生成型深度学习模型辅助多目标优化废水转化为蛋白质的光合作用细菌
Pengfei Hou1, Duofei Hu1, Shiqi Liu1
1School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.
Bioresource technology
|May 21, 2025
概括
这项研究使用生成深度学习来优化光合作用细菌的废水处理和蛋白质回收. 近红外光显著提高了转化为蛋白质的速度,提高了资源回收.
科学领域:
- 环境微生物学 环境微生物学
- 生物技术是生物技术.
- 人工智能的人工智能
背景情况:
- 光合作用细菌 (PSB) 在废水处理和价值化方面受到探索.
- 在PSB中平衡除和资源回收仍然是一个挑战.
研究的目的:
- 在PSB中使用生成式深度学习优化去除,蛋白质度和转化为蛋白质.
- 确定影响这些流程的关键因素,实现多目标优化.
主要方法:
- 生成式深度学习 (变量自动编码器) 用于数据增强.
- 弹性神经网络 (ENN) 用于模型拟合.
- 谢普利添加式解释 (SHAP) 用于因子识别.
- 多目标优化以确定帕雷托前线解决方案.
- 使用近红外 (NIR) 光的验证实验.
主要成果:
- 生成模型增强了数据集,改善了ENN模型的合适性.
- 确定了关键因素:碳/来源,光型,NLR,COD和HRT.
- 多目标优化产生了八个帕雷托前端解决方案.
- 近红外 (NIR) 光显著改善了转化为蛋白质 (高达44%) 和相关的基因表达.
结论:
- 生成型人工智能框架为废水价值化中的多目标优化提供了创新解决方案,特别是在有限的数据的情况下.
- 红外线光是提高PSB系统中转化为蛋白质的效率的一个有希望的因素.
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