Prediction of Losses in an Agave Liquor Production and Packaging System Using a Neural Network and Fuzzy Logic

This study presents the development of a predictive system based on artificial neural networks (ANNs) and fuzzy logic to estimate losses in an agave liquor production and packaging plant. Currently, these losses are discharged into wastewater, generating not only finished product waste, but also greater environmental pollution and higher treatment costs. To address this, agave liquor waste is converted into methane biogas through anaerobic digestion and subsequently transformed into electrical energy. The system begins by collecting historical data from the production process, including production plans and shrinkage rates at each stage of the packaging line. These data are analyzed to identify behavioral patterns and correlations between process variables and losses, allowing a deeper understanding of the packaging process. Critical control points were identified throughout the production stages, and an ANN model was trained with historical data to predict losses. Outstanding results were achieved in the packaging and capping stage, where a significant impact on bottle loss was observed, with a 29% impact in the morning shift and a 35% impact in the afternoon shift. Fuzzy logic was used to manage the uncertainty and subjectivity associated with identifying the stages most susceptible to waste, translating qualitative assessments into quantitative metrics. Estimates allow for approximately 8% to 12% reductions by streamlining the process with this analysis obtained through the use of artificial intelligence tools. This integrated approach aims to optimize operational efficiency, reduce losses, minimize environmental impact, and promote sustainable practices within the agave liquor industry.