Uncertainty and sensitivity analysis for reducing greenhouse gas emissions from wastewater treatment plants

This paper presents the sensitivity and uncertainty analysis of a plant-wide mathematical model for wastewater treatment plants (WWTPs). The mathematical model assesses direct and indirect (due to the energy consumption) greenhouse gases (GHG) emissions from a WWTP employing a whole-plant approach. The model includes: (i) the kinetic/mass-balance based model regarding nitrogen; (ii) twostep
nitrification process; (iii) N2O formation both during nitrification and denitrification (as dissolved and off-gas concentration). Important model factors have been selected by using the Extended-Fourier Amplitude Sensitivity Testing (FAST) global sensitivity analysis method. A scenario analysis has been performed in order to evaluate the uncertainty related to all selected important model factors (scenario 1), important model factors related to the influent features (scenario 2) and important model factors related to the operational conditions (scenario 3). The main objective of this paper was to analyse the key factors and sources of uncertainty at a plant-wide scale influencing the
most relevant model outputs: direct and indirect (DIR,CO2eq and IND,CO2eq, respectively), effluent quality index (EQI), chemical oxygen demand (COD) and total nitrogen (TN) effluent concentration (CODOUT and TNOUT, respectively). Sensitivity analysis shows that model factors related to the influent wastewater and primary effluent COD fractionation exhibit a significant impact on direct, indirect and
EQI model factors. Uncertainty analysis reveals that outflow TNOUT has the highest uncertainty in terms of relative uncertainty band for scenario 1 and scenario 2. Therefore, uncertainty of influential model factors and influent fractionation factors has a relevant role on total nitrogen prediction. Results of the uncertainty analysis show that the uncertainty of model prediction decreases after fixing stoichiometric/kinetic model factors

Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty

This study investigated reservoir operation under climate change for a base period (1981–2000) and future period (2011–2030). Different climate change models, based on A2 scenario, were used and the HAD-CM3 model, considering uncertainty, among other climate change models was found to be the best model. For the Dez basin in Iran, considered as a case study, the climate change models predicted increasing temperature from 1.16 to 2.5C and decreasing precipitation for the future period. Also, runoff volume for the basin would decrease and irrigation demand for the downstream consumption would increase for the future period. A hybrid framework (optimization-climate change) was used for reservoir operation and the bat algorithm was used for minimization of irrigation deficit. A genetic algorithm and a particle swarm algorithm were selected for comparison with the bat algorithm. The reliability, resiliency, and vulnerability indices, based on a multi-criteria model, were used to select the base method for reservoir operation. Results showed the volume of water to be released for the future period, based on all evolutionary algorithms used, was less than for the base period, and the bat algorithm with high-reliability index and low vulnerability index performed better among other evolutionary algorithms.

Nitrous oxide emission from full-scale municipal aerobic granular sludge

The nitrous oxides emission was measured over 7 months in the full-scale aerobic granular sludge plant in Dinxperlo, the Netherlands. Nitrous oxide concentrations were measured in the bulk liquid and the off-gas of the Nereda®reactor. Combined with the batch wise operation of the reactor, this gave a high information density and a better insight into N 2 O emission in general. The average emission factor was 0.33% based on the total nitrogen concentration in the influent. The yearly average emission factor was estimated to be between 0.25% and 0.30%. The average emission factor is comparable to continuous activated sludge plants, using flocculent sludge, and it is low compared to other sequencing batch systems. The variability in the emission factor increased when the reactor temperature was below 14 °C, showing higher emission factors during the winter period. A change in the process control in the winter period reduced the variability, reducing the emission factors to a level comparable to the summer period. Different process control might be necessary at high and low temperatures to obtain a consistently low nitrous oxide emission. Rainy weather conditions lowered the emission factor, also in the dry weather flow batches following the rainy weather batches. This was attributed to the first flush from the sewer at the start of rainy weather conditions, resulting in a temporarily increased sludge loading.

A decade of nitrous oxide (N2O) monitoring in full-scale wastewater treatment processes: A critical review

Direct nitrous oxide (N2O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N2O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N2O calculation. The full-scale N2O monitoring campaigns help to expand our
knowledge on the N2O production pathways and the triggering operational conditions of processes. The accurate N2O monitoring could help to find better process control solutions to mitigate N2O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N2O fluxes in WWTPs remains challenging and costly.

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