Ferreira, C.M.; Akisue, R.A.; Júnior, R.S. Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes. Processes2023, 11, 2121.
Ferreira, C.M.; Akisue, R.A.; Júnior, R.S. Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes. Processes 2023, 11, 2121.
Ferreira, C.M.; Akisue, R.A.; Júnior, R.S. Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes. Processes2023, 11, 2121.
Ferreira, C.M.; Akisue, R.A.; Júnior, R.S. Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes. Processes 2023, 11, 2121.
Abstract
Abstract: An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion, involving the decomposition of the organic matter in two or more substrates by bacteria and archaea, in the absence of oxygen. 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, with Monod kinetics involving two substrates and an intermediate (M2SI model), 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 results simulated by M2SI were used in neural network modeling, firstly employing a “generic” network applicable to all 8 scenarios. A very good fit was obtained (R²>0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition). The parameters of the M2SI model for the 8 different conditions were also mapped using a neural network, as a function of the organic material composition. A fit with R²>0.99 was obtained using 25 neurons. In the case of the fuzzy logic, RMSE 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).
Copyright:
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