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相关概念视频

Microbial Fermentation01:23

Microbial Fermentation

Fermentation is a crucial anaerobic metabolic process that enables microbes to derive energy from sugar without relying on oxygen or an electron transport chain. This process is fundamental to various biological and industrial applications and is classified based on the metabolic products generated.Role of Pyruvate in FermentationPyruvate and its derivatives serve as key electron acceptors in fermentative pathways. The oxidation of NADH to regenerate NAD+ is essential for the continuation of...
Microbes in Food Production01:29

Microbes in Food Production

Microbial fermentation is central to food biotechnology, enhancing flavor, texture, preservation, and stability. Fermentative microorganisms metabolize carbohydrates into organic acids, alcohols, and other metabolites that inhibit spoilage organisms and improve digestibility while contributing distinctive sensory qualities.In baking, amylases naturally present in flour hydrolyze starch into monosaccharides such as glucose, which Saccharomyces cerevisiae ferments anaerobically. Through...
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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Production of Alcohol01:27

Production of Alcohol

Continuous fermentation is a key strategy in industrial ethanol production, particularly when efficiency, scalability, and high yields are essential. This approach allows for uninterrupted operation and optimized resource utilization. The primary feedstock, corn starch, undergoes enzymatic hydrolysis facilitated by α-amylase and glucoamylase. These enzymes break down the starch into fermentable sugars such as glucose, which are readily assimilated by fermentative microorganisms.Fermentation...
Production of Organic Acids01:25

Production of Organic Acids

Lactic acid, an important organic acid extensively applied in food, pharmaceutical, and biodegradable polymer industries, is primarily produced via microbial fermentation. This method is favored over chemical synthesis due to its environmental sustainability and capacity for enantiomerically pure product formation. Among various microbial processes, the fermentation of starch-based substrates stands out due to the abundance and renewability of raw materials like corn and potatoes.Hydrolysis of...

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相关实验视频

Updated: Jul 1, 2026

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
06:03

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

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人工神经网络和遗传算法结合发酵动力学来调节L-氨酸发酵.

Hui Li1, Jiajun Chen1, Xingyan Li1

  • 1College of Biotechnology and Pharmaceutical Engineering, State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University, Nanjing, 211816, China.

Bioresource technology
|December 4, 2023
PubMed
概括

这项研究介绍了一种人工神经网络 (ANN) 和遗传算法 (GA),用于优化氨酸发酵控制. 这种新的方法可以将氨酸的产量提高到213.0g·L-1,从而改善生物过程的调节.

关键词:
人工神经网络的人工神经网络进行发酵控制.发酵动力学的发酵动力学遗传算法 遗传算法 遗传算法对于L-lysine来说,这是一个非常重要的药物.

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科学领域:

  • 生物技术是生物技术.
  • 工业微生物学 工业微生物学
  • 工艺工程是过程工程.

背景情况:

  • 发酵对于生物制品至关重要,但缺乏有效的过程调节.
  • 优化发酵控制对于工业生物工艺至关重要.

研究的目的:

  • 利用人工神经网络 (ANN) 和遗传算法 (GA) 开发一种创新的素发酵控制策略.
  • 预测和优化关键的发酵动力学:特定的素形成率 (qp),特定的基质消耗率 (qs) 和特定的细胞生长率 (μ).

主要方法:

  • 将GA与ANN结合起来,用于开发综合控制战略.
  • 使用一个三层的前向向后传播ANN模型 (4:10:1).
  • 通过GA优化发酵参数.

主要成果:

  • 该ANN-GA模型成功预测并优化了发酵动力学.
  • 确定了关键因素的最佳控制参数,包括碳比,残糖,氨和溶解氧.
  • 在优化条件下,氨酸度达到213.0±5.10 g·L−1的峰值.

结论:

  • 开发的ANN-GA控制策略显著提高了氨酸发酵效率.
  • 这种新的方法表明了优化各种其他生物制品发酵的潜力.