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Verified CAS / Academic Author2 Decoded Studies

Prof. CHEN Ben

School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China

Co-Affiliations:School of Environmental Science and Engineering, Sun Yat-sen University

Research Publications & English Decoded Briefs

Showing 2 publications
Chinese Journal of Environmental Engineering2026DOI: 10.12030/j.cjee.202507110

Combined Ozone Micro-Nano Bubble Oxidation and Powdered Activated Carbon Adsorption for Removal of Taste and Odor Compounds from Drinking Water

Algal-derived taste and odor compounds (2-methylisoborneol, 2-MIB, and geosmin, GSM) in drinking water sources are poorly removed by conventional treatment. This study systematically evaluated the standalone and combined performance of ozone micro-nano bubbles (O3-MNBs) oxidation and powdered activated carbon (PAC) adsorption for removing 2-MIB, GSM, and algal cells from source water. Results showed that O3-MNBs pre-oxidation achieved >97.5% removal of odorants at 400 ng·L−1 and 67.2% algal cell removal within 30 min. When applied as a deep treatment stage, the degradation rate constant (k) was 10.1%–25.6% higher than in pre-oxidation due to lower background matrix interference. Both pre-oxidation and deep treatment reduced effluent concentrations of 2-MIB and GSM to below 10 ng·L−1, with oxidation kinetics fitting pseudo-first-order models (R²>0.95). PAC adsorption of both compounds followed pseudo-second-order kinetics (R²>0.99), with GSM equilibrium adsorption capacity approximately 20.0% higher than that of 2-MIB. In pure water, adsorption capacity increased by >10.0% compared to raw water. Based on kinetic models, a quantitative prediction method was established for O3-MNBs oxidation and PAC adsorption processes, aiming to achieve efficient odorant removal and cost optimization, providing theoretical support for advanced drinking water purification and smart water plant construction.

Chinese Journal of Environmental Engineering2026DOI: 10.12030/j.cjee.202512072

Intelligent Detection of Drainage Pipeline Defects Based on Cross-Frame Annotation and Recall Optimization

Drainage pipeline defect detection predominantly relies on closed-circuit television (CCTV) inspection, which is labor-intensive, inefficient, and prone to missed detections. Although deep learning-based object detection has been applied, it suffers from low precision, recall, and speed in practical scenarios. This study proposes an engineering-oriented detection scheme achieving high recall and low miss rates. The annotation phase employs a cross-frame strategy combining manual labeling of first and last frames with interpolation and tracking-based refinement. Data preprocessing introduces perceptual hashing to identify similar images, enhancing training efficiency. For detection, a Faster R-CNN model is enhanced with Focal Loss to focus on hard examples, defect classification and grading, and a dynamic threshold strategy to improve recall. Validated on 5,068.72 m of real pipeline data, the method achieves a recall rate exceeding 98% across 16 defect categories, a miss rate of only 2% for grade 4 defects, and a 425% improvement in per-segment detection efficiency compared to manual screening. These results demonstrate the method's effectiveness in balancing recall, miss rate, and speed for engineering deployment.

Prof. CHEN Ben | Publications & Academic Profile | SinoGreenTech | SinoGreenTech