Modeling and Simulation of Continuous Anaerobic Reactors Applied to the Removal of Organic Micropollutants in Wastewater

• Grant number: 23/08239-4
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: March 04, 2024
• End date: July 03, 2024
• Field of knowledge: Engineering - Sanitary Engineering - Water Supply and Wastewater Treatment
• Principal Investigator: Marcelo Zaiat
• Grantee: Érika Barbosa Oliveira
• Supervisor: Sihem Tebbani
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: École Centralesupélec, France
• Associated to the scholarship: 22/13771-4 - ASSESSMENT OF HYDRAULIC RETENTION TIME ON THE REMOVAL OF ORGANIC MICROPOLLUTANTS IN SEWAGE VIA ANAEROBIC DIGESTION, BP.IC

Abstract

In response to current environmental concerns, pollution reduction, and resource reuse and recovery are the focus of various studies. One of the goals of sustainable development is to improve water quality by reducing pollution and minimizing the discharge of chemicals and hazardous materials. Organic micropollutants (OMPs) are among the most significant pollutants in wastewater. They include human and animal medicines, natural hormones, cosmetics, disinfectants, and pesticides. In recent years, various DFOs have been found in significant concentrations in wastewater. Due to the persistence of certain micropollutants in conventional effluent treatment systems, the presence of these compounds in water resources has become a concern, given the harmful effects these compounds can cause on human and environmental health. This study focuses on the use of an up-flow anaerobic digester (UASB) for wastewater treatment. This type of process has demonstrated its effectiveness in treating micropollutants, in comparison with a conventional digestion process. Based on experimental data obtained for a laboratory-scale reactor, the study will focus on modeling the bioprocess and, in particular, the dynamics of micropollutant degradation. The simulator to be developed for this system will be a simplified mathematical model. The possibility of a black box model using machine learning techniques (a priori neural networks) will also be explored. This study is a first step towards optimizing the operation of the bioprocess to maximize MPO degradation.