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12. Dynamic Time Warping as Elementary Effects Metric for Morris-Based Global Sensitivity Analysis of High-Dimension Dynamical Models.

Author

Fortela, Dhan Lord B., Mikolajczyk, Ashley P., Hernandez, Rafael, Revellame, Emmanuel, Sharp, Wayne, Holmes, William, Gang, Daniel, and Zappi, Mark E.

Subjects
COST functions, ORDINARY differential equations, DYNAMICAL systems, ADSORPTION kinetics, DECOMPOSITION method
Abstract

This work focused on demonstrating the use of dynamic time warping (DTW) as a metric for the elementary effects computation in Morris-based global sensitivity analysis (GSA) of model parameters in multivariate dynamical systems. One of the challenges of GSA on multivariate time-dependent dynamics is the modeling of parameter perturbation effects propagated to all model outputs while capturing time-dependent patterns. The study establishes and demonstrates the use of DTW as a metric of elementary effects across the time domain and the multivariate output domain, which are all aggregated together via the DTW cost function into a single metric value. Unlike the commonly studied coefficient-based functional approximation and covariance decomposition methods, this new DTW-based Morris GSA algorithm implements curve alignment via dynamic programing for cost computation in every parameter perturbation trajectory, which captures the essence of "elementary effect" in the original Morris formulation. This new algorithm eliminates approximations and assumptions about the model outputs while achieving the objective of capturing perturbations across time and the array of model outputs. The technique was demonstrated using an ordinary differential equation (ODE) system of mixed-order adsorption kinetics, Monod-type microbial kinetics, and the Lorenz attractor for chaotic solutions. DTW as a Morris-based GSA metric enables the modeling of parameter sensitivity effects on the entire array of model output variables evolving in the time domain, resulting in parameter rankings attributed to the entire model dynamics. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Methyl Mercaptan Removal from Methane Using Metal-Oxides and Aluminosilicate Materials.

Author

Martinez-Zuniga, Gerson, Antwi, Samuel, Soni-Castro, Percival, Olayiwola, Olatunji, Chuprin, Maksym, Holmes, William E., Buchireddy, Prashanth, Gang, Daniel, Revellame, Emmanuel, Zappi, Mark E., and Hernandez, Rafael

Subjects
ALUMINUM catalysts, X-ray photoelectron spectroscopy, METALLIC oxides, ZINC oxide, PIPELINE corrosion, ALUMINUM silicates
Abstract

Methyl mercaptan is a sulfur-based chemical found as a co-product in produced natural gas and it causes corrosion in pipelines, storage tanks, catalysts, and solid adsorption beds. To improve the quality of methane produced, researchers have studied the use of metal oxides and aluminum silicates as catalysts for removing mercaptan. However, there are restrictive limitations on the efficiency of metal oxides or aluminum silicates as adsorbents for this application. Therefore, this study investigated the performance of these materials in a fixed-bed reactor with simulated natural gas streams under various operating conditions. The testing procedure includes a detailed assessment of the adsorbent/catalysts by several techniques, such as Braeuer–Emmett–Teller (BET), Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectrometry (EDS), and X-ray Photoelectron Spectroscopy. The results revealed that metal oxides such as copper, manganese, and zinc performed well in methyl mercaptan elimination. The addition of manganese, copper, and zinc oxides to the aluminum silicate surface resulted in a sulfur capacity of 1226 mg S/g of catalyst. These findings provide critical insights for the development of catalysts that combine metal oxides to increase adsorption while reducing the production of byproducts like dimethyl sulfide (DMS) and dimethyl disulfide (DMDS) during methyl mercaptan removal. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Physical and Chemical Characterization of Sustainable Green Adhesives Derived from Municipal Treatment Plant Sludges.

Author

Zeringue, Chelsea Trahan, Chirdon, William M., Sharp, Wayne, Gang, Daniel, Khattab, Ahmed, Hernandez, Rafael, Holmes, William, and Zappi, Mark E.

