Datasheet_YEH.csv

This study presents advanced research on using Artificial Neural Networks (ANNs) to predict Chemical Oxygen Demand (COD) in wastewater treatment, specifically focusing on herbicide degradation. Here are the key aspects:Research Objective:- To develop and evaluate ANN models for predicting COD removal during the degradation of commercial herbicides (Alazine and Gesaprim)- To compare different backpropagation algorithms for training the neural networks- To identify the most influential parameters affecting COD removal<br>Methodology:1. Experimental Setup:- Used a photochemical reactor with 250ml capacity- Employed UV lamp (15W, 352nm) and ultrasonic probe (500W, 20kHz)- Monitored six key variables: * Reaction time * pH * TiO₂ concentration * UV light intensity * Ultrasound frequency * Herbicide concentration<br>2. Neural Network Implementation:- Tested five backpropagation algorithms: * Gradient Descent * Conjugate Gradient * Scaled Conjugate Gradient * Quasi-Newton * Levenberg-Marquardt- Conducted 30 independent runs for each algorithm- Split data: 70% training, 30% testing<br>Key Findings:1. Algorithm Performance:- Levenberg-Marquardt algorithm showed superior performance- Achieved R² value of 0.9999- Demonstrated lowest Mean Square Error (MSE)- Showed statistical significance in performance difference compared to other algorithms<br>2. Parameter Influence:- Reaction time was the most influential parameter (59.91% relative importance)- Followed by: * Herbicide concentration * TiO₂ concentration * pH * Ultrasound frequency * UV light intensity<br>3. Model Accuracy:- Successfully predicted COD values with high precision- Showed strong correlation between experimental and predicted values- Demonstrated robust performance across different operating conditions<br>Significance:- Provides a reliable tool for real-time monitoring of wastewater treatment- Offers potential for process optimization in industrial applications- Contributes to more efficient herbicide removal from contaminated water- Advances the field of environmental engineering through AI application<br>Our research represents a significant advancement in applying machine learning to environmental engineering, particularly in wastewater treatment optimization and herbicide degradation monitoring.<br>