Modelling the predictive analysis of turbidity removal efficiency in the in-line coagulation and flocculation process

Coagulation and flocculation processes are commonly used in conventional water treatment plants to remove suspended solids from water. However, these methods often suffer from drawbacks such as high chemical consumption, sludge generation, and the need for huge areas, leading to high construction costs. To address these limitations, this study investigated the performance of in-line coagulation and flocculation processes as an alternative to conventional methods. The objectives were to determine the efficiency of in-line coagulation and flocculation, understand the underlying mechanisms, establish optimal operating conditions and design criteria, and to develop theoretical models for predicting turbidity removal efficiency in the in-line coagulation-flocculation process. Experiments were conducted using a continuous setup consisting of a static mixer and a 35-m helically coiled hydraulic tube flocculator.

Various operating parameters, including water flow rate (100 – 800 L/hr), initial turbidity (20 – 200 NTU), and coagulant types (aluminum sulfate, poly aluminum chloride, aluminum chlorohydrate, and ferric chloride), were examined. The results demonstrated that aluminum sulfate was the appropriate coagulant for the water characteristics studied, and the in-line coagulation and flocculation processes achieved turbidity removal efficiencies of approximately 91 % under all operating conditions, with a notable 97 % removal efficiency achieved at a liquid flow rate of 600 L/hr, with a Gt value of 21,715 and an overflow rate of 2 m/hr. A prediction model of turbidity removal efficiency was developed, showing good agreement between experimental and predicted removal efficiency model, with an average deviation of about 20 %. This model can aid in determining optimal operating conditions and serve as a hybrid process in future water treatment systems, potentially applicable to other separation processes as well.