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相关概念视频

Bioreactor Design and Operational System01:29

Bioreactor Design and Operational System

140
Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
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Batch vs Continuous Culture01:14

Batch vs Continuous Culture

169
Fermentation is a foundational biotechnological process used to produce pharmaceuticals, biofuels, enzymes, and food additives. Among industrial strategies, batch and continuous fermentation are the two most widely applied. Although both rely on microbial conversion of substrates into desired products, they differ markedly in operation, productivity, and suitability for specific applications.Batch fermentation occurs in a closed system in which nutrient media and inoculum are added at the...
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Upstream Processing01:27

Upstream Processing

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Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
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Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
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从食品加工废水中预测单细胞蛋白质的产量,在批量反应堆中使用集体学习对批量反应堆进行测序.

Rong Huang1, Hui Xu2, Ezequiel Santillan3

  • 1School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798 Singapore.

Bioresource technology
|April 21, 2025
PubMed
概括

这项研究使用集体学习来预测废水中的单细胞蛋白 (SCP) 生产. 机器学习模型,特别是高斯过程回归,准确地预测系统性能,以获得更好的资源回收和动物料生产.

关键词:
生物质是生物质的.废水质量监测 废水质量监测功能重要性 功能重要性高斯过程回归的高斯过程回归.可以解释的分析分析.

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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
  • 环境科学与工程环境科学与工程
  • 在环境系统中的机器学习应用.

背景情况:

  • 从食品加工废水中生产单细胞蛋白质 (SCP) 提供可持续的资源回收和动物料.
  • 在可变条件下准确预测SCP系统性能对于运营效率至关重要,但由于复杂的生物反应而具有挑战性.

研究的目的:

  • 开发和评估集体学习算法,用于预测基于连续流量,测序批量反应器的SCP系统的性能.
  • 识别影响系统性能的关键特征,用于模型优化和运营决策.

主要方法:

  • 经过训练和测试的集体学习算法:集体支持向量回归,集体高斯过程回归 (GPR),随机森林和极端梯度提升.
  • 使用的工业大豆加工废水作为测序批量反应堆 (SBR) 系统中的影响.
  • 进行可解释的分析,以验证模型优化的特征意义.

主要成果:

  • 集体学习模型,特别是基于GPR的集体,在预测废水和生物质变量方面表现明显优于线性回归.
  • 具有影响力的特征的基于GPR的合集实现了生物质生产预测的确定系数 (R2) = 0.72,与线性回归 (R2) = 0.4.4.2相比,显示出优异的性能和减少过度拟合.

结论:

  • 集体学习,特别是GPR,提供了一个强大而准确的方法来预测SCP系统在动态条件下的性能.
  • 开发的模型为运营决策提供了宝贵的见解,提高了资源回收和废水处理过程的效率.