IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7959-d851857.html
   My bibliography  Save this article

An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process

Author

Listed:
  • Mark McCormick

    (Neuroheuristic Research Group, Department of Information Systems, Faculty of Business and Economics, Quartier UNIL-Chamberonne, University of Lausanne, 1015 Lausanne, Switzerland)

Abstract

The purpose of this work was to develop a problem-solving approach and a simulation tool that is useful for the specification of wastewater treatment process equipment design parameters. The proposition of using an artificial neural network (ANN) numerical model for supervised learning of a dataset and then for process simulation on a new dataset was investigated. The effectiveness of the approach was assessed by evaluating the capacity of the model to distinguish differences in the equipment design parameters. To demonstrate the approach, a mock dataset was derived from experimentally acquired data and physical effects reported in the literature. The mock dataset comprised the influent flow rate, the bed packing material dimension, the type of packing material and the packed bed height-to-diameter ratio as predictors of the calorific value reduction. The multilayer perceptron (MLP) ANN was compared to a polynomial model. The validation test results show that the MLP model has four hidden layers, each having 256 units (nodes), accurately predicts calorific value reduction. When the model was fed previously unseen test data, the root-mean-square error (RMSE) of the predicted responses was 0.101 and the coefficient of determination (R 2 ) was 0.66. The results of simulation of all 125 possible combinations of the 3 mechanical parameters and identical influent wastewater flow profiles were ranked according to total calorific value reduction. A t -test of the difference between the mean calorific value reduction of the two highest ranked experiments showed that the means are significantly different ( p -value = 0.011). Thus, the model has the capacity to distinguish differences in the equipment design parameters. Consequently, the values of the three mechanical feature parameters from the highest ranked simulated experiment are recommended for use in the design of the industrial scale upflow anaerobic filter (UAF) for wastewater treatment.

Suggested Citation

  • Mark McCormick, 2022. "An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process," Sustainability, MDPI, vol. 14(13), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7959-:d:851857
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7959/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/7959/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luis Arismendy & Carlos Cárdenas & Diego Gómez & Aymer Maturana & Ricardo Mejía & Christian G. Quintero M., 2020. "Intelligent System for the Predictive Analysis of an Industrial Wastewater Treatment Process," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    2. 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).
    Full references (including those not matched with items on IDEAS)

    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. Sakiewicz, Piotr & Piotrowski, Krzysztof & Kalisz, Sylwester, 2020. "Neural network prediction of parameters of biomass ashes, reused within the circular economy frame," Renewable Energy, Elsevier, vol. 162(C), pages 743-753.
    2. Jolanta Telenga-Kopyczyńska & Izabela Jonek-Kowalska, 2021. "Algorithm for Selecting Best Available Techniques in Polish Coking Plants Supporting Multi-Criteria Investment Decisions in European Environmental Conditions," Energies, MDPI, vol. 14(9), pages 1-24, May.
    3. Kumar, Pankaj & Kumar, Vinod & Singh, Jogendra & Kumar, Piyush, 2021. "Electrokinetic assisted anaerobic digestion of spent mushroom substrate supplemented with sugar mill wastewater for enhanced biogas production," Renewable Energy, Elsevier, vol. 179(C), pages 418-426.
    4. 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).
    5. Piotr Sakiewicz & Krzysztof Piotrowski & Mariola Rajca & Izabella Maj & Sylwester Kalisz & Józef Ober & Janusz Karwot & Krishna R. Pagilla, 2022. "Innovative Technological Approach for the Cyclic Nutrients Adsorption by Post-Digestion Sewage Sludge-Based Ash Co-Formed with Some Nanostructural Additives under a Circular Economy Framework," IJERPH, MDPI, vol. 19(17), pages 1-28, September.
    6. Mahmoodi-Eshkaftaki, Mahmood & Ebrahimi, Rahim, 2021. "Integrated deep learning neural network and desirability analysis in biogas plants: A powerful tool to optimize biogas purification," Energy, Elsevier, vol. 231(C).
    7. Yasmine Ryma Ouahabi & Kenza Bensadok & Abdeldjalil Ouahabi, 2021. "Optimization of the Biomethane Production Process by Anaerobic Digestion of Wheat Straw Using Chemical Pretreatments Coupled with Ultrasonic Disintegration," Sustainability, MDPI, vol. 13(13), pages 1-18, June.
    8. Aleksandra Kuzior & Józef Ober & Janusz Karwot, 2021. "Stakeholder Expectation of Corporate Social Responsibility Practices: A Case Study of PWiK Rybnik, Poland," Energies, MDPI, vol. 14(11), pages 1-24, June.
    9. Luis Arismendy & Carlos Cárdenas & Diego Gómez & Aymer Maturana & Ricardo Mejía & Christian G. Quintero M., 2021. "A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    10. Chun-Ming Xu & Jia-Shuai Zhang & Ling-Qiang Kong & Xue-Bo Jin & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2022. "Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec," Mathematics, MDPI, vol. 10(22), pages 1-15, November.

    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:gam:jsusta:v:14:y:2022:i:13:p:7959-:d:851857. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.