Kazemi
  • Lim, Jun Wei

Abstract

In recent years, machine learning (ML) techniques have been developed to predict the performance of anaerobic digestion (AD) processes including methane potential and reactor stability. However, their practical applications to industrial-scale palm oil mill effluent (POME) treatment plant are limited. In this study, ML algorithms such as response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are employed to model the biogas production and methane yield from the AD of POME in a local industry-scale anaerobic covered lagoon. Results demonstrated that these models were well aligned with two years of operational data with high coefficient of determination (R2) of up to 0.98. ANFIS yields the highest prediction accuracy, with R2 of 0.9791 along with the lowest mean absolute error (MAE) of 0.0730 and root mean squared error (RMSE) of 0.1438. Subsequently, ANFIS is used in the multi-objective optimisation to maximise the biogas production and methane yield. Optimal conditions for the temperature of the anaerobic digester, pH and recirculation ratio are 38.9 °C, 7.03 and 1.89 respectively which could enhance the biogas production and methane yield by 19.4% and 12.2% respectively. Confirmatory experiments were carried out in the biogas plant under this set of optimised variables for a period of two months. The predicted biogas production and methane yield are highly correlated to the actual data with small percentage difference of 1.25% and 5.09% respectively, indicating that ANFIS model was accurate and reliable. Sensitivity analysis shows that pH has the most dominant effect on the methane yield.

Suggested Citation

  • Chong, Daniel Jia Sheng & Chan, Yi Jing & Arumugasamy, Senthil Kumar & Yazdi, Sara Kazemi & Lim, Jun Wei, 2023. "Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production ," Energy, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033357
    DOI: 10.1016/j.energy.2022.126449
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222033357
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126449?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. Olatunji, Kehinde O. & Ahmed, Noor A. & Madyira, Daniel M. & Adebayo, Ademola O. & Ogunkunle, Oyetola & Adeleke, Oluwatobi, 2022. "Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction," Renewable Energy, Elsevier, vol. 189(C), pages 288-303.
    2. Monowar Hossain & Saad Mekhilef & Firdaus Afifi & Laith M Halabi & Lanre Olatomiwa & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2018. "Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-31, April.
    3. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    4. Muhammad Arif Fikri Hamzah & Jamaliah Md Jahim & Peer Mohamed Abdul & Ahmad Jaril Asis, 2019. "Investigation of Temperature Effect on Start-Up Operation from Anaerobic Digestion of Acidified Palm Oil Mill Effluent," Energies, MDPI, vol. 12(13), pages 1-16, June.
    5. Ohimain, Elijah Ige & Izah, Sylvester Chibueze, 2017. "A review of biogas production from palm oil mill effluents using different configurations of bioreactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 242-253.
    6. Loh, S.K. & Nasrin, A.B. & Mohamad Azri, S. & Nurul Adela, B. & Muzzammil, N. & Daryl Jay, T. & Stasha Eleanor, R.A. & Lim, W.S. & Choo, Y.M. & Kaltschmitt, M., 2017. "First Report on Malaysia’s experiences and development in biogas capture and utilization from palm oil mill effluent under the Economic Transformation Programme: Current and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1257-1274.
    7. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    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. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    10. Ludovica Gregorio & Mattia Callegari & Paolo Mazzoli & Stefano Bagli & Davide Broccoli & Alberto Pistocchi & Claudia Notarnicola, 2018. "Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results, Advantages, Limits and Lesson Learned," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 229-242, January.
    11. Solmaz Aslanzadeh & Karthik Rajendran & Azam Jeihanipour & Mohammad J. Taherzadeh, 2013. "The Effect of Effluent Recirculation in a Semi-Continuous Two-Stage Anaerobic Digestion System," Energies, MDPI, vol. 6(6), pages 1-16, June.
    12. Ahmed, Yunus & Yaakob, Zahira & Akhtar, Parul & Sopian, Kamaruzzaman, 2015. "Production of biogas and performance evaluation of existing treatment processes in palm oil mill effluent (POME)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1260-1278.
    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. Siti Rokhiyah Ahmad Usuldin & Zul Ilham & Adi Ainurzaman Jamaludin & Rahayu Ahmad & Wan Abd Al Qadr Imad Wan-Mohtar, 2023. "Enhancing Biomass-Exopolysaccharides Production of Lignosus rhinocerus in a High-Scale Stirred-Tank Bioreactor and Its Potential Lipid as Bioenergy," Energies, MDPI, vol. 16(5), pages 1-18, February.

