一种基于机器学习的吸附出水水质预测方法及系统
By optimizing the random forest model based on machine learning and the cross-validation method, the problem of real-time prediction of adsorption effluent water quality analysis was solved, achieving rapid and accurate water quality prediction, reducing costs and avoiding secondary pollution.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG UNIV
- Filing Date
- 2023-07-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing adsorption effluent water quality analysis methods require additional testing instruments and manpower, and sensor detection is costly and poses a risk of secondary pollution, making real-time prediction difficult.
Based on machine learning methods, a random forest model is constructed by acquiring the operating parameters of the adsorption process and the influent water quality data. The model is then optimized by combining cross-validation to achieve rapid and accurate prediction of the adsorption effluent water quality.
It enables rapid and accurate prediction of the quality of adsorbed water, reduces the investment of manpower and material resources, avoids the risk of secondary pollution introduced by sensing equipment, and reduces water treatment costs.
Smart Images
Figure CN117010278B_ABST