Recovery of Value-Added Products from Industrial Wastewaters: A Review to Potential Feedstocks

Lovato, Giovanna; Ferreira, Carolina Machado; Amui, Mariana Miziara; Devens, Kauanna Uyara; Augusto, Isabela Mehi Gaspari; Ribeiro, Alexandre Rodrigues; Rodrigues, José Alberto Domingues; Silva, Edson Luiz

doi:10.1007/978-3-031-06562-0_9

Cited by 4 publications

(1 citation statement)

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“…Anaerobic digestion is not only a technology to produce bioenergy but also to handle and convert waste and/or subproducts, which is the case regarding glycerol and molasses. If composition of the resulting digestate is monitored experimentally, aside from the formation of gas, it could also be included as an output for neural and fuzzy mapping (in particular, the presence of glycerol has shown the potential to generate propionic acid and 1,3 propanediol, among other compounds [36]).…”

Section: Discussionmentioning

confidence: 99%

Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes

2023

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An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab (v. 6.1.1), considering the Monod two-substrate with an intermediate (M2SI) kinetic model proposed by Rakmak et al. (Rakmak, N.; Noynoo, L.; Jijai, S.; Siripatana, C. Lecture Notes in Applied Mathematics and Applied Science in Engineering. Melaka, Malaysia, p. 11–20, 2019), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi–Sugeno approach available in the ANFIS package of Matlab. The biomethane production data simulated using Scilab were considered in neural network modeling and validation, firstly employing a “generic” network applicable to all eight scenarios, providing a very good fit (R2 > 0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition, again by using validation data generated by the M2SI model). The parameters of the M2SI model for the eight different conditions were also mapped using a neural network, as a function of the organic material composition, providing a fit with R2 > 0.99 when using 25 neurons. In the case of fuzzy logic, an RMSE (Root Mean Squared Error) of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks).

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Section: Discussionmentioning

confidence: 99%