基于遗传算法优化BP神经网络的水生态修复原位控浊混凝投药预测

余峰; 王珂佳; 张文龙; 李轶

doi:10.13205/j.hjgc.202304022

基于遗传算法优化BP神经网络的水生态修复原位控浊混凝投药预测

doi: 10.13205/j.hjgc.202304022

余峰^1,2,
王珂佳²,
张文龙^1,2, ,,
李轶^1,2

1. 河海大学环境学院浅水湖泊综合治理与资源开发教育部重点实验室, 南京 210098;
2. 河海大学环境学院, 南京 210098

基金项目:

江苏省碳达峰碳中和科技创新专项重大科技示范项目(BE2021783)

详细信息

作者简介:
余峰(1998-),女,硕士研究生,主要研究方向为水资源保护和生态修复。yufeng98616@qq.com

通讯作者:
张文龙(1987-),男,教授,主要研究方向为水资源保护和生态修复。1223zhangwenlong@163.com

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出版历程
- 收稿日期: 2022-02-21
- 网络出版日期: 2023-05-26
- 刊出日期: 2023-04-01

PREDICTION OF COAGULANT DOSAGE FOR IN-SITU TURBIDITY CONTROL IN WATER ECOLOGICAL RESTORATION BASED ON BP NEURAL NETWORK OPTIMIZED BY GENETIC ALGORITHM

YU Feng^1,2,
WANG Kejia²,
ZHANG Wenlong^{1,2
, ,},
LI Yi^1,2

1. Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China;
2. College of Environment, Hohai University, Nanjing 210098, China

摘要

摘要: 暴雨过后河湖浑浊度急剧升高,严重干扰了河湖水生态修复工程中沉水植物的恢复和重建。针对水生态修复原位混凝控浊过程中混凝剂选型和投加量难以确定的问题,对模拟河湖浑浊水样进行混凝实验并构建混凝预测数据集,采用BP神经网络模型对混凝剂投加量进行预测,并结合遗传算法对模型进行优化。基于混凝实验结果,选取综合混凝效果更佳和成本更低的混凝剂(硫酸铝),和不同投加量间混凝效果存在显著差异的混凝剂投加量范围在0~30 mg/L的混凝数据进行混凝预测模型的训练。结果表明:1)BP神经网络回归模型性能(R²=0.78)优于多元非线性、多元线性回归模型和BP神经网络分类模型,88.67%的样本预测绝对误差<5 mg/L;经遗传算法优化后,模型R²提升至0.86%且95%以上的样本预测绝对误差<5 mg/L,说明遗传算法有效提升了模型的预测精度和预测稳定性。2)混凝剂投药梯度是除建模数据量之外另一个影响模型性能的重要因素,在实际工程应用中,应尽可能增加建模数据量和降低投药梯度,以提高混凝投药预测模型性能。研究结果可为水生态修复原位混凝控浊过程中混凝剂种类和投加量的选择提供可靠理论依据。
- BP神经网络 /
- 遗传算法 /
- 水生态修复 /
- 原位混凝 /
- 优化混凝剂投量
Abstract: After the rainstorm, the turbidity of rivers and lakes increased sharply, which seriously interfered with the restoration and reconstruction of submerged plants in the ecological restoration projects of rivers and lakes. For the problem that the selection and dosage of coagulants in the process of in-situ coagulation and turbidity control in water ecological restoration are difficult to determine, in this study, the simulated river and lake turbid water samples were coagulated under laboratory conditions, and the coagulation prediction data set was constructed. BP neural network model was used to predict the dosage of coagulant, and the genetic algorithm was used to optimize the prediction model. Based on the coagulation experiment results, and the coagulation effect and cost, the coagulation data of the coagulant (aluminum sulfate) with better coagulation performance, and the dosage range (0~30 mg/L) with significant differences in the coagulation effect between different dosages were selected to train the coagulation prediction model. The results showed that, 1) the performance of the BP neural network regression model (R² was 0.78) was better than the multivariate nonlinear, multiple linear regression model and BP neural network classification model, and the prediction error of 88.67% of the samples was below 5 mg/L. After optimization by the genetic algorithm, the model R² was improved to 0.86 and the prediction error of more than 95% of the samples was below 5 mg/L, indicating that the genetic algorithm effectively improved the prediction accuracy and prediction stability of the model. 2) in addition to the amount of modelling data, the coagulant dosing gradient was another important factor affecting the performance of the model. In practical application, the amount of modelling data should be increased as much as possible and the dosing gradient should be reduced, to improve the performance of the coagulation dosing prediction model. The research results provide a reliable theoretical basis for the selection of coagulant types and dosage in the process of in-situ coagulation and turbidity control in water ecological restoration.
- BP neural network /
- genetic algorithm /
- water environment restoration /
- in-situ coagulation /
- optimize coagulant dosage

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