Novel approaches for predicting efficiency in helically coiled tube flocculators using regression models and artificial neural networks

Abstract

AbstractIn this paper, prediction models for turbidity removal efficiency (TRE) in helically coiled tube flocculators (HCTFs) are presented. The TRE was determined by physically modelling a compact, high‐performance and low detention time clarification system composed of a HCTF coupled to a decantation system. The values of hydrodynamic representative parameters of the flow were determined by CFD modelling. Eighty‐four different configurations of HCTFs were evaluated. Multiple linear/non‐linear regression and artificial neural network analyses were performed. A determination coefficient (R2) of 0.81 was obtained using multiple linear regression with the geometric and hydraulic parameters. In this model, the root mean squared error (RMSE) was 3.29%. Adding hydrodynamic parameters and using the artificial neural networks, R2 reaches 0.96 and RMSE decay to 1.58%. These results indicate that the use of effective efficiency prediction models can be helpful in the design of new flocculation units and for the improvement of existing ones.