| 摘要: |
| 废水污染物排放的精确预测有助于企业合理安排生产计划,减少污染排放。本研究以江苏省某电子制造企业为例,基于每小时工况数据和化学需氧量排放数据(12127组样本),构建基于XGBoost的实时预测模型,比较了不同特征选择情况下的模型精度差异,并与其他机器学习算法进行对比,结果表明：对于长时间序列的污染物预测,在用电数据的基础上增加污染物历史排放数据作为特征变量,能够有效提升模型预测精度。相较于LightGBM、Random Forest、GBR等机器学习算法,XGBoost预测效果更优,其R2为0.95,RMSE为171.32 g/h,MAPE为13.55% ,MAE为49.53 g/h。该模型在前三周的预测效果优于三周后,对未来短时间内的预测效果好,对于中长期预测需要通过迭代更新来保持模型预测精度。本方法可以快速预测企业未来污染物排放量,支撑企业合理安排生产计划,为环境管理决策提供技术支持。 |
| 关键词: XGBoost 特征选择 用电量 污染物排放 预测 |
| DOI： |
| 分类号:X83 |
| 基金项目:苏州市科技计划项目 |
|
| Prediction of Chemical Oxygen Demand Emissions from Wastewater Enterprises Based on Electricity Consumption Data |
|
LU Zhixiang, MA Xu, WU Zhijie
|
| Abstract: |
| Accurate prediction of wastewater pollutant emissions contributes to the rational planning of production schedules, reduction of pollution emission. In this study, taking a company in Jiangsu Province as an example, a total of 12,127 samples were used to build an XGBoost prediction model based on hourly operating data and hourly Chemical Oxygen Demand emissions data. The differences in model accuracy were compared under different feature selection scenarios and were compared with other machine learning algorithms. The results showed that for long-term pollutant prediction, the prediction accuracy of the model could be effectively improved by adding historical pollutant emission data as feature variables on top of electricity consumption data. Compared with machine learning algorithms such as LightGBM, Random Forest, and GBR, XGBoost showed the best prediction with a R2 of 0.95, RMSE of 171.32 g/h, MAPE of 13.55%, and MAE of 49.53 g/h. The XGBoost predicts better in the first three weeks than after three weeks, and is good for short-term predictions. It requires iterative updating to maintain model prediction accuracy for medium and long-term predictions. This method enables the precise prediction of future pollutant emissions from enterprises and provides support for environmental management decision making. |
| Key words: XGBoost Feature selection Electricity consumption Pollutant emissions Prediction |
