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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.
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通过基于机器学习的可解释组合模型优化碳源添加,以控制过剩的污泥产量.

Bowen Li1, Li Liu2, Zikang Xu1

  • 1College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; MOE Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, Nankai University, Tianjin, 300350, China.

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概括

使用机器学习组合模型优化废水处理厂 (WWTP) 的碳来源添加,大大降低了运营成本和过剩的污泥产量. 这种方法提高了废水管理的效率和可持续性.

关键词:
碳的来源 碳的来源机器学习 机器学习模型解释模型解释过剩的污泥产量可以产生.废水处理厂的废水处理厂.权重平均整体组合.

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科学领域:

  • 环境工程 环境工程
  • 机器学习应用 机器学习应用
  • 废水处理技术 废水处理技术

背景情况:

  • 污水处理厂 (WWTP) 在优化运营成本和管理多余污泥方面面临着挑战.
  • 碳源添加是影响成本和污泥产量的关键因素.
  • 机器学习 (ML) 提供了WWTP中复杂模式识别的潜力,但其在优化碳源和污泥产量的应用尚未得到充分发展.

研究的目的:

  • 开发和评估一个整体机器学习模型,以优化WWTP中的碳源添加.
  • 进一步利用开发的模型来控制和减少过剩的污泥产量.
  • 为现实世界WWTP应用创建一个实用,可访问的工具.

主要方法:

  • 采用加权平均组合策略,将多种不同的基本机器学习模型结合起来.
  • 开发了两个整体模型:模型-1用于碳源添加优化和模型-2用于过剩污泥产量控制.
  • 进行了特征选择,以确定最佳输入子集,以实现模型简化和效率.

主要成果:

  • 整体模型的表现明显优于单个模型,实现了高精度 (模型-1的R2为0.98,模型-2的R2为0.93).
  • 优化的模型具有减少的功能保持高精度 (R2的0.97模型-1,0.95模型-2).
  • 在Web应用程序中部署显示了实际好处,包括47.25%的碳源节约和15.89%的过剩污泥产量减少.

结论:

  • 集成机器学习提供了一种强大而准确的方法,用于优化WWTP中碳源添加.
  • 这种方法为减少过剩的污泥产量和提高运营效率提供了巨大的潜力.
  • 开发的可部署模型为现实世界废水处理管理提供了实际的解决方案.