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) two-step 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.

Integrated membrane bioreactors modelling: A review on new comprehensive modelling framework.

Integrated Membrane Bioreactor (MBR) models, combination of biological and physical models, have been representing powerful tools for the accomplishment of high environmental sustainability. This paper, produced by the International Water Association (IWA) Task Group on Membrane Modelling and Control, reviews the state-of-the-art, identifying gaps for future researches, and proposes a new integrated MBR modelling framework. In particular, the framework aims to guide researchers and managers in pursuing good performances of MBRs in terms of effluent quality, operating costs (such as membrane fouling, energy consumption due to aeration) and mitigation of greenhouse gas emissions.

Aeration control in membrane bioreactor for sustainable environmental footprint.

In this study different scenarios were scrutinized to minimize the energy consumption of a membrane bioreactor system for wastewater treatment. Open-loop and closed-loop scenarios were investigated by two-step cascade control strategies based on dissolved oxygen, ammonia and nitrite concentrations. An integrated MBR model which includes also the greenhouse gas formation/emission processes was applied. A substantial energy consumption reduction was obtained for the closed-loop scenarios (32% for Scenario 1 and 82% for Scenario 2). The air flow control based on both ammonia and nitrite concentrations within the aerobic reactor (Scenario 2) provided excellent results in terms of reduction of operating cost reduction (64%), direct (10%) and indirect (81%) emissions.

A plant-wide modelling comparison between membrane bioreactors and conventional activated sludge.

A comprehensive plant-wide mathematical modelling comparison between conventional activated sludge (CAS) and Membrane bioreactor (MBR) systems is presented. The main aim of this study is to highlight the key features of CAS and MBR in order to provide a guide for an effective plant operation. A scenario analysis was performed to investigate the influence on direct and indirect greenhouse gas (GHG) emissions and operating costs of (i) the composition of inflow wastewater (scenario 1), (ii) operating conditions (scenario 2) and (iii) oxygen transfer efficiency (scenario 3). Scenarios show higher indirect GHG emissions for MBR than CAS, which result is related to the higher energy consumption in MBR. The simultaneous variation of the investigated factors (scenario 4) exacerbates direct and indirect GHG emissions for both CAS and MBR. Indeed, during scenario 4 a maximum direct GHG emissions of 0.94 kgCO2eq m-3 and 1.56 kgCO2eq m-3 for CAS and MBR, respectively, was obtained.

Decision support systems (DSS) for wastewater treatment plants - A review of the state of the art.

The use of decision support systems (DSS) allows integrating all the issues related with sustainable development in view of providing a useful support to solve multi-scenario problems. In this work an extensive review on the DSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide an updated compendium on DSSs in view of supporting researchers and engineers on the selection of the most suitable method to address their management/operation/design problems. Results showed that DSSs were mostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective in order to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based on quality and operational issues are very often applied to site-specific conditions. Finally, it would be important to encourage the development of more user-friendly DSSs to increase general interest and usability.