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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

62
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
62
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

66
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
66

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相关实验视频

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Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
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开发无氧消化过程的数据驱动模型的多层统计方法.

Moonil Kim1, Fenghao Cui2

  • 1Department of Civil and Environmental Engineering, Hanyang University, 55 Hanyangdaehak-ro, Ansan, Kyeonggido, 426-791, Republic of Korea.

Journal of environmental management
|October 7, 2023
PubMed
概括

本研究引入了一种机器学习方法,通过增强数据处理和参数指定来改进无氧消化模型. 开发的模型准确地预测了生物气生产和废水化学氧气需求,从而提高了模型的可靠性.

关键词:
无氧消化消化无氧消化生物气体是一种生物气体.数据驱动模型是基于数据的模型.多重线性回归的多重线性回归.统计 统计 统计 统计

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科学领域:

  • 环境工程 环境工程
  • 生物技术是生物技术.
  • 数据科学数据科学数据科学

背景情况:

  • 不有效的数据处理和参数指定损害了无氧消化模型的可靠性.
  • 开发强大的模型对于优化无氧消化过程至关重要.

研究的目的:

  • 引入使用机器学习的多层统计技术,以开发可靠的无氧消化模型.
  • 系统地支持数据驱动的无氧消化模型的开发和验证.

主要方法:

  • 采用多层统计技术,包括立方平滑线,主要成分分析,方差分析和线性回归.
  • 利用实验室规模,试点规模和全规模无氧消化反应堆的实验数据.
  • 开发了多变量,数据驱动的模型来预测生物气生产和废水化学氧气需求.

主要成果:

  • 开发的模型准确地预测了生物气生产和废水化学氧气需求.
  • 统计分析证实了模型完整性和参数有效性.
  • 实验室规模的模型验证显示了高准确度 (R2=0.86,SSE=34.45,RMSE=0.72).

结论:

  • 多层统计技术提高了无氧消化模型的可靠性.
  • 数据驱动的模型可以有效地预测无氧消化中的关键性能指标.
  • 该方法确保了数据完整性和参数有效性,以实现可靠的模型开发.