Green diesel blends design using decomposition-based optimization approach

Petrodiesel-biofuel/biochemical blend (green diesel blend) is a promising solution in reducing environmental impact while improving the performance of petrodiesel. A systematic computer-aided approach can efficiently solve green diesel blends‟ design problems to replace the iterative, costly and tim...

Full description

Saved in:

Bibliographic Details
Main Author:	Phoon, Li Yee
Format:	Thesis
Language:	English
Published:	2017
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/79353/1/PhoonLiYeePFChE2017.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.79353
record_format	uketd_dc
spelling	my-utm-ep.793532018-10-14T08:44:49Z Green diesel blends design using decomposition-based optimization approach 2017 Phoon, Li Yee TP Chemical technology Petrodiesel-biofuel/biochemical blend (green diesel blend) is a promising solution in reducing environmental impact while improving the performance of petrodiesel. A systematic computer-aided approach can efficiently solve green diesel blends‟ design problems to replace the iterative, costly and time-consuming experimental trial-and-error approach. Other than the engine performance related fuel properties (density, kinematic viscosity, cetane number and higher heating value), the safety indicator: flash point is an important safety consideration for diesel fuel and it should be considered in the green diesel blend design to avoid fire accident. The aims of this study are to develop a systematic computer-aided tailor-made green diesel blend design algorithm and to improve the flash point prediction model, which is the Liaw model for B5 palm oil biodiesel (B5)-ester/ether/alcohol blends. The algorithm contains two main phases: the model-based design and the experimental validation. The optimum green diesel blend is computationally optimized in the model-based design phase. The accuracy of the Liaw model using UNIFAC type models is improved for B5-ester/ether/alcohol by regressing the UNIFAC group interaction parameters. The verified and improved Liaw models are embedded into the model-based design phase to optimize the green diesel blend. The physicochemical property, engine performance and emissions of the optimum blend obtained in the model-based design phase are experimentally validated in the experimental validation phase. The application of the developed design algorithm is illustrated by finding the right combination of the binary and ternary blends of B5-ester/ether/alcohol. The ideal Liaw model, the Liaw model using the original UNIFAC and the Liaw model using original UNIFAC with parameters set B (group interaction parameter between CH2 and OH are revised) are used to predict the flash points of the B5-ether, B5-ester and B5-alcohol blends. GB3 (B5-11.1 % by mass of diethyl succinate) and GT1 (B5-24.1% octanol-5.9 % diethyl succinate, by mass %) with or without the cetane enhancer: 2-ethylhexyl nitrate (2EHN) are the optimum binary and ternary blends with the high oxygen content obtained in the model-based design phase. Satisfactory experimental validation results were obtained in experimental validation phase and GT1A (GT1-0.17 % by mass of 2EHN) ) was identified to be the most promising green diesel blend owning to its lower emissions of nitrogen oxide (19.21 % lower than B5), un-burnt hydrocarbon (27.48 % lower than B5) and carbon monoxide (36.73 % lower than B5). Meanwhile, GT1A has comparative fuel efficiency to B5. The developed green diesel blend design algorithm serves as an improved model for solving green diesel blend design problem. 2017 Thesis http://eprints.utm.my/id/eprint/79353/ http://eprints.utm.my/id/eprint/79353/1/PhoonLiYeePFChE2017.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Chemical & Energy Engineering Faculty of Chemical & Energy Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Phoon, Li Yee Green diesel blends design using decomposition-based optimization approach
description	Petrodiesel-biofuel/biochemical blend (green diesel blend) is a promising solution in reducing environmental impact while improving the performance of petrodiesel. A systematic computer-aided approach can efficiently solve green diesel blends‟ design problems to replace the iterative, costly and time-consuming experimental trial-and-error approach. Other than the engine performance related fuel properties (density, kinematic viscosity, cetane number and higher heating value), the safety indicator: flash point is an important safety consideration for diesel fuel and it should be considered in the green diesel blend design to avoid fire accident. The aims of this study are to develop a systematic computer-aided tailor-made green diesel blend design algorithm and to improve the flash point prediction model, which is the Liaw model for B5 palm oil biodiesel (B5)-ester/ether/alcohol blends. The algorithm contains two main phases: the model-based design and the experimental validation. The optimum green diesel blend is computationally optimized in the model-based design phase. The accuracy of the Liaw model using UNIFAC type models is improved for B5-ester/ether/alcohol by regressing the UNIFAC group interaction parameters. The verified and improved Liaw models are embedded into the model-based design phase to optimize the green diesel blend. The physicochemical property, engine performance and emissions of the optimum blend obtained in the model-based design phase are experimentally validated in the experimental validation phase. The application of the developed design algorithm is illustrated by finding the right combination of the binary and ternary blends of B5-ester/ether/alcohol. The ideal Liaw model, the Liaw model using the original UNIFAC and the Liaw model using original UNIFAC with parameters set B (group interaction parameter between CH2 and OH are revised) are used to predict the flash points of the B5-ether, B5-ester and B5-alcohol blends. GB3 (B5-11.1 % by mass of diethyl succinate) and GT1 (B5-24.1% octanol-5.9 % diethyl succinate, by mass %) with or without the cetane enhancer: 2-ethylhexyl nitrate (2EHN) are the optimum binary and ternary blends with the high oxygen content obtained in the model-based design phase. Satisfactory experimental validation results were obtained in experimental validation phase and GT1A (GT1-0.17 % by mass of 2EHN) ) was identified to be the most promising green diesel blend owning to its lower emissions of nitrogen oxide (19.21 % lower than B5), un-burnt hydrocarbon (27.48 % lower than B5) and carbon monoxide (36.73 % lower than B5). Meanwhile, GT1A has comparative fuel efficiency to B5. The developed green diesel blend design algorithm serves as an improved model for solving green diesel blend design problem.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Phoon, Li Yee
author_facet	Phoon, Li Yee
author_sort	Phoon, Li Yee
title	Green diesel blends design using decomposition-based optimization approach
title_short	Green diesel blends design using decomposition-based optimization approach
title_full	Green diesel blends design using decomposition-based optimization approach
title_fullStr	Green diesel blends design using decomposition-based optimization approach
title_full_unstemmed	Green diesel blends design using decomposition-based optimization approach
title_sort	green diesel blends design using decomposition-based optimization approach
granting_institution	Universiti Teknologi Malaysia, Faculty of Chemical & Energy Engineering
granting_department	Faculty of Chemical & Energy Engineering
publishDate	2017
url	http://eprints.utm.my/id/eprint/79353/1/PhoonLiYeePFChE2017.pdf
_version_	1747818207802032128

Green diesel blends design using decomposition-based optimization approach

Similar Items