通过动力建模和机器学习揭示了从预处理食品废物中选择性挥发性脂肪酸生产的洞察力
Weidong Zhang1, Wenguang Zhang1, Huihui Qiu1
1Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Fujian Key Laboratory of Chemical Biology, Xiamen University, Xiamen 361005, China.
Water research
|February 22, 2026
概括
这项研究通过优化预处理条件来增强食品废物发酵中的挥发性脂肪酸 (VFA) 生产. 使用pH和热水解预处理 (THP) 的新框架使选择性VFA回收成为可能,为工业应用铺平了道路.
科学领域:
- 生物技术是生物技术.
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 食品废弃物 (FW) 酸性生成的工业应用受到混合挥发性脂肪酸 (VFA) 分离的挑战所限制.
- 目前对选择性VFA生产的机制理解不足以进行精确的工艺控制.
研究的目的:
- 系统地调查从FW选择性VFA生产的预处理条件.
- 通过动态建模和机器学习,确定控制VFA产品选择性的关键因素.
- 为控制的VFA生产建立一个监管框架.
主要方法:
- 系统地调查预处理条件,包括pH值和热水解预处理 (THP).
- 动力建模和机器学习的应用,用于分析选择性因素.
- 涉及pH值和基质组成的双重控制机制的量化.
主要成果:
- 达到最高的选择性为酸盐96.0%,酸盐52.1%,酸盐92.6%.
- 酸和丁酸盐的理论选择性分别达到56.9%和96.3%.
- 确定了一种双重控制机制,其中pH调节路径活性,基质成分调节代谢流量.
结论:
- 基于pH和THP的监管框架使FW的选择性VFA生产成为可能.
- 确定的双重控制机制提供了对选择性VFA生产的定量理解.
- 这项工作为提高VFA产量和选择性建立了基准,推动了工业部署.
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