Prediction of Losses in an Agave Liquor Production and Packaging System Using 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 facility. Currently, these losses are discharged into the wastewater stream, resulting not only in the waste of finished product but also in increased environmental pollution and higher treatment costs. To address this, the agave liquor waste is converted into methane biogas via anaerobic digestion and subsequently transformed into electrical energy. The system begins with the collection of historical data from the production pro-cess, including production plans and shrinkage rates at each stage of the packaging line. These data are analyzed to identify behavioural patterns and correlations between process variables and losses, allowing a deeper understanding of the packaging process. Critical control points are identified across the production stages, and an ANN model is trained using historical data to predict losses. Fuzzy logic is employed to manage the uncertainty and subjectivity associated with identifying the stages most susceptible to waste, trans-lating qualitative assessments into quantitative metrics. This integrated approach aims to optimize operational efficiency, reduce losses, minimize environmental impact, and pro-mote sustainable practices within the agave liquor industry.