通过基托桑基预处理和机器学习优化,增强酸乳糖的资源回收
1Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97331, USA.
Bioresource technology
|December 5, 2024
概括
奇托凝有效降低乳制品副产品酸乳清中的度. 机器学习优化了这一过程,实现了91%的度降低,为乳制品废物管理提供了可持续的解决方案.
科学领域:
- 乳制品科学 乳制品科学
- 环境工程 环境工程
- 材料科学 材料科学 材料科学
背景情况:
- 酸性乳清是一种低pH的乳制品副产品,由于有机含量高,因此存在处置挑战.
- 从酸乳清中回收资源对于可持续的乳制品行业实践至关重要.
研究的目的:
- 为了合成基托凝,以减少酸乳清度.
- 使用机器学习来优化预处理过程.
- 评估基多凝用于酸乳清处理的成本效益和效率.
主要方法:
- 基托桑凝的合成和表征.
- 在处理前和处理后测量酸乳清的度.
- 开发和应用机器学习模型 (随机森林) 用于预测.
- 使用模拟回火算法优化预处理条件.
主要成果:
- 随机森林模型实现了0.78.8的预测准确度.
- 优化条件 (2.2%奇多凝,24 g/L剂量,pH值4.6,12 h) 导致度降低了91%.
- 从优化前的71%的度降低显著改善.
- 验证实验证实了该模型的预测和优化能力.
结论:
- 机器学习有效地优化了酸乳糖的奇托桑预处理.
- 素凝是一种具有成本效益和高效的酸乳糖预处理剂.
- 这种方法提高了乳制品行业的资源回收潜力.
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