    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. Choong, Yee Yaw & Chou, Kian Weng & Norli, Ismail, 2018. "Strategies for improving biogas production of palm oil mill effluent (POME) anaerobic digestion: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2993-3006.
    2. Khatun, Rahima & Reza, Mohammad Imam Hasan & Moniruzzaman, M. & Yaakob, Zahira, 2017. "Sustainable oil palm industry: The possibilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 608-619.
    3. Zain, Munirah Md & Mohamed, Abdul Rahman, 2018. "An overview on conversion technologies to produce value added products from CH4 and CO2 as major biogas constituents," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 56-63.
    4. A Aziz, Md Maniruzzaman & Kassim, Khairul Anuar & ElSergany, Moetaz & Anuar, Syed & Jorat, M. Ehsan & Yaacob, H. & Ahsan, Amimul & Imteaz, Monzur A. & Arifuzzaman,, 2020. "Recent advances on palm oil mill effluent (POME) pretreatment and anaerobic reactor for sustainable biogas production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    5. Sharvini, S.R. & Noor, Z.Z. & Stringer, L.C. & Afionis, S. & Chong, C.S., 2022. "Energy generation from palm oil mill effluent: A life cycle cost-benefit analysis and policy insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    6. Tan, Yue Dian & Lim, Jeng Shiun, 2019. "Feasibility of palm oil mill effluent elimination towards sustainable Malaysian palm oil industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 507-522.
    7. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    8. Tong Su & Xinjun Wang & Songrui Ning & Jiandong Sheng & Pingan Jiang & Shenghan Gao & Qiulan Yang & Zhixin Zhou & Hanyu Cui & Zhilin Li, 2024. "Enhancing Soil Salinity Evaluation Accuracy in Arid Regions: An Integrated Spatiotemporal Data Fusion and AI Model Approach for Arable Lands," Land, MDPI, vol. 13(11), pages 1-20, November.
    9. Abdeshahian, Peyman & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2016. "Potential of biogas production from farm animal waste in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 714-723.
    10. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    11. Sharvini, Siva Raman & Noor, Zainura Zainon & Chong, Chun Shiong & Stringer, Lindsay C & Glew, David, 2020. "Energy generation from palm oil mill effluent: A life cycle assessment of two biogas technologies," Energy, Elsevier, vol. 191(C).
    12. Muhammad, Gul & Potchamyou Ngatcha, Ange Douglas & Lv, Yongkun & Xiong, Wenlong & El-Badry, Yaser A. & Asmatulu, Eylem & Xu, Jingliang & Alam, Md Asraful, 2022. "Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 184(C), pages 753-764.
    13. Chao Deng & Liang Ma & Taishan Zeng, 2021. "Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    14. Notodarmojo, Peni Astrini & Fujiwara, Takeshi & Habuer, & Pham Van, Dinh, 2022. "Effectiveness of oyster shell as alkali additive for two-stage anaerobic co-digestion: Carbon flow analysis," Energy, Elsevier, vol. 239(PC).
    15. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    16. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    17. Binhweel, Fozy & Pyar, Hassan & Senusi, Wardah & Shaah, Marwan Abdulhakim & Hossain, Md Sohrab & Ahmad, Mardiana Idayu, 2023. "Utilization of marine ulva lactuca seaweed and freshwater azolla filiculoides macroalgae feedstocks toward biodiesel production: Kinetics, thermodynamics, and optimization studies," Renewable Energy, Elsevier, vol. 205(C), pages 717-730.
    18. Taghizadeh-Alisaraei, Ahmad & Assar, Hossein Alizadeh & Ghobadian, Barat & Motevali, Ali, 2017. "Potential of biofuel production from pistachio waste in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 510-522.
    19. Arnita Rishanty & Maxensius Tri Sambodo & Mesnan Silalahi & Erliza Hambali, 2021. "Zero-Waste Bioenergy To Lower Energy Transition Risks In Indonesia," Working Papers WP/17/2021, Bank Indonesia.
    20. Joel A. Martínez-Regalado & Cinthia Leonora Murillo-Avalos & Purificación Vicente-Galindo & Mónica Jiménez-Hernández & José Luis Vicente-Villardón, 2021. "Using HJ-Biplot and External Logistic Biplot as Machine Learning Methods for Corporate Social Responsibility Practices for Sustainable Development," Mathematics, MDPI, vol. 9(20), pages 1-16, October.

    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:266:y:2023:i:c:s0360544222033357. 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.