Novel approaches for predicting efficiency in helically coiled tube flocculators using regression models and artificial neural networks. (16th May 2019)

• Record Type: Journal Article
• Authors: Oliveira, D. S.; Teixeira, E. C.; Donadel, C. B.
• Is Part Of: Water and environment journal. Volume 34:Number 4(2020)
• Page Start: 550
• Page End: 562
• Publication Date: 2019-05-16
• Subjects: artificial neural networks -- CFD modelling -- efficiency prediction model -- experimental modelling -- helically coiled tube flocculators -- regression models; Sewage -- Periodicals; Water-supply -- Periodicals; Environmental management -- Periodicals; Water-supply engineering -- Periodicals; Pollution -- Periodicals; 628.1
• DOI: 10.1111/wej.12484
• Languages: English
• ISSNs: 1747-6585

Abstract

Abstract: In 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 ( R 2 ) 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, R 2 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.