Applying global sensitivity analysis to the modelling of flow and water quality in sewers

Global Sensitivity Analysis of Factors Influencing the Surface Temperature of Mold during Autoclave Processing

During the process of forming carbon fiber reinforced plastics (CFRP) in an autoclave, deeply understanding the global sensitivity of factors influencing mold surface temperature is of paramount importance for optimizing large frame-type mold thermally and enhancing curing quality. In this study, the convective heat transfer coefficient (CHTC), the thickness of composite laminates (TCL), the thickness of mold facesheet (TMF), the mold material type (MMT), and the thickness of the auxiliary materials layer (TAL) have been quantitatively assessed for the effects on the mold surface temperature. This assessment was conducted by building the thermal-chemical curing model of composite laminates and utilizing the Sobol global sensitivity analysis (GSA) method. Additionally, the interactions among these factors were investigated to gain a comprehensive understanding of their combined effects. The results show that the sensitivity order of these factors is as follows: CHTC > MMT > TMF > TCL > TAL. Moreover, CHTC, MMT, and TMF are the main factors influencing mold surface temperature, as the sum of their first-order sensitivity indices accounts for over 97.3%. The influence of a single factor is more significant than that of the interaction between factors since the sum of the first-order sensitivity indices of the factors is more than 78.1%. This study will support the development of science-based guidelines for the thermal design of molds and associated heating equipment design.

Sensitivity Analysis of a Groundwater Infiltration Model and Sea-Level Rise Applications for Coastal Sewers

Groundwater elevations in coastal cities will be affected by climate-change-induced sea level rise (SLR) and wastewater collection systems will experience increased groundwater infiltration (GWI) due to greater submergence of sewer pipes. Commercial sewer hydraulics models consider GWI to be a constant quantity estimated via a low-flow monitoring campaign and are incapable of predicting future flows due to changes in GW elevations. A global sensitivity analyses conducted for a two-dimensional GWI pipe flow model found the most important input parameters are groundwater head and surrounding soil hydraulic conductivity. Two case studies were conducted considering a range of pipe defect severity to estimate increases in GWI associated with predictions of future SLR. The findings are that SLR will begin to have noticeable impacts in terms of increased average dry weather flow (ADWF) as soon as 2030 (3–10%) and will increase dramatically in the future (10–29% by 2050, and 50% or more by 2100). Daily and seasonal tide ranges affect the normal diurnal flow variations by between 3% and 10%. The estimation methodology and case studies described here illustrate the coming future importance of SLR effects on GWI in coastal collection systems that should be included in facilities planning and design.

Global and local sensitivity analysis to improve the understanding of physically-based urban wash-off models from high-resolution laboratory experiments

Physically-based urban wash-off models are a promising means of studying the transport of finer suspended solids and their associated pollutants during rain events, considering spatial and temporal heterogeneities. This study contributes to the understanding of these models through an in-depth sensitivity analysis to provide the necessary information to simplify the model and deal with parameter identifiability. First, based on twelve tailored high-resolution experiments, the accurate measurement of input variables was used to study the parameters of the Hairsine-Rose sediment transport model through a global sensitivity analysis. Using Standardized Regression Coefficients (SRC) and Extended Fourier Amplitude Sensitivity Test (EFAST) methods, the analysis showed that both the total washed-off mass and the TSS peaks concentration are highly sensitive to the critical mass, which considers the reduction in the detachment of particles when the sediment available decreases and is scattered over the surface. In addition, the rain- and flow-driven detachment parameters were presented as key for smaller and larger sediment particles, respectively. Then, those uncertainties that are associated in field studies with the determination of the model input variables were also considered by conducting a local sensitivity analysis. The initial load of sediment and the mean grain size were seen to be the most important variables, thus underlining the need for very accurate measurements here. Moreover, a precise definition of Harsine-Rose parameters is also necessary to achieve reliable results in order to work on treatment and management techniques to minimize the impact of urban surface contaminants on urban environments.

Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts

Dissolved oxygen within water column is a key variable to characterize the water quality. Water quality modeling has been extensively developed for decades. However, complex biogeochemical cycles are described using a high number of parameters. Hence, parameters' uncertainty constitutes a major problem in the application of these models. Sensitivity analysis allows the identification of the most influential parameters in a model and a better understanding of the governing processes. This paper presents a time-dependent sensitivity analysis for dissolved oxygen using Morris and Sobol methods combined with a functional principal components analysis for dimension reduction. The aim of this study is to identify the most important parameters of C-RIVE model in different trophic contexts and to understand the biogeochemical functioning of river systems. The results indicate that the maintenance respiration of phytoplankton and the photosynthetic parameters (i.e. photosynthetic capacity, the maximal photosynthesis rate and light extinction coefficients) are the most influential parameters during algal blooms. When the river system becomes heterotrophic, the bacterial activities (moderate and high temperature) and the reaeration coefficients (low temperature) affect the most the dissolved oxygen concentration in the water column. An anthropogenic effect (ship navigation) on variation of dissolved oxygen concentration has been identified and the role of this anthropogenic effect evolves with hydrological and trophic conditions.

