Application of Time Series Models to Analyze and Forecast the Influent Components of Wastewater Treatment Plants (WWTPs)
Application of Time Series Models to Analyze and Forecast the Influent Components of Wastewater Treatment Plants (WWTPs)
复制标题
应用时间序列模型分析和预测污水处理厂（WWTP）进水成分
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
C. D. Cox
中科院分区：
文献类型：
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作者：
J. Huo;W. Seaver;R. B. Robinson;C. D. Cox
建立了描述美国田纳西州橡树岭某污水处理厂进水组分统计特性的时间序列模型。用于生成模型的数据包括近3年的流量，温度，BOD 5，悬浮固体和氨氮的测量。数据集的特点是在周末和节假日期间定期缺失值。在时间序列建模之前，开发了一种双向指数平滑方法来估计这些缺失数据点的值。几种常用的时间序列模型，包括指数平滑模型，ARIMA模型，和动态回归模型，被应用到五个工厂进水变量的时间序列。根据各种统计数据和模型预测时间序列中未来值的能力，选择每个影响变量的最佳模型。然后，时间序列模型被用来模拟具有与原始数据相同的统计特性的随机时间序列的影响变量。原始和随机生成的时间序列的特征在于相似的均值，标准差，互相关和自相关函数。这些随机生成的时间序列可以与动态过程模型结合使用，以评估给定设计在不同于历史数据中存在的可变性条件下有效处理流出物流量的能力。
Time series models were developed to describe the statistical characteristics of the influent components of a wastewater treatment plant (WWTP) in Oak Ridge, TN. The data used to generate the models consisted of measurements of flow, temperature, BOD5, suspended solids, and ammonia nitrogen over nearly a 3-year period. The data set was characterized by periodically missing values during weekends and holidays. A two-directional exponential smoothing method was developed to estimate the values of those missing data points, prior to time series modeling. Several commonly used time series models, including the exponential smoothing model, ARIMA model, and the dynamic regression model, were applied to the time series of the five plant influent variables. The best models for each influent variable were selected based on various statistics and the ability of the models to forecast future values in the time series. The time series models were then used to simulate random time series of the influent variables with the same statistical characteristics as the original data. The original and randomly generated time series were characterized by similar means, standard deviations, cross-correlations and autocorrelation functions. These randomly generated time series can be used in conjunction with dynamic process models to evaluate the ability of a given design to effectively treat effluent flows under conditions of variability different than those present in the historical data.