Abstract
This study introduces a novel method for estimating floc conditions in sludge dewatering plants by employing image analysis and automatic control of polymer dosage. While previous research has focused on drinking water treatment plants, few reports address polymer dosage optimization using image analysis in sludge dewatering plants. The challenge lies in the high sludge dry solids hindering individual floc recognition due to overlap. The study aims to estimate floc conditions by focusing on gap areas between flocs and implementing automatic polymer dosage control accordingly. The proposed method uses images from an Internet Protocol camera and semantic segmentation to identify the floc gap area. For validation of the estimation method, variations of over and under polymer dosage scenarios were investigated and compared with commonly used floc area methods. The findings indicate that the gap area estimation effectively reproduces the theory of polymer cohesion. Automatic polymer dosage control based on this method demonstrates stable operation in both scenarios. Notably, automatic control outperformed manual operation during continuous operation, resulting in a significant reduction in polymer dosage and a notable increase in heating efficiency compared to manual control. This study presents an efficient approach to optimize polymer dosage in sludge dewatering plants, utilizing image analysis for real-time monitoring and control. By focusing on the gap area between flocs, the method enhances accuracy in estimating floc conditions, thereby improving overall dewatering efficiency. The findings highlight the practical benefits of implementing automatic control systems in sludge treatment plants, potentially reducing costs and environmental impact.