Using image texture to monitor the growth and settling of flocs

Currently, a reliable and easy-to-use method to monitor flocculation in the water treatment process is highly demand, especially for small water purification stations. For this problem, in situ images were used to analyze the flocculation process under different conditions via jar tests. A texture feature of the gray level co-occurrence matrix was found to be helpful for monitoring the floc status, such as growth rate and settling velocity. To further verify this finding, we established the correlation between the texture time sequence curve (TTSC) and its corresponding floc status. The slope of the TTSC during the growth phase and the TTSC during the settling phase can describe the growth rate and the settling velocity, respectively, i.e., the higher the slope, the higher the growth rate and settling velocity. In addition, significant differences between the TTSCs in various abnormal conditions and the normal condition of coagulation can be identified. By using the TTSC for detecting abnormal conditions, we again verified that the texture feature can reliably reflect the flocculation process. Our study helps to develop a low-cost, stable, and simple method for monitoring flocculation and detecting abnormal conditions, which can effectively be used in the operation and management of water treatment plants.

Automated summary

This study used in situ images to monitor the flocculation process in water treatment and found that the texture feature of the gray level co-occurrence matrix is helpful for monitoring the floc status. By establishing the correlation between the texture time sequence curve (TTSC) and the floc status, the growth rate and settling velocity can be accurately described. Differences between the TTSCs in abnormal conditions and the normal condition can be identified, allowing for the detection of abnormal conditions. The results of this study contribute to the development of a low-cost, stable, and simple method for monitoring flocculation and detecting abnormal conditions in water treatment plants.