深水港口废水污染物排放量预测与治理措施研究  

Prediction and Control Measures of Wastewater Pollutant Discharge in Deepwater Ports

作　　者：张琦 周瑞琦 张冠君 吕淑琪 鲁鉴予 Zhang Qi;Zhou Ruiqi;Zhang Guanjun;Lv Shuqi;Lu Jianyu(Key Laboratory of Environmental Protection in Water Transport Engineering Ministry of Communications,Tianjin 300456,China;Tianjin Research Institute for Water Transport Engineering,M.O.T,Tianjin 300456,China)

机构地区：[1]交通运输部天津水运工程科学研究所,天津300456 [2]水路交通环境保护技术交通行业重点实验室,天津300456

出　　处：《环境科学与管理》2023年第3期82-86,共5页Environmental Science and Management

摘　　要：针对因废水污染物排放量数据维度高而导致预测较为复杂的问题,提出深水港口废水污染物排放量预测方法。首先,通过小波包分解和重构对原始数据去噪处理,提高数据质量。然后,采用主成分分析法对数据特征实施降维处理,再利用改进的遗传算法获取BP神经网络的初始连接权值和偏置,构建废水污染物排放量预测模型,将数据特征输入到模型中,输出预测结果。基于此,从制定科学合理的废水治理规划、完善废水处理基础设施和优化废水处理科学技术3个方面,提出了针对性的深水港口废水污染物治理措施。Aiming at the problem that the prediction is complicated due to the high dimension of wastewater pollutant discharge data,a prediction method of wastewater pollutant discharge from deepwater ports is proposed.Firstly,the original data is de-noised through wavelet packet decomposition and reconstruction to improve the data quality.Then,the principal component analysis method is used to reduce the dimension of the data features,and then the improved genetic algorithm is used to obtain the initial connection weight and bias of the BP neural network to build a prediction model for the discharge of wastewater pollutants.The study input the data features into the model,and output the prediction results.Based on this,from three aspects of formulating scientific and reasonable wastewater treatment plan,improving wastewater treatment infrastructure and optimizing wastewater treatment science and technology,targeted measures for wastewater treatment in deepwater ports are proposed.

关 键 词：深水港口 废水污染物排放量预测 废水污染物治理 小波包去噪 BP神经网络 

分 类 号：X391[环境科学与工程—环境工程]