[1]杨浩,杨彪龙,刘海清,等.数据-模型驱动下污水处理厂进水数据预测与分析[J].中国给水排水,2026,42(13):55-62.
　YangHao,YangBiaolong,LiuHaiqing,et al.Data-Model Driven Forecasting and Analysis of Influent Data in Wastewater Treatment Plant[J].China Water & Wastewater,2026,42(13):55-62.
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数据-模型驱动下污水处理厂进水数据预测与分析

中国给水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第42卷 期数: 2026年第13期 页码: 55-62 栏目: 出版日期: 2026-07-01
Title:
Data-Model Driven Forecasting and Analysis of Influent Data in Wastewater Treatment Plant
作者:
杨浩1,2,3， 杨彪龙1， 刘海清4， 王宝山1,2,3， 周添红1,2,3， 吴楠1,2,3， 张婷婷1
（1.兰州交通大学 环境与市政工程学院，甘肃 兰州 730070；2.兰州交通大学 寒旱地区水资源综合利用教育部工程研究中心，甘肃 兰州 730070；3.甘肃省黄河水环境重点实验室，甘肃 兰州 730070；4.甘肃水投庄浪环保有限责任公司，甘肃 平凉 744000）
Author(s):
Yang　Hao1，2，3， Yang　Biaolong1， Liu　Haiqing4， Wang　Baoshan1，2，3， Zhou　Tianhong1，2，3， Wu　Nan1，2，3， Zhang　Tingting1
（1. School of Environmental and Municipal Engineering， Lanzhou Jiaotong University， Lanzhou 730070， China； 2. Ministry of Education Engineering Research Center of Water Resource Comprehensive Utilization in Cold and Arid Regions， Lanzhou Jiaotong University， Lanzhou 730070， China； 3. Key Laboratory of Yellow River Water Environment in Gansu Province， Lanzhou 730070， China； 4. Gansu Water Investment Zhuanglang Environmental Protection Limited Liability Company， Pingliang 744000， China）
关键词:
城镇污水处理厂;  进水数据预测;  Prophet模型;  节能减排
Keywords:
urban wastewater treatment plant;  influent data forecasting;  Prophet model;  energy saving and emission reduction
摘要:
对西北某县城污水处理厂2020年3月—2023年8月的原始进水数据进行预处理及探索性分析，并以此数据为训练集，基于R语言的开源Prophet模型对该污水厂的进水数据进行预测，再将预测结果与实际进水数据进行对比验证。结果表明，原始进水量与水质数据自相关检验结果显著，数据间存在自相关性，进水量和水质数据总体呈正态分布；Prophet模型在进水数据解释和预测中具有一定的有效性和准确性，尤其对进水量的预测效果优异，平均绝对百分比误差（MAPE）为4.55%。预测结果显示，该污水厂进水量具有显著的多重季节性变化特征，节假日效应显著，整体呈增长趋势；进水COD和TP总体呈下降趋势，而NH3-N和TN总体呈上升趋势，COD和TN季节性变化显著、在冬季达到浓度峰值，而NH3-N和TP波动较小、周期性特征不明显。预测该污水厂2027年9月—2028年9月的平均进水量将达到18 541 m3/d，进水COD、TN、NH3-N、TP分别为1 334.4、184.6、146.6、15.7 mg/L，该数据可为污水厂的进一步投资规划提供重要依据。
Abstract:
The raw influent data from March 2020 to August 2023 of a county-level wastewater treatment plant (WWTP) in Northwest China were preprocessed and subjected to exploratory analysis. Using this dataset as the training set, the influent data of the plant were forecasted with the open-source Prophet model based on R language. The predicted results were then compared and validated against the actual influent data. The results showed that the raw influent quantity and quality data exhibited significant autocorrelation, indicating the presence of temporal dependencies, and the overall distributions influent quantity and quality data approximate normality. The Prophet model demonstrated a certain level of validity and accuracy in interpreting and forecasting influent data, with particularly excellent performance in predicting influent quantity achieving a mean absolute percentage error (MAPE) of 4.55%. The prediction results revealed that the influent quantity of the WWTP had significant multi-seasonal variation characteristics and a notable holiday effect, with an overall increasing trend. Influent COD and TP showed a general decreasing trend, whereas NH3-N and TN displayed an overall increasing trend. COD and TN exhibited significant seasonal variations, peaking in winter, while NH3-N and TP showed smaller fluctuations and less obvious periodic patterns. The forecast indicates that from September 2027 to September 2028, the average influent quantity of the plant will reach 18 541 m3/d, with influent COD, TN, NH3-N, and TP concentrations of 1 334.4 mg/L, 184.6 mg/L, 146.6 mg/L, and 15.7 mg/L, respectively. These data can provide an important basis for further investment planning of the plant.

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更新日期/Last Update: 2026-07-01