Study on optimal conditions of flocculation in deinking wastewater treatment

Abstract

Flocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physical methods. The goal of this paper is to determine the optimal hydraulic conditions using machine learning in order to enable efficient flocculation and improve performance during the treatment of deinking wastewater. Experiments using polymerized aluminum chloride as flocculant to treat deinking wastewater were carried out. Based on the orthogonal array test, 16 different combinations of hydraulic conditions were chosen to investigate the performance of flocculation, which was indicated by the turbidity of the solution after treatment. To develop a model representing the relationship between the hydraulic conditions and the performance of wastewater treatment, the machine learning methods, support vector regression and Gaussian process regression, were compared, whereby the support vector regression method was chosen. According to the fitness function derived from the support vector regression model, a genetic algorithm was applied to evaluate the optimal hydraulic conditions. Based on the optimal conditions determined by the genetic algorithm and real-life experience, a set of hydraulic conditions were implemented experimentally. After treatment under higher stirring speed at 120 rpm for 1 min and lower stirring speed at 20 rpm for 5 min at a temperature of 20 °C, the turbidity of deinking wastewater was measured as 1 NTU. The turbidity reduction was as high as 99.6%, which indicated good performance of the deinking wastewater treatment.

Photocatalytic degradation of drugs and dyes using a maching learning approach

“…[82][83][84][85][86][87] Machine learning algorithms have also been used to examine the hydraulic conditions for efficient occulation during wastewater treatment. 79,[88][89][90][91] The integration of machine learning algorithms with wastewater treatment processes offers numerous benets. 92 It can improve the overall efficiency and effectiveness of the treatment process, leading to higher rates of pollutant degradation and cleaner water.…”

Proceedings - The Eleventh International Symposium GRID 2022

“…However, its performance depends on different factors such as wastewater and flocculants properties etc. (Li, Hu & Wang, 2021). In the study of Li, Hu & Wang (2021), the optimization of hydraulic conditions was conducted through 16 experiments consisting of different combinations of hydraulic conditions and two ML methods were applied for its evaluation.…”

“…(Li, Hu & Wang, 2021). In the study of Li, Hu & Wang (2021), the optimization of hydraulic conditions was conducted through 16 experiments consisting of different combinations of hydraulic conditions and two ML methods were applied for its evaluation. Support vector regression was superior in comparison to Gaussian process regression for the optimization of flocculation conditions in deinking WWT.…”

“…Support vector regression was superior in comparison to Gaussian process regression for the optimization of flocculation conditions in deinking WWT. Finally, determination of optimal hydraulic conditions (high and low-stirring speed and temperature) led to the improvement of the flocculation process (Li, Hu & Wang, 2021).…”

A study on the removal of polyacrylamide by titanium dioxide nanoparticles/polyacrylonitrile composite in aqueous solutions

“…The most important factors affecting the e ciency of the coagulation process are the ions in the aqueous solution (ionic strength of water), the concentration of humic substances, pH, water temperature, and the type of coagulant. The use of water-soluble organic polymers has been very common in the pretreatment of drinking water and industrial wastewater for the past 40 years [4]. Polyacrylamide (abbreviated as PAM) is the most common water-soluble polymer composed of acrylamide molecules.…”