1
|
Borobio-Castillo RA, Cabrera-Miranda JM, Corona-Vásquez B. Metamodeling-based reliability analysis framework for activated sludge processes. Water Res 2024; 255:121436. [PMID: 38503185 DOI: 10.1016/j.watres.2024.121436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/07/2024] [Accepted: 03/06/2024] [Indexed: 03/21/2024]
Abstract
The reliability of activated sludge processes will be adversely affected by alterations in wastewater production and pollutant loading foreseen due to population growth, urbanization, and climate change, as well as the tendency to amend environmental regulations to mandate stricter effluent quality standards to alleviate water pollution. Until now, there was no framework capable of effectively managing these multifaceted challenges in reliability analysis. Previous attempts conducted a low number of simulations leading to insufficient statistical significance to properly validate reliability quantification. A metamodeling-based reliability analysis framework for the activated sludge process is introduced to cope with alterations in wastewater production and pollutant loading, assesses the reliability under different effluent regulations, and leverages metamodels to conduct extensive simulation work, to estimate the reliability. All metamodels produced high-resolution results, enabling reliability estimation after 100 000 simulations. The framework effectively assessed the annual failure rates of various activated sludge facility designs under four regulations, demonstrating the impact of stricter effluent quality standards. Integrating metamodels for reliability analysis greatly lowers computational costs, making the framework a time and resource-efficient choice for quick decision-making in facility design.
Collapse
Affiliation(s)
- R A Borobio-Castillo
- Department of Civil and Environmental Engineering, Universidad de las Américas Puebla, Ex-Hacienda Santa Catarina Mártir S/N, San Andrés Cholula, Puebla 72810, México
| | - J M Cabrera-Miranda
- Department of Civil and Environmental Engineering, Universidad de las Américas Puebla, Ex-Hacienda Santa Catarina Mártir S/N, San Andrés Cholula, Puebla 72810, México
| | - B Corona-Vásquez
- Department of Civil and Environmental Engineering, Universidad de las Américas Puebla, Ex-Hacienda Santa Catarina Mártir S/N, San Andrés Cholula, Puebla 72810, México.
| |
Collapse
|
2
|
Berthelson PR, Ghassemi P, Wood JW, Stubblefield GG, Al-Graitti AJ, Jones MD, Horstemeyer MF, Chowdhury S, Prabhu RK. A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions. Med Biol Eng Comput 2021; 59:1065-1079. [PMID: 33881704 DOI: 10.1007/s11517-021-02349-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 03/17/2021] [Indexed: 11/26/2022]
Abstract
A finite element (FE)-guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body's response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC15) trends were predicted from Kriging models across varied impact velocities (10-45 mph; 16.1-72.4 km/h), locations (near side, far side, front, and rear), and angles (-45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC15 increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC15 under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously. This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10-45 mph), locations, and angles (-45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.
Collapse
Affiliation(s)
- P R Berthelson
- Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
| | - P Ghassemi
- Department of Mechanical and Aerospace Engineering, University at Buffalo, 246 Bell Hall, Buffalo, NY, 14260, USA
| | - J W Wood
- Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
| | - G G Stubblefield
- Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA
- Department of Mechanical Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
| | - A J Al-Graitti
- School of Engineering, Cardiff University, Cardiff, Wales, CF10 3AT, UK
| | - M D Jones
- School of Engineering, Cardiff University, Cardiff, Wales, CF10 3AT, UK
| | - M F Horstemeyer
- Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA
- Department of Mechanical Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
| | - S Chowdhury
- Department of Mechanical and Aerospace Engineering, University at Buffalo, 246 Bell Hall, Buffalo, NY, 14260, USA.
| | - R K Prabhu
- Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
| |
Collapse
|
3
|
Ramanantenasoa MMJ, Génermont S, Gilliot JM, Bedos C, Makowski D. Meta-modeling methods for estimating ammonia volatilization from nitrogen fertilizer and manure applications. J Environ Manage 2019; 236:195-205. [PMID: 30731243 DOI: 10.1016/j.jenvman.2019.01.066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 10/22/2018] [Accepted: 01/20/2019] [Indexed: 06/09/2023]
Abstract
Accurate estimations of ammonia (NH3) emissions due to nitrogen (N) fertilization are required to identify efficient mitigation techniques and improve agricultural practices. Process-based models such as Volt'Air can be used for this purpose because they incorporate the effects of several key factors influencing NH3 volatilization at fine spatio-temporal resolutions. However, these models require a large number of input variables and their implementation on a large scale requires long computation times that may restrict their use by public environmental agencies. In this study, we assess the capabilities of various types of meta-models to emulate the complex process-based Volt'Air for estimating NH3 emission rates from N fertilizer and manure applications. Meta-models were developed for three types of fertilizer (N solution, cattle farmyard manure, and pig slurry) for four major agricultural French regions (Bretagne, Champagne-Ardenne, Ile-de-France, and Rhône-Alpes) and at the national (France) scale. The meta-models were developed from 106,092 NH3 emissions simulated by Volt'Air in France. Their performances were evaluated by cross-validation, and the meta-models providing the best approximation of the original model were selected. The results showed that random forest and ordinary linear regression models were more accurate than generalized additive models, partial least squares regressions, and least absolute shrinkage and selection operator regressions. Better approximations of Volt'Air simulations were obtained for cattle farmyard manure (3% < relative root mean square error of prediction (RRMSEP) < 8%) than for pig slurry (17% < RRMSEP < 19%) and N solution (21% < RRMSEP < 40%). The selected meta-models included between 6 and 15 input variables related to weather conditions, soil properties and cultural practices. Because of their simplicity and their short computation time, our meta-models offer a promising alternative to process-based models for NH3 emission inventories at both regional and national scales. Our approach could be implemented to emulate other process-based models in other countries.
Collapse
Affiliation(s)
- Maharavo Marie Julie Ramanantenasoa
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Route de la Ferme, 78850, Thiverval Grignon, France; ADEME, 20, avenue du Grésillé, BP 90406, 49004, Angers Cedex 01, France
| | - Sophie Génermont
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Route de la Ferme, 78850, Thiverval Grignon, France.
| | - Jean-Marc Gilliot
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Route de la Ferme, 78850, Thiverval Grignon, France
| | - Carole Bedos
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Route de la Ferme, 78850, Thiverval Grignon, France
| | - David Makowski
- UMR Agronomie, INRA, AgroParisTech, Université Paris-Saclay, Avenue Lucien Brétignières, 78850, Thiverval Grignon, France; CIRED, 45bis Avenue de la Belle Gabrielle, 94130 Nogent-sur-Marne, France
| |
Collapse
|
4
|
Mathew S, Bartels J, Banerjee I, Vodovotz Y. Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses. J Theor Biol 2014; 358:132-48. [PMID: 24909493 DOI: 10.1016/j.jtbi.2014.05.036] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2014] [Revised: 05/22/2014] [Accepted: 05/23/2014] [Indexed: 01/09/2023]
Abstract
The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses from IL-10 and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.
Collapse
Affiliation(s)
- Shibin Mathew
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | | | - Ipsita Banerjee
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15219, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Yoram Vodovotz
- Immunetrics, Inc., Pittsburgh, PA 15203, USA; Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA.
| |
Collapse
|