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<p>The examination and application of biological treatment methods for solid and semi-solid waste have become significant due to the intricacies associated with employing living microorganisms. While sigmoidal kinetic models facilitate the establishment of correlations between process development outcomes and critical factors, as well as kinetic reaction parameters, artificial intelligence (AI) methodologies can be employed to enhance the prediction of biotransformation process dynamics. This study anticipated the production of lactic acid (LA) and biomass from the anaerobic biotransformation of pig manure using <i>Lactobacillus fermentum</i>. Triangular and trapezoidal membership functions with several tiers were employed within a Mamdani-type fuzzy inference system consisting of 69 rules. Experimental data collected at a pilot scale (20&#xa0;L) during semicontinuous operation over a duration of 5 days were utilized to forecast LA and biomass yields via fuzzy logic approach, considering three replicates. Furthermore, two sigmoidal kinetic models-Gompertz and logistic were utilized to align both actual and anticipated data. The outcomes confirmed the models with correlation coefficients R² &gt;0.90. The Gompertz model demonstrated superior fit, with R² exceeding 0.93. At modest agitation rates (100&#xa0;rpm), the maximum concentrations of LA and biomass achieved were 35.44&#xa0;g/L and 87.28&#xa0;g/L experimentally, and 34.6&#xa0;g LA/L and 86.5&#xa0;g biomass/L using fuzzy logic prediction. This verifies that AI methodologies and/or traditional kinetic models may accurately elucidate and forecast the behavior of biotransformation processes of agro-industrial organic waste utilizing probiotic bacteria, or under analogous conditions to those in our study.</p>

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Modeling and mathematical validation of the anaerobic biotransformation process of pig manure with L. fermentum using AI techniques and sigmoidal kinetic models

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Diana Ibeth Romero-Mota,
  • Joaquín Estrada-García,
  • Quetzalli Amaya-Sánchez,
  • Alberto Alfonso Aguilar-Lasserre,
  • Juan Manuel Méndez-Contreras

摘要

The examination and application of biological treatment methods for solid and semi-solid waste have become significant due to the intricacies associated with employing living microorganisms. While sigmoidal kinetic models facilitate the establishment of correlations between process development outcomes and critical factors, as well as kinetic reaction parameters, artificial intelligence (AI) methodologies can be employed to enhance the prediction of biotransformation process dynamics. This study anticipated the production of lactic acid (LA) and biomass from the anaerobic biotransformation of pig manure using Lactobacillus fermentum. Triangular and trapezoidal membership functions with several tiers were employed within a Mamdani-type fuzzy inference system consisting of 69 rules. Experimental data collected at a pilot scale (20 L) during semicontinuous operation over a duration of 5 days were utilized to forecast LA and biomass yields via fuzzy logic approach, considering three replicates. Furthermore, two sigmoidal kinetic models-Gompertz and logistic were utilized to align both actual and anticipated data. The outcomes confirmed the models with correlation coefficients R² >0.90. The Gompertz model demonstrated superior fit, with R² exceeding 0.93. At modest agitation rates (100 rpm), the maximum concentrations of LA and biomass achieved were 35.44 g/L and 87.28 g/L experimentally, and 34.6 g LA/L and 86.5 g biomass/L using fuzzy logic prediction. This verifies that AI methodologies and/or traditional kinetic models may accurately elucidate and forecast the behavior of biotransformation processes of agro-industrial organic waste utilizing probiotic bacteria, or under analogous conditions to those in our study.