Design of an environmentally friendly fuel based on a synthetic composite nano-catalyst through parameter estimation and process modeling
View/ Open
Main article (461.7Kb)
Download
Publication date
2021-01Rights
© 2021 de Gruyter. Reproduced in accordance with the publisher's self-archiving policy.Peer-Reviewed
YesOpen Access status
embargoedAccess
Metadata
Show full item recordAbstract
In this paper, oxidative desulfurization (ODS) process is studied for the purpose of removing the sulfur components from light gas oil (LGO) via experimentation and process modeling. A recently developed (by the authors) copper and nickel oxide based composite nano-catalyst is used in the process. The ODS experiments are conducted in a batch reactor and air is used as an oxidizer under moderate operation conditions. Determination of the kinetic parameters with high accuracy is necessary of the related chemical reactions to develop a helpful model for the ODS operation giving a perfect design of the reactor and process with high confidence. High conversion of 92% LGO was obtained under a reaction temperature of 413 K and reaction time of 90 min for synthesized Cu Ni /HY nano-catalyst. Here model based optimization technique incorporating experimental data is used to estimate such parameters. Two approaches (linear and non-linear) are utilized to estimate the best kinematic parameters with an absolute error of less than 5% between the predicted and the experimental results. An environmentally friendly fuel is regarded the main goal of this study, therefore the optimization process is then employed utilizing the validated model of the prepared composite nano-catalyst to get the optimal operating conditions achieving maximum conversion of such process. The results show that the process is effective in removing more than 99% of the sulfur from the LGO resulting in a cleaner fuel.Version
Accepted manuscriptCitation
Jarullah AT, Muhammed SK, Al-Tabbakh BA and Mujtaba IM (2021) Design of an environmentally friendly fuel based on a synthetic composite nano-catalyst through parameter estimation and process modelling. Chemical Product and Process Modeling. 17(3): 213-233.Link to Version of Record
https://doi.org/10.1515/cppm-2020-0097Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1515/cppm-2020-0097