Evaluation of a liquid-phase plasma discharge process for ammonia oxidation in wastewater: Process optimization and kinetic modeling
Graphical abstract
Introduction
Ammonia is the second-most produced chemical commodity after sulfuric acid (Ghavam et al., 2021) and is widely used in industrial manufacturing processes including chemical fertilizer production, petroleum refineries, and metallurgy. As a result, it is a prevalent environmental pollutant that contributes significantly to water pollution problems (Huff et al., 2013; Ahmed and Lan, 2012). Ammonia nitrogen (NH3N) is used in measuring the quantity of ammonia ions present in water or waste solvents (Morris et al., 2019). It includes both the non-ionized (NH3) and ionized form (NH4+), which are both pH and temperature dependent. In aqueous solutions, NH3N generally exists as ammonium ions [NH4+] at pH below 8, whereas NH3 is the predominant form when the pH is above 9.75 (Zhang et al., 2018). These two species of NH3N are the most common source of nitrogen contamination in drinking water and the primary cause of eutrophication, which is harmful to fish and other aquatic organisms (Capodaglio et al., 2016; Liu et al., 2018).
In response to these issues, research on wastewater treatment technologies has increased significantly in recent years owing to stringent environmental regulatory requirements for the disposal of nitrogen-containing compounds into receiving water systems. Biological treatment, which consists of a combination of aerobic nitrification and anaerobic denitrification using specific types of microorganisms, is the most commonly employed method for treating municipal and industrial wastewaters with low NH3N concentrations (≤100 mg/L) (Cho et al., 2020; Winkler and Straka, 2019; Shao and Wu, 2021). Even though this process is cheap and relatively easy to implement, it has limited flexibility and is generally difficult to control since it is readily inhibited by low-dissolved oxygen, high levels of toxic organics (alcohols, aldehydes, and phenols), fluctuations in pH, low winter temperatures, and high concentrations of NH3N (You et al., 2020). Furthermore, the process sometimes requires the addition of methanol (addition of pollutants) due to a lack or shortage of carbon sources for the denitrification stage (Karri et al., 2018), including large clarifiers and aeration tanks, making it difficult to implement in areas where land is expensive or limited. It also requires long treatment times (>1 week), and large volumes of sludge are often produced, which requires further disposal (Akpor and Muchie, 2010). Other physicochemical treatment technologies such as air stripping (Limoli et al., 2016), struvite precipitation (Wu and Vaneeckhaute, 2021), ion exchange (Jorgensen and Weatherley, 2003), activated carbon fiber adsorption (Zheng et al., 2016), and breakpoint chlorination (Shao and Wu, 2021) can also be used to reduce or remove NH3N from wastewater. However, most of these technologies require the use of large quantities of chemicals and often produce harmful emissions or secondary contaminants. Consequently, new wastewater treatment technology developments are needed to effectively remove NH3N from wastewater sources.
Over the years, advanced oxidation processes (AOPs), such as photocatalysis, electrolysis (Zheng et al., 2020) peroxone-process (O3/H2O2), Fenton processes, UV-based processes (UV/H2O2), and liquid phase plasma discharge, have attracted a lot of attention as viable alternative options for wastewater treatment owing to the in-situ generation of strong oxygen-based oxidizers such as hydrogen peroxide (H2O2), atomic oxygen (O), ozone (O3) as well as hydroxyl radicals [OH], which is an excellent oxidant (E° = 2.85 V) that is able to degrade hazardous compounds in water (Jiang et al., 2014; Miklos et al., 2018). Among these, liquid phase plasma discharge has emerged as the most suitable option for wastewater treatment due to its high efficiency, simple operation, low energy cost, and ability to remove pollutants from highly contaminated effluents without producing any harmful gasses and toxic sludge (Magureanu et al., 2011). Plasma is generally formed by applying a high voltage discharge (electric field) between two electrodes—one high voltage and one grounded (Schmidt et al., 2015). In the gas-liquid interface, this generates a reactive environment of heat, ultraviolet (UV) light, shockwaves, and multiple reactive chemical species such as •OH, ‧O3 ‧O, ‧H, ‧HO2, ‧O2–, H2, O2, H2O2, including hydrated electrons (e–aq) that can react non selectively with the most pollutants in solution (Yang et al., 2018; Stratton et al., 2015). Numerous studies with a variety of electrical discharge plasma reactor types have demonstrated that plasma technology can efficiently remove a wide range of organic and inorganic contaminants in wastewater, including 4-chlorophenol (Marković et al., 2015), nitrophenols (Shang et al., 2019), dyes (Li et al., 2019; Krosuri et al., 2021), pesticides (Mousavi et al., 2017), per-fluorinated alkyl substances (PFAS) (Singh et al., 2019), volatile organic compounds (VOCs) (Lu et al., 2019), pharmaceuticals and personal care products (Magureanu et al., 2018; Guo et al., 2020). However, plasma-based degradation studies of NH3N removal from wastewater are very scarce. To the best of the authors' knowledge, there are no studies on optimizing NH3N removal from aqueous solutions by liquid-phase plasma discharge using response surface methods (RSM). RSM is a set of statistical tools that can be used for process design and optimization by evaluating the interactive effects of various process parameters on a particular response variable under investigation. The use of such statistical tools for optimizing process variables is essential for the efficient use of time and resources.
Therefore, the primary objective of this work was to fill this knowledge gap by using RSM to determine the effects of the primary operating variables, including initial NH3N concentration, power, and gas-flow rate, on the removal of ammonia from synthetic aqueous solutions treated by liquid phase plasma technology. The optimal operating conditions for maximum NH3N removal were also determined, and an empirical model correlating the NH3N removal efficiency to the three variables was then developed. It is also the first time that a continuous flow plasma reactor (with wastewater recirculation) has been used to degrade/remove NH3N from aqueous solutions.
Section snippets
Reagents and experimental setup
The simulated NH3N contaminated wastewater [50, 125, and 200 mgL−1 NH3N] used in this study was prepared by dissolving specific amounts of NH4Cl (Sigma–Aldrich) into distilled water at room temperature. The initial pH and conductivity of the solution were 4.62 and 2.2 m/s, respectively. In each experiment, 300 mL of NH3N solution with a specified concentration was placed in the plasma reactor and treated for 2 hrs. A schematic representation of the CLPD system is presented in Fig. 1. The
Regression analysis for liquid phase plasma activity on NH3N removal
In this study, the removal of NH3N from aqueous solutions was investigated using a continuous-flow liquid plasma discharge reactor. The effects of process variables such as NH3N concentration (50–200 mg/L), power input (150–300 W), and gas-flow rate (0.5–2.5 L/min) on the NH3N removal efficiency were investigated using RSM according to the BBD. The design matrix of the variables by BBD experimental design, together with the predicted and experimental values of the responses, i.e., NH3N
Conclusions
In this work, the feasibility of NH3N removal from aqueous solutions was investigated using a continuous flow liquid phase plasma discharge process. The effects of various plasma operating parameters, including discharge power, gas-flow rate, and initial concentration of NH3N on the NH3N removal efficiency, as well as the binary interactions between these parameters, were investigated using the Box–Behnken experimental design of RSM. All three independent variables were effective in the NH3N
Declaration of Competing Interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Sarah Wu reports financial support was provided by National Institute of Food and Agriculture.
Acknowledgements
This work is financially supported by the USDA National Institute of Food and Agriculture (NIFA) Foundational and Applied Science Program (Grant #: 2020–67022–31699) and USDA NIFA Hatch project IDA01723, United States.
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