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Preeti Aghalayam

Researcher at Indian Institute of Technology Madras

Publications -  64
Citations -  1561

Preeti Aghalayam is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Catalysis & Underground coal gasification. The author has an hindex of 22, co-authored 59 publications receiving 1360 citations. Previous affiliations of Preeti Aghalayam include University of Delaware & University of Massachusetts Amherst.

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Reactor Model for the Underground Coal Gasification (UCG) Channel

TL;DR: In this article, the gasification channel is viewed as a one-dimensional packed bed reactor and a pseudo-transient model is simulated for temperature and composition profiles of the gas and solid phases.
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Detailed surface reaction mechanism for reduction of NO by CO

TL;DR: In this article, a quantitative surface reaction mechanism based on elementary steps was developed to understand the phenomena of catalytic reduction of NO by CO. The elementary reaction mechanism was coupled with the continuously stirred tank reactor/packed bed reactor models and the simulation results were validated against literature experiments for the NO-CO reaction on Pt, and the NO−CO-O 2 reaction on Ir catalyst.
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A detailed surface reaction mechanism for CO oxidation on Pt

TL;DR: In this article, a detailed surface reaction mechanism for oxidation of CO on polycrystalline Pt surfaces, capable of predicting various available experimental features, has been developed using a multistep methodology.
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Simulation of hydrogen mitigation in catalytic recombiner: Part-I: Surface chemistry modelling

TL;DR: In this paper, the authors compared the performance of a single step reaction and a multiple reaction model for predicting hydrogen-oxygen reaction over a platinum surface and found that the diffusion controlled model over-predict the data.
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Compartment Modeling for Flow Characterization of Underground Coal Gasification Cavity

TL;DR: In this paper, the authors simplify the modeling approach for relatively quick but reliable predictions for application in process design and optimization, and the primary objective of this work is to simplify the modelling approach for a relatively quick and reliable prediction.