Neural network for fractal dimension evolution.

Alessandra Da Silva Oliveira,André Luiz De Oliveira,Rodrigo Braga Moruzzi,Ubirajara Coutinho Filho,Verônica Dos Santos Lopes

doi:10.2166/wst.2018.349

Abstract

The coagulation/flocculation process is an essential step in drinking water treatment. The process of formation, growth, breakage and rearrangement of the formed aggregates is key to enhancing the understanding of the flocculation process. Artificial neural networks (ANNs) are a powerful technique, which can be used to model complex problems in several areas, such as water treatment. This work evaluated the evolution of the fractal dimension of aggregates obtained through ANN modeling in the coagulation/flocculation process conducted in high apparent color water (100 ± 5 PtCo), using alum as coagulant in dosages varying from 1 to 12 mg Al3+ L-1, and shear rates from 20 to 60 s-1 for flocculation times from 1 to 60 minutes. Based on raw data, the ANN model resulted in optimized condition of 9.5 mg Al3+ L-1 and pH 6.1, for color removal of 90.5%. For fractal dimension evolution, the ANN was able to represent from 95% to 99% of the results.

Highlights

Humic substances (HS) constitute the major part of the organic matter dissolved in natural waters
It can be observed that pH values between 5.5 and 6.5 and coagulant dosage greater than 6.0 mg LÀ1 of Al3þ favored the color removal, which was greater than 75%
This work evaluated the evolution of the aggregates’ fractal dimension obtained through neural network modeling (ANN) in the coagulation/flocculation process conducted in high color water (100 ± 5 PtCo), using alum as a coagulant, and varying shear rates, expressed as average velocity gradients, and flocculation time

Summary

Introduction

Humic substances (HS) constitute the major part of the organic matter dissolved in natural waters They are derived from decomposed plant and animal biomass, and may have many adverse effects on water quality (Aftab & Hur ). In addition to the traditional method, artificial neural networks (ANNs) can be used, which are inspired by the functioning of biological nervous systems. It is a powerful technique with a strong ability to learn and predict, and has been successfully applied to model complex nonlinear problems in several areas of knowledge (Khataee et al ; Sahin et al ).

Methods

Results

Conclusion