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Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification

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  • Sakiewicz, P.
  • Piotrowski, K.
  • Ober, J.
  • Karwot, J.

Abstract

An anaerobic fermentation process for biogas production integrated with wastewater purification in a modern wastewater treatment plant (WWTP) of designed nominal capacity 27,000 m3/day was modelled using artificial neural networks (ANNs). Neural models were trained, validated, and tested based on real-scale industrial data (covering three years of continuous plant operation), considering both technological aspects of the process and treated wastewater quality. An innovative approach addressing the simultaneous effect of seven adjustable main plant operation parameters together with wastewater characteristics (five parameters) on biogas production is reported for the first time in the literature. A parameter sensitivity analysis indicated clearly the higher importance of the operation process parameters on the biogas yield compared to the wastewater quality (COD, BOD5, TSS, Pg, Ng). The operation process parameters were the subject of modelling and analysis in respect to new, innovative possibilities, and technological strategies for biogas yield enhancement. The ANN model presented can be used as a predictive tool, an important element in such complex processes as steering/control strategies or for their optimisation procedures, as well as in the testing of other promising process intensification and optimisation scenarios.

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  • 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).
    • Handle: RePEc:eee:rensus:v:124:y:2020:i:c:s1364032120300800
    DOI: 10.1016/j.rser.2020.109784
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    3. 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.
    4. 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.
    5. 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.
    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. 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).
    8. 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.
    9. 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.

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