Using image texture to monitor the growth and settling of flocs
Qidong Ma,
Yan Liu,
Zhangwei He,
Haiguang Wang,
Ruolan Wang,
Yueping Kong,
Zhihua Li
Affiliations
Qidong Ma
Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Yan Liu
Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Zhangwei He
Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Haiguang Wang
Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Ruolan Wang
Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Yueping Kong
Xi'an Key Laboratory of Intelligent Equipment Technology for Environmental Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Zhihua Li
Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Currently, a reliable and easy-to-use method to monitor flocculation in the water treatment process is highly demanded, 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 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. HIGHLIGHTS Compared with the morphological features, the texture features are more stable.; The texture time sequence curve (TTSC) can describe flocculation.; Variation characteristics of TTSC can detect abnormal coagulation conditions.; TTSC-based methods can be readily applied online.;
