Enhancing resource recovery from acid whey through chitosan-based pretreatment and machine learning optimization

Abstract

Acid whey, a dairy byproduct with low pH and high organic content, presents disposal challenges but also potential for resource recovery. In this study, chitosan gel was synthesized and evaluated for turbidity reduction of acid whey. Machine learning (ML) models were employed to predict and optimize the pretreatment process, with the Random Forest algorithm achieving a prediction accuracy of 0.78. Using the Simulated Annealing algorithm, optimal conditions were identified, applying a 2.2 % chitosan solution gel at a dosage of 24 g/L to acid whey at pH 4.6 for 12 h, achieving a 91 % turbidity reduction, a significant improvement over the 71 % obtained prior to optimization. Validation experiments confirmed its effectiveness in predicting and optimizing the pretreatment process. These findings highlight the feasibility of ML in optimizing chitosan pretreatment and demonstrate chitosan gel as a cost-effective, efficient option for acid whey, with potential to enhance resource recovery in the dairy industry.