Microalga-Mediated Tertiary Treatment of Municipal Wastewater: Removal of Nutrients and Pathogens

The microalgal strain Chlorella sorokiniana isolated from a waste stabilization pond was used for tertiary treatment of municipal wastewater. Three light:dark (L:D) regimes of 12:12, 16:8, and 24:0 were used for treating wastewater in microalga (A), microalga + sludge (A + S), and sludge (S) reactors. The removal of nutrients (N and P) was found to be the highest in the microalga-based reactor, with more than 80% removal of biochemical oxygen demand (BOD) and 1.2–5.6 log unit removal of pathogens. The addition of sludge improved chemical oxygen demand (COD) removal. Nitrifiers were found to be predominant in the A + S reactor. Algal biomass productivity was more than 280 mg/L/d in all the L:D regimes. The increase in light regime improved nutrient removal and biomass productivity in the algal reactor. Results of the kinetic study showed that (i) nitrifiers had more affinity for ammonium than microalga, and hence, most of the ammonia was oxidized to nitrate, (ii) microalga assimilated nitrate as the primary nitrogen source in the A + S reactor, and (iii) solubilization of particulate organic nitrogen originated from dead cells reduced the nitrogen removal efficiency. However, in the microalga-based reactor, the ammonium uptake was higher than nitrate uptake. Among pathogens, the removal of Salmonella and Shigella was better in the A + S reactor than in the other two reactors (microalga and sludge reactor). Additionally, the heterotrophic plate count was drastically reduced in the presence of microalga. No such drastic reduction was observed in the stand-alone sludge reactor. Kinetic modeling revealed that microalga–pathogen competition and pH-induced die-off were the two predominant factors for pathogen inactivation.

Analysis of Reactivity Accidents in Modular HTGRs

A control rod withdrawal (CRW) is a possible reactivity initiated accident (RIA) in modular high-temperature gas-cooled reactors (mHTGRs). The purpose of this study is to perform a sensitivity analysis of a CRW event in an mHTGR model using the systems code RELAP5-3D with point kinetics feedback to demonstrate the impact of uncertainty in heat transfer and reactor kinetic parameters. The adaptive Sobol decomposition method in the uncertainty quantification code RAVEN is used to perform the sensitivity study and to determine which input parameters have the greatest impact on the figures of merit, which in this case are peak reactor power and maximum fuel temperature. This study addresses a need highlighted by the Nuclear Regulatory Commission (NRC) for transient fuel testing by quantifying the impact of uncertainty in heat transfer and reactor kinetic parameters and by generating potential boundary conditions for transient testing of conventional mHTGR fuel.

Determination and Application of Improved Kinetic Parameters for Simulation of Maleic Anhydride Synthesis in Industrial Fixed- Bed Reactor

The aims of this study were to determine improved kineticparameters in five kinetic models for oxidation of n-butane intomaleic anhydride in an industrial fixed-bed reactor, and tosimulate the reactor performance. On the basis of the measuredprocess parameters, inlet and outlet concentrations of n-butanewere calculated and then used to fit the kinetic models. Theindustrial fixed-bed reactor was approximated by 10 continuousstirred tank reactors (CSTR) connected in series. Based on thecalculated outlet concentration of n-butane from the industrialreactor, the outlet concentration of n-butane from thepenultimate reactor was calculated. Then the concentrations ofn-butane were calculated until the inlet concentration of nbutanein the first reactor was obtained. Kinetic parameterswere determined by comparing the inlet concentrations of nbutanein the first reactor with the inlet concentration of nbutaneobtained on the basis of the measured processparameters in the industrial fixed-bed reactor. Kinetic modelswith improved kinetic parameters showed better simulationresults compared to kinetic models with the existing kineticparameters. The best agreement of simulation results andmeasured values was achieved with application of the kineticmodel 2 (Equations (2a-c)). The smallest deviations ofnumerical simulation in comparison with measured values of theoutlet pressure of reaction mixture were 0.45, 0.75 and 0.75%for application of the kinetic model 3 (Equations (3a-c)). Thepercentage deviations of numerical simulation with improvedkinetic parameters and the existing kinetic parameters incomparison with measured values of inside reactor temperaturewere in the range 0.90-5.36% and in the range 4.17-9.78%(kinetic model 2, Equations (2a-c)), respectively.