Machine Learning Modeling of Anchovy Waste Treatment Using Solar Drying

Abstract

This study aims to valorize coproducts from the anchovy processing chain by obtaining compounds of interest through the implementation of environmentally friendly and energy-efficient techniques. These methods, which also apply to other fresh anchovy waste coproducts, seek to minimize the environmental pollution associated with conventional systems. The investigation focused on the application of solar drying as a treatment of anchovy waste. The resulting data were employed to model the drying behavior of anchovy waste using five machine learning algorithms. A thermokinetic study was conducted under both natural and forced convection solar drying to establish the optimal conditions for drying and storing anchovy heads, which are a significant source of high-quality proteins for human and animal nutrition. Drying kinetics were examined at three temperatures (60°C, 70°C, and 90°C) and two airflow rates (150 and 300 m³/h). The study identified air drying temperature as the most critical factor affecting the drying kinetics of anchovy wastes. Machine learning modeling of anchovy waste solar drying was conducted, and evaluated models were RNN, LSTM, GRU, LightGBM, and CatBoost. CatBoost demonstrated superior performance in predicting moisture content. It achieved the lowest Mean Squared Error of 1.1491e − 06, the lowest Mean Absolute Error of 0.0006265, and the highest coefficient of determination (R²) of 99.99%. The comparative analysis highlighted distinct differences in the predictive accuracy of the models, with CatBoost emerging as the most effective.