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Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural
G Baskar1, A N Parameswaran2, R Sathyanathan3
1Head of the Department, Civil Engineering, Adhiyamaan College of Engineering, Hosur, 635130, India. hod_civil@adhiyamaan.ac.in.
Environmental Monitoring and Assessment
|January 17, 2025
Summary
A new method optimizes papermaking wastewater treatment by predicting chemical oxygen demand (COD) using node-level capsule graph neural networks. This approach enhances monitoring accuracy and efficiency for industrial wastewater management.
Area of Science:
- Environmental Science
- Chemical Engineering
- Data Science
Background:
- Papermaking wastewater requires precise real-time monitoring of effluent indices, particularly chemical oxygen demand (COD), due to complex, nonlinear, and time-varying treatment processes.
- Traditional COD prediction models often exhibit parameter sensitivity and lack interpretability, necessitating advancements in industrial wastewater treatment monitoring.
Purpose of the Study:
- To propose an optimized papermaking wastewater treatment method for predicting effluent quality.
- To enhance the accuracy of predicting critical effluent chemical oxygen demand (COD) indices.
Main Methods:
- Development of a node-level capsule graph neural network (NLCGNN) model for predicting papermaking wastewater effluent quality (PWWT-PEQ-NLCGNN).
- Optimization of NLCGNN weight parameters using the hermit crab optimization (HCO) algorithm to improve COD prediction accuracy.
Main Results:
- The proposed PWWT-PEQ-NLCGNN technique demonstrated significant improvements over existing methods.
- Achieved higher accuracy (30.53%, 23.34%, 32.64%), precision (20.53%, 25.34%, 29.64%), and sensitivity (20.53%, 25.34%, 29.64%) compared to benchmark models.
- Outperformed models including WQP-GPR-DL-CLPWWTS, POEQ-PWWTP-DKBELM, and QRM-PWWTP-DMPLS.
Conclusions:
- The PWWT-PEQ-NLCGNN method offers a promising solution for timely and accurate monitoring of papermaking wastewater treatment processes.
- Optimized NLCGNN with HCO algorithm effectively enhances the prediction of key effluent quality indices like COD.
- The study highlights the potential for advanced machine learning techniques in improving industrial wastewater management and environmental protection.
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