Machine Learning-Driven COD Real-Time Monitoring Electrochemical Analysis System Based on Nitrogen-Etched Boron-Doped Diamond Electrode

<p dir="ltr">The development of industrial technology and urban expansion exerts considerable pressure on the ecological environment owing to the discharge of large amounts of organic wastewater. Therefore, a fast and real-time water-quality monitoring method is required to monitor wastewater treatment processes and effectively warn against unsafe water discharge. In this study, a miniaturized electrochemical (EC) detector and artificial intelligence (AI)-based real-time water quality analysis software were developed to monitor the organic components, concentrations, and chemical oxygen demand (COD) in wastewater. A nitrogen-etched boron-doped diamond (NBDD) sensing electrode was employed. It was characterized by a surface composed of relatively intact (100) planes of diamond and graphene-covered diamond nanocrystals. This electrode exhibited high electrocatalytic activity, chemical stability, and responsiveness to all six dyes. The AI software employed PCA for dimensionality reduction; SVM for dye classification; and a fusion algorithm (IRLS/MLP/GBM) for predicting COD and concentration. It achieved dye identification accuracies ranging from 82.7% to 100%. The NBDD-EC detector combined with the AI analysis system achieved high correlations between the predicted and actual values of dye concentration and COD. When installed in industrial equipment, the system demonstrated 96±2% accuracy in predicting the COD of dye wastewater. The developed NBDD-EC detection system was demonstrated to provide a reliable strategy and a complete set of hardware and software for real-time monitoring of COD. This is of high significance for monitoring sewage discharge.</p>