Refinery hydrogen network optimisation with improved hydroprocessor modelling

Heavier crude oil, tighter environmental regulations and increased heavy-end upgrading in the petroleum industry are leading to the increased demand for hydrogen in oil refineries. Hence, hydrotreating and hydrocracking processes now play increasingly important roles in modern refineries. Refinery h...

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Main Author: Jia, Nan
Other Authors: Zhang, Nan ; Smith, Robin
Published: University of Manchester 2011
Subjects:
662
Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.532192
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spelling ndltd-bl.uk-oai-ethos.bl.uk-5321922017-07-25T03:20:54ZRefinery hydrogen network optimisation with improved hydroprocessor modellingJia, NanZhang, Nan ; Smith, Robin2011Heavier crude oil, tighter environmental regulations and increased heavy-end upgrading in the petroleum industry are leading to the increased demand for hydrogen in oil refineries. Hence, hydrotreating and hydrocracking processes now play increasingly important roles in modern refineries. Refinery hydrogen networks are becoming more and more complicated as well. Therefore, optimisation of overall hydrogen networks is required to improve the hydrogen utilisation in oil refineries. In previous work for hydrogen management many methodologies have been developed for H2 network optimisation, all with fixed H2/Oil ratio and H2 partial pressure for H2 consumers, which may be too restrictive for H2 network optimisation. In this work, a variable H2/Oil and H2 partial pressure strategy is proposed to enhance the H2 network optimisation, which is verified and integrated into the optimisation methodology. An industrial case study is carried out to demonstrate the necessity and effectiveness of the approach. Another important issue is that existing binary component H2 network optimisation has a very simplistic assumption that all H2 rich streams consist of H2 and CH4 only, which leads to serious doubts about the solution's validity. To overcome the drawbacks in previous work, an improved modelling and optimisation approach has been developed. Light-hydrocarbon production and integrated flash calculation are incorporated into a hydrogen consumer model. An optimisation framework is developed to solve the resulting NLP problem. Both the CONOPT solver in GAMS and a simulated annealing (SA) algorithm are tested to identify a suitable optimisation engine. In a case study, the CONOPT solver out-performs the SA solver. The pros and cons of both methods are discussed, and in general the choice largely depends on the type of problems to solve.662University of Manchesterhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.532192https://www.research.manchester.ac.uk/portal/en/theses/refinery-hydrogen-network-optimisation-with-improved-hydroprocessor-modelling(e9fe6201-0e62-4b10-b51d-fe747f811ea1).htmlElectronic Thesis or Dissertation
collection NDLTD
sources NDLTD
topic 662
spellingShingle 662
Jia, Nan
Refinery hydrogen network optimisation with improved hydroprocessor modelling
description Heavier crude oil, tighter environmental regulations and increased heavy-end upgrading in the petroleum industry are leading to the increased demand for hydrogen in oil refineries. Hence, hydrotreating and hydrocracking processes now play increasingly important roles in modern refineries. Refinery hydrogen networks are becoming more and more complicated as well. Therefore, optimisation of overall hydrogen networks is required to improve the hydrogen utilisation in oil refineries. In previous work for hydrogen management many methodologies have been developed for H2 network optimisation, all with fixed H2/Oil ratio and H2 partial pressure for H2 consumers, which may be too restrictive for H2 network optimisation. In this work, a variable H2/Oil and H2 partial pressure strategy is proposed to enhance the H2 network optimisation, which is verified and integrated into the optimisation methodology. An industrial case study is carried out to demonstrate the necessity and effectiveness of the approach. Another important issue is that existing binary component H2 network optimisation has a very simplistic assumption that all H2 rich streams consist of H2 and CH4 only, which leads to serious doubts about the solution's validity. To overcome the drawbacks in previous work, an improved modelling and optimisation approach has been developed. Light-hydrocarbon production and integrated flash calculation are incorporated into a hydrogen consumer model. An optimisation framework is developed to solve the resulting NLP problem. Both the CONOPT solver in GAMS and a simulated annealing (SA) algorithm are tested to identify a suitable optimisation engine. In a case study, the CONOPT solver out-performs the SA solver. The pros and cons of both methods are discussed, and in general the choice largely depends on the type of problems to solve.
author2 Zhang, Nan ; Smith, Robin
author_facet Zhang, Nan ; Smith, Robin
Jia, Nan
author Jia, Nan
author_sort Jia, Nan
title Refinery hydrogen network optimisation with improved hydroprocessor modelling
title_short Refinery hydrogen network optimisation with improved hydroprocessor modelling
title_full Refinery hydrogen network optimisation with improved hydroprocessor modelling
title_fullStr Refinery hydrogen network optimisation with improved hydroprocessor modelling
title_full_unstemmed Refinery hydrogen network optimisation with improved hydroprocessor modelling
title_sort refinery hydrogen network optimisation with improved hydroprocessor modelling
publisher University of Manchester
publishDate 2011
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.532192
work_keys_str_mv AT jianan refineryhydrogennetworkoptimisationwithimprovedhydroprocessormodelling
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