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Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

机译：废水处理厂能耗的分析与预测

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摘要

Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.
机译：废水处理厂（WWTP）是能源密集型产业。在废水处理的每个阶段消耗能量。这是WWTP成本的主要贡献者。能耗的分析和预测对节能至关重要。许多因素会影响能源消耗。能源消耗和废水之间的关系复杂，挑战识别。本文采用了模糊聚类方法来对WWTP的样本数据进行分析，并分析了不同类别中能源消耗与影响因素之间的关系。该研究发现，各种类别的能效发生了改变，不同类型的影响因素具有不同的影响力。径向基函数（RBF）神经网络用于预测能量消耗。采用完整集和类别的数据来培训和测试模型。结果表明，RBF模型使用来自子集的日期具有比多变量线性回归（MLR）模型的性能更好。本研究的结果为WWTP中节能提供了基本的理论依据。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第16期|8690898.1-8690898.8|共8页
  • 作者

    Li ZhenHua; Zou ZhiHong; Wang LiPing;

  • 作者单位

    Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China;

    Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China|Beijing Key Lab Emergency Support Simulat Technol Beijing 100191 Peoples R China;

    Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China;

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