Modelling Micropollutant Removal Through Ozonation in Wastewater

Abstract

Proper management of water resources is a key to climate change adaptation and resilience in modern societies. Adequate treatment of wastewater is essential for ensuring the sustainability of the water cycle and the health of the environment and the ecosystems that inhabit it. It can also contribute to water supply needs in regions facing issues stemming from water stress. In this context, computer models can play an important role in assisting wastewater treatment systems facing growing populations and more stringent demands.

This study proposes the combined use of an ozone decomposition model and a micropollutant model to simulate micropollutant removal through ozonation. The model is based on the use of second-order rate constants, denominated kinetic coefficients, and it solves a continuity equation that describes the dynamics of compounds over time. It is developed from the work of Audenaert et al. (2013), which includes the presence of Dissolved Organic Matter (DOM), and adds the effect of Total Suspended Solids (TSS). It also considers fractionation steps for the Chemical Oxyen Demand (COD) and the conjugated, particulate and soluble fractions of a sample of micropollutants, to enable it to be used in combination with other treatment configurations.

The model is calibrated by using two sets of data, one from batch experiments carried out by Juárez et al. (2021) and the other from the operation of an ozonation pilot plant (Ekblad et al., 2021). The validation is then performed by comparing to experimental data from Lee et al. (2014), and achieved with an average coefficient of determination (r2) of 0.89.

Overall, the model presents a good fit for simulations with values based on batch experimentation, and to a lesser extent in comparison to the data from the pilot plant. Moreover, when the model diverges from the source data it does so with an underestimation in most cases. It also shows lower removal performance with the addition of H2O2, and better removal capacity with higher Hydraulic Retention Time (HRT). It responds, however, with negligible sensitivity to variations in pH.

This research offers many possibilities for future work, as it could be applied into risk impact studies, expanded to other micropollutant species, or improved by deepening the work on the effect of TSS, pH, nitrogen species, particulate organic matter, inorganic matter, or others.

The model can also be tested in combination with other treatment configurations and hybrid systems, or together with Computational Fluid Dynamics (CFD). Lastly, there could be a way to put the emerging potential of Artificial Intelligence (AI) to the benefit of wastewater treatment modelling.

Popular Abstract

Water is running out in many places. Why? Because of inadequate management of water resources, because of climate change, because of population growth, or a little bit of everything. When this happens, reusing the water that comes out as waste from human activities might just as well become an interesting alternative. If you don’t believe this, you are late to the party. Drinking water and even beer have already been made from treated wastewater (check out the examples of Windhoek, NEWater and Epic OneWater Brew). So, one might think that from here on it will be easy to secure water supply wherever we need it. To get there, however, there’s still a lot of work to do when it comes to cleaning water - work made easier with the help of computers.

This might sound alarming, but we still don’t know exactly what is hiding in our wastewaters. The more we improve our measuring technologies, the more components we find that can harm the environment and, therefore, us. A name coined for a notorious group of them is “micropollutants”. These are sneaky compounds that are present in the tiniest of concentrations, but that tend to accumulate over the years and cause mayhem wherever they land. They come from personal care products, drugs, pesticides, hormones, pathogens and a bunch of other stuff, and are super tricky to remove. A great deal of effort has gone into researching how to handle this problem, and many technologies have been identified and developed for this very purpose. Among those, it’s ozonation.

Ozonation, as its name hints, means using ozone to remove micropollutants from water. It is a very popular technology due to its effectiveness, but it still needs to be implemented very carefully so there are not unwanted results. Here is where computers play a very big role, as they help us simulate a lot of complex phenomena when we apply them to assist treatment processes.

In this research, a model is proposed that can be used to simulate the removal of micropollutants from ozonation in wastewater. Nine micropollutants are selected to test the model, and results are compared with data found in research papers. The model shows a good agreement with the data, and offers a starting point to keep building and expanding its capabilities. The aim is to help water engineers that are implementing ozonation, and to keep expanding what we know about its effects. Water regulations are going to keep getting ever more strict (and rightly so), and it is good that we use everything we have to continue improving the quality of the water we treat. That includes all the computer power we can harness.

• author: Oportus Foster, Maximiliano Andrés
• supervisor: Fabio Polesel; Ramesh Saagi
• organization: Chemical Engineering (M.Sc.Eng.)
• course: VVAM01 20231
• year: 2023
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Micropollutants, water reuse, wastewater treatment models, ozonation, computer modeling, water and environmental engineering
• language: English
• id: 9123936
• date added to LUP: 2023-06-22 10:55:07
• date last changed: 2023-06-22 10:55:07