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相关概念视频

Methods of Medium Optimization01:28

Methods of Medium Optimization

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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...
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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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Production of Biopesticides01:18

Production of Biopesticides

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Biopesticides offer a sustainable alternative to chemical pesticides, utilizing microbial agents to control agricultural pests. Bacillus thuringiensis (Bt) is a widely employed bacterium known for its potent insecticidal activity. Bt biopesticides are favored for their specificity to insect pests, minimal environmental impact, and natural degradability.Mechanism of Bt Toxin Action Bt produces insecticidal crystal (Cry) proteins during its sporulation phase. These proteins form parasporal...
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相关实验视频

Updated: May 4, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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运输和生产协作调度优化与多层编码基因算法为非管道井.

Qiushi Li1, Yuze Li2, Haitong Sun3

  • 1Digital and Intelligent Business Unit, PetroChina Changqing Oil Field Branch, Xian, 710018, China.

Heliyon
|January 16, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种基因算法,以优化边际油井的油轮调度,大大降低运输成本,提高低透性油田的生产效率. 新方法很快就能找到可行的解决方案,特别是在大规模运营时.

关键词:
遗传算法 遗传算法 遗传算法MILP MILP 这是一个非常好的方法.尽量减少驾驶距离的距离快速解决方案 快速解决方案

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科学领域:

  • 石油工程是石油工程中的一个.
  • 运营研究 运营研究
  • 人工智能的人工智能

背景情况:

  • 低透性油田的边缘油井面临着独特的挑战,包括小规模的储存,分散的分配和间歇性生产.
  • 目前用于油井生产和原油运输的手动调度是低效的,阻碍了产能释放和增加成本.
  • 现有的混合整数线性编程 (MILP) 模型是计算密集的,由于大变量尺度的原因,解决时间很长.

研究的目的:

  • 开发一种高效的算法,以优化边际油井原油运输计划.
  • 解决手动调度和计算上昂贵的MILP模型的局限性.
  • 通过改进调度,最大限度地降低运输成本,最大限度地提高油井生产能力.

主要方法:

  • 一个新的多层编码基因算法 (GA) 设计用于调度油轮航线和装卸操作.
  • GA的第一层编码油轮的行驶路径,而第二层处理原油数量和时间.
  • 该算法采用代码操纵技术,如翻转,交换和转移,以扩大搜索域.

主要成果:

  • 提出的遗传算法与各种规模 (5,10,30,200个井) 的精确算法进行了验证.
  • 该GA显示了显著更快的计算时间,特别是在大规模的场景 (如200个井).
  • 对于200个井的情况,GA找到了一个可行的方案,总行驶距离为11,280公里,1062.3秒.

结论:

  • 开发的遗传算法为原油油轮在非管道油田的调度提供了快速有效的解决方案.
  • 这种方法通过快速制定调度计划和尽量减少旅行距离,提高了运营效率.
  • 该研究为油田提供了实际指导,以优化物流并降低边际油井开采中的运营成本.