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.

中文翻译：

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通过基于壳聚糖的预处理和机器学习优化，提高酸性乳清的资源回收率


酸性乳清是一种低 pH 值和高有机物含量的乳制品副产品，不仅存在处置挑战，但也具有资源回收的潜力。在本研究中，合成了壳聚糖凝胶并评价了酸性乳清的浊度降低效果。采用机器学习 （ML） 模型来预测和优化预处理过程，随机森林算法的预测精度达到 0.78。使用模拟退火算法，确定了最佳条件，将 2.2% 壳聚糖溶液凝胶以 24 g/L 的剂量应用于 pH 值为 4.6 的酸性乳清中 12 小时，实现了 91% 的浊度降低，比优化前获得的 71% 有了显着改善。验证实验证实了它在预测和优化预处理过程方面的有效性。这些发现强调了 ML 在优化壳聚糖预处理方面的可行性，并证明了壳聚糖凝胶是一种经济高效、高效的酸性乳清选择，有可能提高乳制品行业的资源回收率。