scholarly journals Sensitivity analysis on a three-phase plant-wide water and resource recovery facility model for identification of significant parameters

Water SA ◽  
2020 ◽  
Vol 46 (3 July) ◽  
Author(s):  
DS Ikumi
Keyword(s):  
Sensitivity Analysis ◽  
Mathematical Models ◽  
Screening Method ◽  
Resource Recovery ◽  
Future Application ◽  
Model Output ◽  
Widespread Application ◽  
Model Input ◽  
Three Phase ◽  
Decision Making Processes

Water and resource recovery facility (WRRF) mathematical models have been advancing towards their widespread application for sizing and operation of treatment plants to minimize energy consumption and cost while maximizing nutrient recovery and effluent quality. Effective utilisation of these models requires that they are well calibrated. However, difficulties (with important parameters not identified and uncertainties in interpretation of model output results) can be experienced in model calibration, especially due to (i) the intricate relationships of model output variables with model input factors (where parameters are inter-related to various model outputs), resulting in non-linearity, and (ii) the limitations (due to expensive and/or time-consuming experimental methods) experienced in procuring and reconciling data required for determination of the model input factors. This paper presents the performance of a sensitivity analysis, reinforced with expert-based reasoning, on a three-phase (aqueous-gas-solid) plant-wide model (PWM_SA, Ikumi et al., 2015), for identification of significant parameters, and highlights the ones requiring experimental determination, specific to the system. The sensitivity analysis exercise was performed using two methods – i.e., Morris screening (screening method) and standardised regression coefficient (SRC; based on regression). This process was useful towards detection of the parameters, which are not normally measured at WRRFs, but may require attention for future application of mathematical models in decision-making processes for WRRFs. These included the influent fractions of unbiodegradable and readily biodegradable organics, the kinetic constants for hydrolysis of biodegradable particulates, the elemental composition of the organics and the specific growth rate of autotrophic nitrifying biomass.

Improving Casthouse Throughput Using Discrete-Event Modelling

2011 ◽  
Vol 693 ◽  
pp. 3-9 ◽  
Author(s):  
Bruce Gunn ◽  
Yakov Frayman
Keyword(s):  
Sensitivity Analysis ◽  
Discrete Event ◽  
Casting Machine ◽  
Production Data ◽  
Model Output ◽  
Arrival Times ◽  
Event Simulation ◽  
Modelling System ◽  
Key Parameter

The scheduling of metal to different casters in a casthouse is a complicated problem, attempting to find the balance between pot-line, crucible carrier, furnace and casting machine capacity. In this paper, a description will be given of a casthouse modelling system designed to test different scenarios for casthouse design and operation. Using discrete-event simulation, the casthouse model incorporates variable arrival times of metal carriers, crucible movements, caster operation and furnace conditions. Each part of the system is individually modelled and synchronised using a series of signals or semaphores. In addition, an easy to operate user interface allows for the modification of key parameters, and analysis of model output. Results from the model will be presented for a case study, which highlights the effect different parameters have on overall casthouse performance. The case study uses past production data from a casthouse to validate the model outputs, with the aim to perform a sensitivity analysis on the overall system. Along with metal preparation times and caster strip-down/setup, the temperature evolution within the furnaces is one key parameter in determining casthouse performance.

scholarly journals The effects of flooding and weather conditions on leptospirosis transmission in Thailand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Karine Chalvet-Monfray ◽  
Anuwat Wiratsudakul ◽  
Charin Modchang
Keyword(s):  
Sensitivity Analysis ◽  
Public Education ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Weather Conditions ◽  
Transmission Dynamics ◽  
Multiplication Rate ◽  
High Degree ◽  
Transmission Models

AbstractThe epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to prevent Leptospira infections.

scholarly journals Measuring Performance in Transportation Companies in Developing Countries: A Novel Rough ARAS Model

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 434 ◽  
Author(s):  
Dunja Radović ◽  
Željko Stević ◽  
Dragan Pamučar ◽  
Edmundas Zavadskas ◽  
Ibrahim Badi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Developing Countries ◽  
Performance Indicators ◽  
Three Phase ◽  
Business Efficiency ◽  
Success Indicators ◽  
Product Assessment ◽  
Simple Additive Weighting ◽  
Five Phases ◽  
Spearman’S Correlation

The success of any business depends fundamentally on the possibility of balancing (symmetry) needs and their satisfaction, that is, the ability to properly define a set of success indicators. It is necessary to continuously monitor and measure the indicators that have the greatest impact on the achievement of previously set goals. Regarding transportation companies, the rationalization of transportation activities and processes plays an important role in ensuring business efficiency. Therefore, in this paper, a model for evaluating performance indicators has been developed and implemented in three different countries: Bosnia and Herzegovina, Libya and Serbia. The model consists of five phases, of which the greatest contribution is the development of a novel rough additive ratio assessment (ARAS) approach for evaluating measured performance indicators in transportation companies. The evaluation was carried out in the territories of the aforementioned countries in a total of nine companies that were evaluated on the basis of 20 performance indicators. The results obtained were verified throughout a three-phase procedure of a sensitivity analysis. The significance of the performance indicators was simulated throughout the formation of 10 scenarios in the sensitivity analysis. In addition, the following approaches were applied: rough WASPAS (weighted aggregated sum product assessment), rough SAW (simple additive weighting), rough MABAC (multi-attributive border approximation area comparison) and rough EDAS (evaluation based on distance from average solution), which showed high correlation of ranks by applying Spearman's correlation coefficient (SCC).

scholarly journals Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

2013 ◽  
Vol 17 (2) ◽  
pp. 461-478 ◽  
Author(s):  
L. Loosvelt ◽  
H. Vernieuwe ◽  
V. R. N. Pauwels ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Sensitivity Analysis ◽  
Soil Texture ◽  
Compositional Data ◽  
High Sensitivity ◽  
Hydrologic Model ◽  
Model Output ◽  
Local Sensitivity ◽  
Hydrologic Modelling ◽  
Compositional Model ◽  
Additional Data Collection

Abstract. Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

scholarly journals Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

2011 ◽  
Vol 8 (2) ◽  
pp. 2555-2608 ◽  
Author(s):  
E. H. Sutanudjaja ◽  
L. P. H. van Beek ◽  
S. M. de Jong ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Modeling ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Groundwater Model ◽  
Model Output ◽  
Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

scholarly journals MATHEMATICAL MODELLING AND STATISTICAL ANALYSIS IN POST - COVID-19 TOURISM RECOVERY

2021 ◽  
Author(s):  
Marija Paunovic ◽  
Keyword(s):  
Statistical Analysis ◽  
Mathematical Modelling ◽  
Mathematical Models ◽  
Mitigation Measures ◽  
Impact Factors ◽  
Widespread Application ◽  
Recovery Programs ◽  
Tourism Sector ◽  
Travel Restrictions

The COVID-19 pandemic, along with responses to pandemic control in the form of a national blockade with widespread application of travel restrictions, has made tourism one of the most vulnerable sectors. With increasing mitigation measures in many countries regarding tourism and travel restrictions in some segments, different tourism recovery programs are being launched according to the specifics of each country. The paper will consider various proposed measures through different impact factors set in order to accelerate the recovery of the tourism sector. Selected mathematical models will be presented and discussed.

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