Abstract

Adhesive formulations derived from sustainable feedstocks, like waste-activated sludge and biosolids from wastewater treatment plants, are developed due to protein-based adhesives receiving attention for their low-cost, resourcefulness, and minimal ecological footprint. The protein composition and associated dynamic changes of the adhesive formulations were studied via gel permeation chromatography, which detailed a molecular size distribution of 8.72 × 105 g/mol for the adhesive formulation and 6.89 × 103 g/mol for the dewatered biosolid base fraction, which confirms the formation of multiple protein functional groups combining to form the larger adhesive molecules. Further analysis determined the types of proteins present in the dewatered biosolids as glutelin, prolamin, globulin, and albumin proteins, with the glutelin proteins as the most prevalent, as thus likely responsible for adhesive formation. The rheological properties of the novel protein adhesive were also studied to interpret the structure of the adhesives, which detailed the findings of viscoelastic properties and flow behaviors of each adhesive in relation to the wastewater treatment plant sample location, which yielded higher flow points, storage moduli, and loss moduli for the dewatered biosolids in comparison to the waste-activated sludge and biosolid adhesives, which correlates with the higher solids content of the dewatered biosolids and potentially cell rupturing when exposed to filtration stress. [ABSTRACT FROM AUTHOR]

Published
2024
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23. FeS2 deposited on 3D-printed carbon microlattices as free-standing electrodes for lithium-ion batteries.

Author

Romero, Cameron, Liu, Zhi, Gordon, Kenneth, Lei, Xiaobo, Joseph, Karius, Broussard, Emily, Gang, Daniel, Wei, Zhen, and Fei, Ling

Subjects
LITHIUM-ion batteries, CLEAN energy, THREE-dimensional printing, ELECTRODES, CHARGE exchange, COMPUTER-aided design, ELECTRIC batteries
Abstract

We introduce free-standing FeS2/carbon microlattice composites as electrodes for lithium-ion batteries through 3D printing. The computer-aided design allows for any shape. The microlattice features aligned microchannels, promoting ion transfer, while the carbon skeleton facilitates electron transfer. Overall, this study shows 3D printing is highly promising in advancing sustainable energy applications. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Nonpoint source pollution

Author

Lian, Qiyu, Yao, Lunguang, Ahmad, Zaki Uddin, Lei, Xiaobo, Islam, Fahrin, Zappi, Mark E., and Gang, Daniel Dianchen

Published
2019

38. Evaluating the Suitability of Linear and Nonlinear Regression Approaches for the Langmuir Adsorption Model as Applied toward Biomass-Based Adsorbents: Testing Residuals and Assessing Model Validity

Author

Mikolajczyk, Ashley P., Fortela, Dhan Lord B., Berry, J. Calvin, Chirdon, William M., Hernandez, Rafael A., Gang, Daniel D., and Zappi, Mark E.

Abstract

Regression analysis is a powerful tool in adsorption studies. Researchers often favor linear regression for its simplicity when fitting isotherm models, such as the Langmuir equation. Validating regression assumptions is crucial to ensure that the model accurately represents the data and allows appropriate inferences. This study provides a detailed examination of assumption checking in the context of adsorption studies while simultaneously evaluating the robustness of linear regression methods for fitting the Langmuir equation to isotherm data from 2,4-dichlorophenol (DCP) adsorption onto various biomass-based adsorbents and activated carbon. Different linearized Langmuir equations (Hanes–Woolf, Lineweaver–Burk, Eadie–Hofstee, and Scatchard) were compared to nonlinear regression, and each method was validated by rigorous residual checking. This included visual plots of residuals as well as statistical tests, including the Durbin–Watson test for autocorrelation (independence), the Shapiro–Wilk test for normality, and the White test for homoscedasticity. Key findings indicate that the Hanes–Woolf (type 1) and Lineweaver–Burk (type 2) linearizations were the best for most biomass adsorbents studied and that Eadie–Hofstee (type 3) and Scatchard (type 4) were generally invalid due to the negative parameters or assumption violations. For activated carbon, all linearization methods were unsuitable due to independence violations. In the case of nonlinear regression, there were no major assumption violations for all of the adsorbents. Symbolic regression identified the Langmuir equation only for activated carbon (AC). This study revealed shortcomings in relying solely on linearized Langmuir models. A proposed workflow recommends using nonlinear or weighted nonlinear regression, starting with Hanes–Woolf or Lineweaver–Burk linearization results as initial values for parameter estimation. If assumptions remain violated with nonlinear techniques, novel methods such as symbolic regression should be employed. This advanced regression technique can improve adsorption models’ accuracy and predictive behavior without the stringent need for assumption checking. Symbolic regression can also aid in understanding mechanisms of novel adsorbents.

Published
2024
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40. Nonpoint Source Pollution

Author

Islam, Fahrin, Lian, Qiyu, Ahmad, Zaki Uddin, Zappi, Mark E., Yao, Lunguang, and Gang, Daniel Dianchen

Published
2018

47. Nonpoint Source Pollution

Author

Ahmad, Zaki Uddin, Sanin, Maxim, Lian, Qiyu, Zappi, Mark, and Gang, Daniel Dianchen

Published
2017

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