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Title: Molecular management for refining operations
Author: Wu, Yongwen
ISNI:       0000 0004 2691 4732
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2010
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Molecular management targets the right molecules to be at the right place, at the right time and at the right price. It consists of molecular characterisation of refining streams, molecular modelling and optimisation of refining processes, as well as overall refinery optimisation integrating material processing system and utility system on the molecular level. The need to increase modelling details to a molecular level is not just a result of political regulations, which force refiners to managing the molecule properly, but also seems to be a very promising to increase the refining margin. In this work, four aspects of molecular management are investigated respectively. Molecular Type Homologous Series (MTHS) matrix framework is enhanced on both representation construction and transformation methodology. To improve the accuracy and adequacy of the representation model, different strategies are formulated separately to consider isomers for light and middle distillates. By introducing statistical distribution, which takes the composition distribution of molecules into account, the transformation approach is revolutionised to increase the usability, and tackle the challenge of possibly achieving significantly different compositions from the same bulk properties by the existing approaches. The methodology is also enhanced by applying extensive bulk properties. Case studies demonstrate the effectiveness and accuracy of the methodology. Based on the proposed characterisation method, refining processes are modelled on a molecular level, and then process level optimisation is preformed to have an insight view of economic performance. Three different processes, including gasoline blending, catalytic reforming, and diesel hydrotreating, are investigated respectively. Regarding gasoline blending, the property prediction of blending components, and the blending nonlinearity are discussed. To tightly control on the property giveaway, a molecular model of gasoline blending is developed, and then integrated into the recipe optimisation. As for the conversion processes, catalytic reforming and diesel hydrotreating, reactions and reactors are modelled separately, and then followed by the consideration of catalyst deactivation. A homogeneous rigorous molecular model of a semiregenerative catalytic reforming process, considering pressure drop, has been developed. In addition, a multi-period process optimisation model has been formulated. Regarding diesel hydrotreating, a molecular model of reactions with a three-phase trickle-bed reactor has been developed. The concept of reaction family is successfully applied. A structural contribution approach is used to obtain kinetics and adsorption parameters. A series of procedures are developed to solve the complex problem. Thereafter, a process optimisation model has been developed with the consideration of catalyst deactivation, with a new strategy on the division of catalyst life. Finally, a two-level decomposition optimisation method is extended to incorporate molecular modelling into the overall refinery optimisation, and then applied in two aspects. Firstly, with the integration of the process and the site-level models, a better perspective is obtained with regard to a material processing system. By molecular modelling of refining streams and processes, the integrated approach not only controls the molecules in products properly, but also increases the overall performance. In the second application, a framework integrating a hydrogen network with hydroprocesses is developed to target the maximum profit, rather than saving hydrogen. It allocates hydrogen on the hydrogen network level and utilise hydrogen efficiently on the process level by optimising operating conditions. Consequently, the extent of achieving the maximum profit could be fully exploited with optimal hydrogen utilisation.
Supervisor: Zhang, Nan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Molecular Management ; Molecular Characterisation ; Molecular Moelling ; Refinery Optimisation