Abstract: Utilizing ammonia as a storage medium for hydrogen is currently receiving increased attention. A possible method to retrieve the hydrogen is by plasma-catalytic decomposition. In this work, we combined an experimental study, using a dielectric barrier discharge plasma reactor, with a plasma kinetic model, to get insights into the decomposition mechanism. The experimental results revealed a similar effect on the ammonia conversion when changing the flow rate and power, where increasing the specific energy input (higher power or lower flow rate) gave an increased conversion. A conversion as high as 82 % was achieved at a specific energy input of 18 kJ/Nl. Furthermore, when changing the discharge volume from 31 to 10 cm3, a change in the plasma distribution factor from 0.2 to 0.1 was needed in the model to best describe the conversions of the experiments. This means that a smaller plasma volume caused a higher transfer of energy through micro-discharges (non-uniform plasma), which was found to promote the decomposition of ammonia. These results indicate that it is the collisions between NH3 and the high-energy electrons that initiate the decomposition. Moreover, the rate of ammonia destruction was found by the model to be in the order of 1022 molecules/(cm3 s) during the micro-discharges, which is 5 to 6 orders of magnitude higher than in the afterglows. A considerable re-formation of ammonia was found to take place in the afterglows, limiting the overall conversion. In addition, the model revealed that implementation of packing material in the plasma introduced high concentrations of surface-bound hydrogen atoms, which introduced an additional ammonia re-formation pathway through an Eley-Rideal reaction with gas phase NH2. Furthermore, a more uniform plasma is predicted in the presence of MgAl2O4, which leads to a lower average electron energy during micro-discharges and a lower conversion (37 %) at a comparable residence time for the plasma alone (51 %).

Abstract: Nitrate contamination of groundwater has been recognized as a significant environmental problem world widely. Sulfur-driven mixotrophic denitrification has been demonstrated as a promising groundwater treatment process, which though plays an important role in nitrous oxide (N2O) emissions, significantly contributing to the overall carbon footprint of the system. However, the current process optimizations only focus on nitrate removal and excess sulfate control, with the N2O emission being ignored. In this work, an integrated mathematical model was proposed to evaluate the N2O emission as well as the excess sulfate production and carbon source utilization in sulfur-driven mixotrophic denitrification process. In this model, autotrophic and heterotrophic denitrifiers use their corresponding electron donors (sulfur and organic matter, respectively) to reduce nitrate to nitrogen gas, with each modeled as three-step denitrification (NO3 to N-2 via NO2 and N2O) driven by sulfur or organic matter to describe all potential N2O accumulation steps. The developed model, employing model parameters previously reported in literature, was successfully validated using N2O and sulfate data from two mixotrophic denitrification systems with different initial conditions. Modeling results revealed substantial N2O accumulation due to the relatively low autotrophic N2O reduction activity as compared to heterotrophic N2O reduction activity, explaining the observation that higher carbon source addition resulted in lower N2O accumulation in sulfur-driven mixotrophic denitrifying system. Based on the validated model, optimizations of the overall system performance were carried out. Application of the model to simulate long-term operations of sulfur-driven mixotrophic denitrification process indicates that longer sludge retention time reduces N2O emission due to better retention of active biomass. High-level total nitrogen removal with significant N2O emission mitigation, appropriate excess sulfate control and maximized COD utilization can be achieved simultaneously through controlling the influent nitrate and COD concentrations. (C) 2017 Elsevier Ltd. All rights reserved.