Distribution-Based Calibration of a Stormwater Quality Model

Stormwater quality models are usually calibrated using observed pollutographs. As current models still rely on simplified model concepts for pollutant accumulation and wash-off, calibration results for continuous pollutant concentrations are highly uncertain. In this paper, we introduce an innovative calibration approach based on total suspended solids (TSS) event load distribution. The approach is applied on stormwater quality models for a flat roof and a parking lot for which reliable distributions are available. Exponential functions are employed for both TSS buildup and wash-off. Model parameters are calibrated by means of an evolutionary algorithm to minimize the distance between a parameterized lognormal distribution function and the cumulated distribution of simulated TSS event loads. Since TSS event load characteristics are probabilistically considered, the approach especially respects the stochasticity of TSS buildup and wash-off and, therefore, improves conventional stormwater quality calibration concepts. The results show that both experimental models were calibrated with high goodness-of-fit (Kolmogorov–Smirnov test statistic: 0.05). However, it is shown that events with high TSS event loads (>0.8 percentile) are generally underestimated. While this leads to a relative deviation of −28% of total TSS loads for the parking lot, the error is compensated for the flat roof (+5%). Calibrated model parameters generally tend to generate wash-off proportional to runoff, which is indicated by mass-volume curves. The approach itself is, in general, applicable and creates a new opportunity to calibrate stormwater quality models especially when calibration data is limited.

Evaluation of robustness of activated sludge using calcium-induced enhancement of respiration

Robustness of an activated sludge system, describing uncertainty and operational risk, was evaluated using the absence or presence of calcium-induced enhancement of respiration (CaER) effect. Generally, the fast-growing system was susceptible to external environmental variations, of which the sludge exhibited significant CaER effect under normal operational conditions, while the slow growing system showed less significant CaER effect. However, sludge in both systems exhibited CaER effect under stressed conditions of decreasing temperature or ammonia shocking. Therefore, the absence of CaER effect indicates a more robust system, while the presence of CaER effect indicates a susceptible system. Additionally, a method to identify safe and dangerous shocking was established by a hybrid usage of absence or presence of CaER effect and recovery index (RI) curve type. The evaluation of robustness could help determining when adjustment should be really taken to cope with the uncertainty, and thus holds a high promise for field application.

Modeling washoff of total suspended solids in the tropics

Washoff behavior in the tropics is expected to behave differently from temperate areas due to differences in rainfall characteristics. In this study, rainfall, runoff and total suspended solids (TSS) were monitored from 9 catchments distinguished by different types of land use, in Singapore. The catchments ranged in size from 5.7ha to 85.2ha. Over 120 rain events were studied and more than 1000 storm samples were collected and analyzed. Monte Carlo analysis was applied to obtain the best fit values of the washoff model parameters consisting the washoff coefficient c₃, washoff exponent c₄ and initial mass on surface B_ini. The exponent c₄ was found to be approximately unity for all the events monitored, in agreement with other studies. The values of c₃ and B_ini were found to vary between events. Among all the rainfall and runoff characteristics studied, rainfall depth of the current event (d) was found to be the single parameter that significantly influenced the values of c₃ and B_ini. Contrary to expectations, B_ini did not correlate well with antecedent dry period or with rainfall depth of the prior storm event. The results show that the common modeling practice where B_ini is assumed to vary with antecedent dry period and previous rainfall depth should be reassessed when applied to catchments in the tropics. ANCOVA analysis showed that land use was not significant, but rather the variation of c₃ and B_ini with d was found to correlate well with the catchment area.

Parameter estimation of hydrologic models using a likelihood function for censored and binary observations

Observations of a hydrologic system response are needed to accurately model system behaviour. Nevertheless, often very few monitoring stations are operated because collecting such reference data adequately and accurately is laborious and costly. It has been recently suggested to use observations not only from dedicated flow meters but also from simpler sensors, such as level or event detectors, which are available more frequently but only provide censored information. Binary observations can be considered as extreme censoring. It is still unclear, however, how to use censored observations most effectively to learn about model parameters. To this end, we suggest a formal likelihood function that incorporates censored observations, while accounting for model structure deficits and uncertainty in input data. Using this likelihood function, the parameter inference is performed within the Bayesian framework. We demonstrate the implementation of our methodology on a case study of an urban catchment, where we estimate the parameters of a hydrodynamic rainfall-runoff model from binary observations of combined sewer overflows. Our results show, first, that censored observations make it possible to learn about model parameters, with an average decrease of 45% in parameter standard deviation from prior to posterior. Second, the inference substantially improves model predictions, providing higher Nash-Sutcliffe efficiency. Third, the gain in information largely depends on the experimental design, i.e. sensor placement. Given the advent of Internet of Things, we foresee that the plethora of censored data promised to be available can be used for parameter estimation within a formal Bayesian framework.

