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相关概念视频

Bioremediation00:46

Bioremediation

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Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.
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Control Systems01:10

Control Systems

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Environmental Applications of Microorganisms01:30

Environmental Applications of Microorganisms

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Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
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Biocontained Carcass Composting for Control of Infectious Disease Outbreak in Livestock
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走向数据驱动的智能堆肥技术和控制系统

Baoju Liu1, Kui Zhang1, Yuquan Wei1

  • 1College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; Organic Recycling Research Institute (Suzhou) of China Agricultural University, Suzhou 215100, China.

Bioresource technology
|October 16, 2025
PubMed
概括

智能堆肥使用传感器和人工智能来优化有机废物回收,改善营养回收和减少排放. 这种以数据为导向的方法提高了堆肥质量,并支持可持续农业.

关键词:
堆肥化 堆肥化 堆肥化环境传感 环境传感智能控制 智能控制 智能控制物联网 (IoT) 的物联网 (IoT) 的物联网.机器学习是机器学习.多尺度部署场景多尺度部署场景

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科学领域:

  • 环境科学 环境科学
  • 农业科学 农业科学
  • 计算机科学 计算机科学

背景情况:

  • 传统的堆肥依赖于手动控制,导致效率低下和营养损失.
  • 智能堆肥集成了传感,物联网和机器学习,用于自适应控制.
  • 目前的方法在传感器耐用性和数据标准化方面扎.

研究的目的:

  • 审查智能堆肥技术和应用方面的进展.
  • 识别阻碍广泛采用的障碍.
  • 提出可扩展,低碳堆肥的未来研究方向.

主要方法:

  • 审查用于监测堆肥参数 (温度,湿度,氧气,气体) 的传感器技术.
  • 分析数据驱动的建模和机器学习,用于预测控制.
  • 检查各种规模 (从家庭到工业) 的智能堆肥部署.

主要成果:

  • 智能堆肥可以实时监控和动态调整条件.
  • 预测算法提高了堆肥稳定性,营养节约和减排.
  • 在传感器技术,数据建模,智能控制和部署方面取得了进展.

结论:

  • 强大的传感器和标准化的数据集对于可靠的智能堆肥至关重要.
  • 不同的运营规模需要人工智能驱动的决策支持工具.
  • 智能堆肥可以增强营养回收和减少损失,支持循环农业.