Application of the Morris method for screening the influential parameters of fuzzy controllers applied to wastewater treatment plants

Environmental emissions and pollution characteristics of mosquitocides for the control of dengue fever in a typical urban area

Mosquitocides are frequently used to control the spread of dengue fever in tropical and sub-tropic urban regions worldwide, resulting in their discharge into the environment via rainfall runoff, causing adverse effects on ecological health. This study quantitatively evaluated mosquitocide emissions and environmental pollution in a typical urban district in China affected by the dengue fever epidemic, using a method combining market surveys, monitoring campaigns and SWMM (storm water management model) modelling tools. During the assessment period, the total mosquitocide usage in the urban district reached 6334 kg, with an estimated load of 56.55 g entering the receiving environment via rainfall runoff, 91.04 % of which occurred in the rainy season. Monitoring results indicated that the initial 0.5-1 h was the main period of mosquitocide wash off into the receiving water. Environmental mosquitocide pollution levels were found to be affected by the mosquitocide type and the time interval between mosquitocide application and precipitation events. The measured environmental concentrations of mosquitocides in this study were generally higher than those areas unaffected by the dengue fever epidemic. The modelled mosquitocide concentrations were in accordance with monitoring results. The finding of this study are important for assessing the environmental impact of dengue fever control activities, while also providing valuable baseline data for the effective environmental management of mosquitocides.

Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection

Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods' feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case's traffic emissions.

Global sensitivity and uncertainty analysis of a microalgae model for wastewater treatment

The results of a global sensitivity and uncertainty analysis of a microalgae model applied to a Membrane Photobioreactor (MPBR) pilot plant were assessed. The main goals of this study were: (I) to identify the sensitivity factors of the model through the Morris screening method, i.e. the most influential factors; (II) to calibrate the influential factors online or offline; and (III) to assess the model's uncertainty. Four experimental periods were evaluated, which encompassed a wide range of environmental and operational conditions. Eleven influential factors (e.g. maximum specific growth rate, light intensity and maximum temperature) were identified in the model from a set of 34 kinetic parameters (input factors). These influential factors were preferably calibrated offline and alternatively online. Offline/online calibration provided a unique set of model factor values that were used to match the model results with experimental data for the four experimental periods. A dynamic optimization of these influential factors was conducted, resulting in an enhanced set of values for each period. Model uncertainty was assessed using the uncertainty bands and three uncertainty indices: p-factor, r-factor and ARIL. Uncertainty was dependent on both the number of influential factors identified in each period and the model output analyzed (i.e. biomass, ammonium and phosphate concentration). The uncertainty results revealed a need to apply offline calibration methods to improve model performance.

Kinetic modeling of autotrophic microalgae mainline processes for sewage treatment in phosphorus-replete and -deplete culture conditions

A kinetic model of autotrophic microalgal growth in sewage was developed to determine the biokinetic processes involved, including carbon-, nitrogen- and phosphorus-limited microalgal growth, dependence on light intensity, temperature and pH, light attenuation and gas exchange to the atmosphere. A new feature was the differentiation between two metabolic pathways of phosphorus consumption according to the availability of extracellular phosphorus. Two scenarios were differentiated: phosphorus-replete and -deplete culture conditions. In the former, the microalgae absorbed phosphorus to grow and store polyphosphate. In the latter the microalgae used the stored polyphosphate as a phosphorus source for growth. Calibration and validation were performed with experimental data from a pilot-scale membrane photobioreactor (MPBR) fed with the permeate obtained from an anaerobic membrane bioreactor (AnMBR) pilot plant fed with real urban wastewater. 12 of the model parameters were calibrated. Despite the dynamics involved in the operating and environmental conditions, the model was able to reproduce the overall process performance with a single set of model parameters values. Four periods of different environmental and operational conditions were accurately simulated. Regarding the former, light and temperature ranged 10-406 μmol·m^-2·s^-1 and 19.7-32.1 °C, respectively. Concerning the later, the photobioreactors widths were 0.25 and 0.10 m, and the biomass and hydraulic retention times ranged 3-4.5 and 1.5-2.5 days, respectively. The validation of the model resulted in an overall correlation coefficient (R²) of 0.9954. The simulation results showed the potential of the model to predict the dynamics of the different components: the relative proportions of microalgae, nitrogen and phosphorus removal, polyphosphate storage and consumption, and soluble organic matter concentration, as well as the influence of environmental parameters on the microalgae's biokinetic processes. The proposed model could provide an effective tool for the industry to predict microalgae production and comply with the discharge limits in areas declared sensitive to eutrophication.

Application of SWMM 5.1 in flood simulation of sponge airport facilities

Construction of an airport runway makes the impervious area of the airport high, which leads to the deterioration of the water environment and frequent waterlogging disasters. The selection of sponge airport facilities (e.g., pump, multi-functional storage tanks, green roof) to mitigate airport flooding has been a crucial issue in China. This study aims to develop a conceptual rainwater-runoff simulation model, which can take into account the effects of such facilities of a sponge airport. Taking catchment N1 of Beijing Daxing Airport as a case study, SWMM 5.1 was implemented to develop three sponge airport models (one pump, two pumps, combination of pump and multi-functional storage tanks). A sensitivity analysis was carried out to guarantee the robustness of the developed models. A 1-hour rainfall scenario with a 5-year return period was employed on the three sponge airport models. The results showed that the effect rankings of the control strategies on the water depth, volume and peak inflow of catchment N1 were comparable - combined strategies (combination of pump and multi-functional storage tanks) > one pump and two pumps. The conceptual and hydrological models developed in this study can serve as a simulation tool for implementing a real-time rainwater drainage control system in Beijing Daxing Airport.

