A data-mining approach to predict influent quality
A data-mining approach to predict influent quality
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DOI：
10.1007/s10661-012-2701-2
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发表时间：
2013-03-01
影响因子：
3
通讯作者：
Wei, Xiupeng
中科院分区：
文献类型：
--
作者：
Kusiak, Andrew;Verma, Anoop;Wei, Xiupeng
在污水处理厂中，进水水质预测对能源管理具有重要意义。进水水质通过碳质生化需氧量(CBOD)、氢势和总悬浮固体等指标进行衡量。提出了一种基于数据驱动的CBOD提前预测方法。由于工业数据采集系统的局限性，CBOD不是按固定的时间间隔记录的，这就造成了时间序列数据的空白。已经进行了大量的实验来近似输入和输出参数之间的函数关系，从而填补缺失的CBOD数据。研究了考虑季节性影响的模型。采用多层感知器、分类回归树、多元自适应回归样条法和随机森林等4种数据挖掘算法构建预测模型，最大预测期为5天。
In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented. Due to limitations in the industrial data acquisition system, CBOD is not recorded at regular time intervals, which causes gaps in the time-series data. Numerous experiments have been performed to approximate the functional relationship between the input and output parameters and thereby fill in the missing CBOD data. Models incorporating seasonality effects are investigated. Four data-mining algorithms-multilayered perceptron, classification and regression tree, multivariate adaptive regression spline, and random forest-are employed to construct prediction models with the maximum prediction horizon of 5 days.