Modelling the effects of water diversion and combined sewer overflow on urban inland river quality

In order to assist and optimize the operation of a clean water diversion project for the medium-sized inland rivers in Chaohu, China, an integrated hydrodynamic and water quality model was used in this study. Sixteen diversion scenarios and five sewage interception scenarios were defined to assess the improvement of water quality parameters including ammonia nitrogen (NH₃-N), total phosphorus (TP) and chemical oxygen demand (COD) under different diverted water flows, diverting times, diverting points, diverting routines and sewage interception proportions. An index of pollutant removal rate per unit diverted water flow (PRUWF) was proposed to evaluate the effect of the clean water diversion. Results show that operating conditions played important roles in water quality improvement of medium-sized inland rivers. The optimal clean water diversion was operated under the conditions of a flow rate of 5 m³/s for 48 h with an additional constructed bridge sluice. A global sensitivity analysis using the Latin Hypercube One-Factor-at-a-Time (LH-OAT) method was conducted to distinguish the contributions of various driving forces to inland river water restoration. Results show that sewage interception was more important than diverted water flow and diverting time with respect to water quality improvement, especially for COD.

Development and testing of a fast conceptual river water quality model

Modern, model based river quality management strongly relies on river water quality models to simulate the temporal and spatial evolution of pollutant concentrations in the water body. Such models are typically constructed by extending detailed hydrodynamic models with a component describing the advection-diffusion and water quality transformation processes in a detailed, physically based way. This approach is too computational time demanding, especially when simulating long time periods that are needed for statistical analysis of the results or when model sensitivity analysis, calibration and validation require a large number of model runs. To overcome this problem, a structure identification method to set up a conceptual river water quality model has been developed. Instead of calculating the water quality concentrations at each water level and discharge node, the river branch is divided into conceptual reservoirs based on user information such as location of interest and boundary inputs. These reservoirs are modelled as Plug Flow Reactor (PFR) and Continuously Stirred Tank Reactor (CSTR) to describe advection and diffusion processes. The same water quality transformation processes as in the detailed models are considered but with adjusted residence times based on the hydrodynamic simulation results and calibrated to the detailed water quality simulation results. The developed approach allows for a much faster calculation time (factor 10⁵) without significant loss of accuracy, making it feasible to perform time demanding scenario runs.

Modelling real-time control of WWTP influent flow under data scarcity

In order to comply with effluent standards, wastewater operators need to avoid hydraulic overloading of the wastewater treatment plant (WWTP), as this can result in the washout of activated sludge from secondary settling tanks. Hydraulic overloading can occur in a systematic way, for instance when sewer network connections are extended without increasing the WWTP's capacity accordingly. This study demonstrates the use of rule-based real-time control (RTC) to reduce the load to the WWTP while restricting the overall overflow volume of the sewer system to a minimum. Further, it shows the added value of RTC despite the limited availability of monitoring data and information on the catchment through a parsimonious simulation approach, using relocation of spatial system boundaries and creating required input data through reverse modelling. Focus was hereby on the accurate modelling of pump hydraulics and control. Finally, two different methods of global sensitivity analysis were employed to verify the influence of parameters of both the model and the implemented control algorithm. Both methods show the importance of good knowledge of the system properties, but that monitoring errors play a minor role.

Uncertainty assessment of water quality modeling for a small-scale urban catchment using the GLUE methodology: a case study in Shanghai, China

There is often great uncertainty in water quality modeling for urban drainage systems because water quality variation in systems is complex and affected by many factors. The stormwater management model (SWMM) was applied to a small-scale urban catchment with a simple and well-maintained stormwater drainage system without illicit connections. This was done to assess uncertainty in build-up and wash-off modeling of pollutants within the generalized likelihood uncertainty estimation (GLUE) methodology, based on a well-calibrated water quantity model. The results indicated great uncertainty of water quality modeling within the GLUE methodology. Comparison of uncertainties in various pollutant build-up and wash-off models that were available in SWMM indicated that those uncertainties varied slightly. This may be a consequence of the specific characteristics of rainfall events and experimental sites used in the study. The uncertainty analysis of water quality parameters in SWMM is conducive to effectively evaluating model reliability, and provides an experience base for similar research and applications.