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相关概念视频

Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
Bioplastics01:27

Bioplastics

Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...
Biofuels01:25

Biofuels

The microbial conversion of organic matter into biofuels holds potential as a renewable energy source. Among biofuel sources, microalgae are recognized as a highly efficient and adaptable feedstock for biodiesel production, owing to their rapid biomass accumulation, elevated lipid productivity, and capacity to proliferate in diverse aquatic systems, including freshwater, marine, and wastewater habitats. Unlike terrestrial crops, microalgae do not compete for land and can achieve significantly...

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相关实验视频

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Label-free in situ Imaging of Lignification in Plant Cell Walls
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通过机器学习促进纤维素生物炼油:进展,前景和挑战

Xiao-Yan Huang1, Xue Zhang1, Lei Xing2

  • 1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Bioresource technology
|March 26, 2025
PubMed
概括

机器学习 (ML) 可以优化纤维蛋白生物炼油厂,提高效率和可持续性. 本综述探讨了整个管道中的ML应用,包括用于更好的性能的先进建模技术.

关键词:
无氧消化消化无氧消化酶性水解是一种酶性水解.发酵 发酵 是一个过程.基纤维素生物精炼厂 基纤维素生物精炼厂机器学习 机器学习预处理 预处理

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相关实验视频

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科学领域:

  • 生物技术和生物化学工程 生物技术和生物化学工程
  • 可持续的化学加工 可持续的化学加工
  • 计算生物学和机器学习应用程序

背景情况:

  • 纤维素生物炼油厂通过多个阶段将生物质转化为有价值的产品:预处理,酶化水解,发酵和消化.
  • 传统的流程优化依赖于经验方法,这些方法往往耗时且不理想.
  • 现有的研究经常集中在单个生物炼油模块上,缺乏整体优化方法.

研究的目的:

  • 提供机器学习 (ML) 应用在整个纤维素生物炼油管道的全面审查.
  • 突出ML在优化过程参数和菌株开发方面的潜力.
  • 讨论高级的ML策略,如转移学习和混合模型,以提高性能和可解释性.

主要方法:

  • 整体审查的ML集成在林氏纤维素生物炼油工艺.
  • 探索ML模型构建,评估和验证策略.
  • 讨论新兴的ML技术,包括转移学习和混合模型.

主要成果:

  • 机器学习为优化复杂的生物炼油操作提供了传统方法的强大替代方案.
  • ML可以在预处理,水解,发酵和消化阶段提高效率和产量.
  • 先进的ML模型在克服数据限制和改善模型理解方面表现有前途.

结论:

  • 将ML整合到基纤维素生物炼油厂对于实现可持续和经济可行的生物基生产至关重要.
  • 一个整体的ML引导的方法可以显著提高整体系统性能,而不是模块特定的优化.
  • 对机器学习的进一步研究,特别是转移学习和混合模型,将加速竞争力的生物炼油系统的发展.