Mathematical modeling informs the impact of changes in circadian rhythms and meal patterns on insulin secretion

Disruption of circadian rhythms has been associated with metabolic syndromes, including obesity and diabetes. A variety of metabolic activities are under circadian modulation, as local and global clock gene knockouts result in glucose imbalance and increased risk of metabolic diseases. Insulin release from the pancreatic β cells exhibits daily variation, and recent studies have found that insulin secretion, not production, is under circadian modulation. As consideration of daily variation in insulin secretion is necessary to accurately describe glucose-stimulated insulin secretion, we describe a mathematical model that incorporates the circadian modulation via insulin granule trafficking. We use this model to understand the effect of oscillatory characteristics on insulin secretion at different times of the day. Furthermore, we integrate the dynamics of clock genes under the influence of competing environmental signals (light/dark cycle and feeding/fasting cycle) and demonstrate how circadian disruption and meal size distribution change the insulin secretion pattern over a 24-h day.

Methane oxidation in a biofilter (Part 1): Development of a mathematical model for designing and optimization

The aim of this work is the evaluation of the efficiency of such a biofilter, through the application of a mathematical model which describes the biological oxidation process. This mathematical model is able to predict the efficiency of the system under varying operating conditions. Literature data have been used in order to build the model. The factors that mostly affect the process and which actually regulate the entire process have been highlighted in this work. Specifically, it was found that temperature, flow and methane concentration are the most important parameters that influence the system. The results obtained from the mathematical model showed also that the biofilter system is simple to implement and manage and allows the achievement of high efficiency of methane oxidation. In the optimal conditions for temperature (between 20-30°C), residence time (between 0.7-0.8 h) and methane molar fraction (between 20-25%) the efficiency of methane oxidation could be around 50%.

Effects of influent fractionation, kinetics, stoichiometry and mass transfer on CH4, H2 and CO2 production for (plant-wide) modeling of anaerobic digesters

This paper examines the importance of influent fractionation, kinetic, stoichiometric and mass transfer parameter uncertainties when modeling biogas production in wastewater treatment plants. The anaerobic digestion model no. 1 implemented in the plant-wide context provided by the benchmark simulation model no. 2 is used to quantify the generation of CH₄, H₂and CO₂. A comprehensive global sensitivity analysis based on (i) standardized regression coefficients (SRC) and (ii) Morris' screening's (MS's) elementary effects reveals the set of parameters that influence the biogas production uncertainty the most. This analysis is repeated for (i) different temperature regimes and (ii) different solids retention times (SRTs) in the anaerobic digester. Results show that both SRC and MS are good measures of sensitivity unless the anaerobic digester is operating at low SRT and mesophilic conditions. In the latter situation, and due to the intrinsic nonlinearities of the system, SRC fails in decomposing the variance of the model predictions (R² < 0.7) making MS a more reliable method. At high SRT, influent fractionations are the most influential parameters for predictions of CH₄and CO₂emissions. Nevertheless, when the anaerobic digester volume is decreased (for the same load), the role of acetate degraders gains more importance under mesophilic conditions, while lipids and fatty acid metabolism is more influential under thermophilic conditions. The paper ends with a critical discussion of the results and their implications during model calibration and validation exercises.

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR)

The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance.

Variance-based sensitivity analysis for wastewater treatment plant modelling

Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models that characterise technical or natural systems. In the field of wastewater modelling, most of the recent applications of GSA use either regression-based methods, which require close to linear relationships between the model outputs and model factors, or screening methods, which only yield qualitative results. However, due to the characteristics of membrane bioreactors (MBR) (non-linear kinetics, complexity, etc.) there is an interest to adequately quantify the effects of non-linearity and interactions. This can be achieved with variance-based sensitivity analysis methods. In this paper, the Extended Fourier Amplitude Sensitivity Testing (Extended-FAST) method is applied to an integrated activated sludge model (ASM2d) for an MBR system including microbial product formation and physical separation processes. Twenty-one model outputs located throughout the different sections of the bioreactor and 79 model factors are considered. Significant interactions among the model factors are found. Contrary to previous GSA studies for ASM models, we find the relationship between variables and factors to be non-linear and non-additive. By analysing the pattern of the variance decomposition along the plant, the model factors having the highest variance contributions were identified. This study demonstrates the usefulness of variance-based methods in membrane bioreactor modelling where, due to the presence of membranes and different operating conditions than those typically found in conventional activated sludge systems, several highly non-linear effects are present. Further, the obtained results highlight the relevant role played by the modelling approach for MBR taking into account simultaneously biological and physical processes.

Comparison of sensitivity analysis methods for pollutant degradation modelling: a case study from drinking water treatment

Five sensitivity analysis methods based on derivatives, screening, regression, variance decomposition and entropy are introduced, applied and compared for a model predicting micropollutant degradation in drinking water treatment. The sensitivity analysis objectives considered are factors prioritisation (detecting important factors), factors fixing (detecting non-influential factors) and factors mapping (detecting which factors are responsible for causing pollutant limit exceedances). It is shown how the applicability of methods changes in view of increasing interactions between model factors and increasing non-linearity between the model output and the model factors. A high correlation is observed between the indices obtained for the objectives factors prioritisation and factors mapping due to the positive skewness of the probability distributions of the predicted residual pollutant concentrations. The entropy-based method which uses the Kullback-Leibler divergence is found to be particularly suited when assessing pollutant limit exceedances.