By Dr. Brooke K. Mayer
Advanced oxidation processes (AOPs) featuring highly reactive oxidizing species, e.g., hydroxyl radicals, can degrade an array of recalcitrant organic pollutants that are not well removed using conventional water and wastewater treatment processes, e.g., personal care products, pharmaceuticals and pesticides. The advanced oxidation aspects of such systems go beyond traditional oxidation/disinfection methods, typically using additional inputs of chemicals (such as H₂O₂ or catalysts) and/or energy (such as UV or electrolysis) to accelerate reactions.
Full-scale AOPs are most commonly installed in water reuse facilities, as well as some drinking water operations. As shown in Figure 1, UV/H₂O₂ is one of the most widely used AOPs at large-scale potable reuse facilities (likewise, varying combinations of UV, H₂O₂ and/or O₃ are most widely adopted for full-scale municipal or industrial water treatment). For 24/7/365 treatment focused on organic pollutants and disinfection, total UV/H₂O₂ life cycle costs are approximately $1.00 – $5.00/1,000 gallons treated. For more selective operation, e.g., disinfection plus seasonal taste and odor control, total life cycle costs drop to approximately $0.30 – $2.50/1,000 gallons treated.
AOPs can also be operated as smaller, decentralized systems, e.g., POE or POU treatment systems, as well as portable greywater reuse operations. Whereas centralized systems will likely continue to rely on well-established AOPs, electricity-based AOPs may have strong potential for decentralized use as they avoid chemical sourcing and transport. Operating costs for highly advanced POU treatment systems may be on the order of $100/$1,000 gallons treated (although POUs offer lower initial capital costs compared to centralized facilities).
Beyond UV/H₂O₂, an array of other AOPs have shown potential in bench, pilot and full-scale configurations (see Box 1 for examples of AOPs; note that O₂ alone is considered a conventional oxidant, but it also generates hydroxyl radicals as it decomposes, leading some to classify it as an AOP or AOP-like process). Many AOPs have demonstrated effective treatment, but these processes are not equally able to remove all compounds, nor are they equally efficient with respect to energy and/or chemical inputs. Despite many years of lab-scale AOP development and testing, pilot- and full-scale operations remain rather sparse. Yet, studies at these scales are needed to overcome the difficulties in comparative evaluations due to physical and chemical differences in AOPs and to establish more direct means of comparing AOP efficiencies. This begs the question: how can we gauge AOP efficiency?
The mechanism of oxidant generation and pathways of contaminant degradation vary dramatically amongst different AOPs. Reaction efficiency can be quantified using reaction rate kinetics, which are modeled as zero, first or second order depending on the target compound and oxidant pairing. Understanding the kinetics of the reaction, at least the order thereof, is key to understanding advanced treatment processes. Extensive databases of oxidant reaction rates with organic micropollutants are available. Organic degradation using AOPs can often be phenomenologically modeled using pseudo first-order kinetics. Table 1 shows several examples of first order rate constants for degradation of carbamazepine (a pharmaceutical), methyl orange (a common dye) and nitrobenzene (an industrial chemical) using a range of AOPs.
Notably, kinetic rate constants characterize reaction efficiency, but do not encompass other aspects of AOP efficiency, for example the costs to operate a system. To help address this issue, Bolton et al. (2001) developed the figure of merit Electrical Energy per Order (EEO; Equation 1). This metric is defined as the electrical energy normalized to reactor volume required to decrease a target contaminant’s concentration by one order of magnitude. Electrical energy consumption often accounts for a major fraction of AOP operating costs and EEO values <1 kW/m³/order are often considered most viable for full-scale operation.[1,13]
where: EEO is electrical energy per order (kW/m³/order), P is system power (kW), V is volume of water treated (L) in time t (hr), C₀ is the initial contaminant concentration (EEO is valid for low initial concentrations, typically < 100 mg/L1) and C is the final concentration.
Table 1. AOP comparison assessed using kinetic rate constants and electrical energy per order (EEO) values. Data are from the direct process comparison performed by Ambrogi et al. (2019) (with EEO values estimated from graphs). Higher process efficiency is indicated by higher rate constants and lower EEO values (green in the color scale).
Differences in interpretations of AOP efficiency using rate constants versus EEO are evident, as illustrated by the color scale in Table 1. For instance, while the O³-based AOPs were characterized by rapid kinetics, energy inputs to generate O³ led to higher EEO values, particularly in the case of methyl orange and nitrobenzene.
Miklos et al. (2018) conducted a carefully curated review of AOP EEOs, as summarized in Figure 2. Despite high variability, the EEOs suggest that ozone- and UV-based AOPs are the most readily applicable for full-scale implementation given that their median energy efficiencies are within the realistic realm of EEO <1 kWh/m³/order. Within this group of technologies, there was no statistical difference among energy efficiencies, with observed variations likely stemming from experimental conditions. In direct comparison tests, however, ozone-based AOPs were reportedly more energy efficient for production of hydroxyl radicals9 and degradation of organic micropollutants compared to UV/H₂O₂ (EEOs for which are generally about 4 – 20 times higher than for O₃/H₂O₂⁶). Additional considerations for ozone-based AOPs include the potential to generate DBPs such as bromate1,14 and the reduced disinfection potential of O₃/H₂O₂ compared to UV/H₂O₂.
Technologies with median EEOs > 1 kWh/m³/order (Figure 2) are less likely to be used in typical large-scale installations in the near-term as current configurations are likely too energy intensive. These technologies, however, warrant further investigation targeting energy savings. In particular, AOPs with median EEO values between 1 and 100 kWh/m3/order may still provide attractive solutions for specific challenges.
One approach to improve energy efficiency is using renewable energy sources to power AOPs. For example, the World Health Organization did not recommend ozone for POU treatment because of the need for a reliable source of electricity for ozone generation, the complexity of the system for appropriate operation and dosing and its relatively high cost. However, alternate sources of energy may alleviate some of these challenges. For example, solar-driven AOPs can reduce energy use and enable AOP deployment in low income or off-the grid applications, i.e., rural or developing areas, transient systems (e.g., emergency response, military operations), etc.[17,18]
The high variability in EEOs reported in the literature to date, particularly for non-optimized, lab-scale tests, illustrates strong dependence on system configuration, process capacity, energy-independent parameters such as chemical doses and water matrix.[1,10] Thus, the EEO metric is most relevant for AOPs that have been optimized in terms of oxidant demand, reactor geometry and other process-specific parameters. In most cases, full-scale EEO data will be lower than bench- or pilot-scale EEO results.[1,3] Another caveat is that while EEO accounts for energy costs, it does not account for chemical costs, which are essential to consider for any real-world application. Some researchers have accounted for such costs by incorporating estimated costs of H₂O₂ production in EEO assessments, but this has not been done universally. An additional real-world consideration is the impact of water quality; EEO comparisons across experiments performed in different water matrices is not recommended. Accordingly, direct experimental AOP comparisons using equivalent conditions (e.g., water matrix) are most desirable as they provide the data needed to scale-up and evaluate AOP economics and sustainability against conventional technologies. Although EEO cannot fully characterize AOP performance, it is a powerful metric that enables a simple comparison of the magnitude of energy efficiency across widely variable AOP technologies.
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About the author
Dr. Brooke K. Mayer is an Associate Professor in the Department of Civil, Construction and Environmental Engineering as part of the Opus College of Engineering at Marquette University. She holds Bachelors, Masters and Doctorate Degrees in civil engineering with an emphasis in environmental engineering from Arizona State University. She is a registered Professional Engineer in the state of Arizona.