The use of hydrogen is gaining reputation because of its many beneficial properties to the environment in comparison to hydrocarbon not minding its net energy requirement for production challenges. With most countries adopting a strategy to achieve their net-zero emissions targets, it becomes much more important to find affordable, low-carbon ways of producing hydrogen. An innovative method of producing hydrogen from hydrocarbon reservoirs while keeping the associated by-products in the reservoir has been demonstrated researchers from the University of Calgary. However, in this study, a framework for designing an combustion model that considers four key hydrogen forming reactions - steam reforming, partial oxidation, autothermal reforming and pyrolysis, was developed. A set of non-linear equations obtained from chemical equilibrium analysis of the hydrogen forming reactions were solved using a Newton-Jacobi interation. Analysis of the change in Gibbs free energy of each reaction were then used as a screening tool for implementing a numerical model. Results obtained from the combustion model were then validated against results obtained from thermal reservoir simulator CMG STARS. Results from the model reveal an upward trending sinusoidal relationship between steam-carbon ratio and the amount of hydrogen yield from an hydrogen production study. The combustion model could be used as a framework to design experimental analysis.
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http://dx.doi.org/10.1039/d3ra01762a | DOI Listing |
Sci Rep
January 2025
Department of Engineering, FH Campus Wien - University of Applied Sciences, Favoritenstraße 226, Vienna, 1100, Austria.
Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi's biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Thiruvananthapuram, Kerala, India.
This study quantified the environmental impacts of residue burning of major produced and burned crops in Madhya Pradesh, central India. The environmental impacts were quantified using Life Cycle Assessment (LCA) coupled with Monte Carlo simulation of 1000 iterations. Crop wise marginal impacts of the crops have been quantified using Multivariate regression model.
View Article and Find Full Text PDFEnviron Sci Technol
January 2025
Department of Chemistry & Chemical Biology, McMaster University, Hamilton L8S 4L8, Canada.
Wildfires emit large amounts of polycyclic aromatic hydrocarbons (PAHs) into the atmosphere. As PAHs emitted from anthropogenic sources are known to accumulate in urban surface grime present on building exteriors and windows, we hypothesized that PAH-containing wildfire smoke plumes could similarly increase PAH grime loadings. To explore this hypothesis, we coupled analysis of PAHs in grime samples collected from August to November 2021 in two historically smoke-affected Canadian cities, Calgary and Kamloops, with contemporaneous field- and model-based indicators of wildfire influence.
View Article and Find Full Text PDFPaediatr Perinat Epidemiol
January 2025
Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA.
Background: Epidemiologic studies have demonstrated that ambient concentrations of particulate matter < 2.5 μm (PM) are associated with reduced fecundability, the per cycle probability of conception. The specific constituents driving this association are unknown.
View Article and Find Full Text PDFLaser absorption spectroscopy (LAS) is a well-established measurement technique for quantitative chemical speciation in a combustion environment. However, LAS measurement of nitric oxide (NO) in ammonia flames has never been reported in the literature. This is despite the community's recent strong interest in carbon-neutral ammonia combustion and the associated NO formation problem.
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