The highly competitive market in the oil refining industry forces refiners look for more detailed information of both feedstocks and products to achieve the optimal economic performance. Due to stricter environmental legislations, the molecular level characterisation has been investigated by various researchers and shows promising advantages in modern refinery design and operation. Although various molecular characterisation methods have been developed, there is an unavoidable trade-off between keeping astronomical molecule details and practicality in industrial applications. In the meantime, many of these methodologies have different characteristics and different focuses according to a particular application purpose. Our aim is hence to tackle the problems of developing manageable and practical technical solutions for molecular characterisation of petroleum fractions for vary refinery processes. A pseudo-component based approach is developed within a modified MTHS (Molecular Type Homologous Series) matrix framework (Peng, 1999) to represent the molecular information of a particular refining stream. This proposed methodology incorporates both molecular type and pseudo-component information by the conjunction of homologous series and boiling points in the matrix framework. To increase the usability of this method, a 3-parameter gamma distribution function is introduced to describe the composition of each structural molecular type. Typical PIONA (paraffin, iso-paraffin, olefin, naphthene, aromatic) analysis, ratios between each homologous types and the percentage of particular carbon type are considered as well as the distillation curve and the density of a stream. More strict product specifications and environmental legislations make strong restriction to the benzene and aromatics content in gasoline products, which motivate refiners to understand, characterise and simulate gasoline catalytic reforming on molecular-level. In this work, kinetic and reactor model of naphtha catalytic reforming is developed based on the proposed MTHS method. The naphtha feedstock composition is represented by the MTHS matrix, and a kinetic network is constructed according to conversions among matrix elements. A process model proposed by Wu (2010) is employed for reforming modelling. The proposed model is then applied to a bench-scale semi-regenerative catalytic reforming unit, which contains 3 fixed-bed reactors, for validation. The influences of essential operating conditions, such as reactor inlet temperature, pressure and weight hourly space velocity (WHSV), on the product distribution and quality are explored. The developed characterisation is also applied in gasoline blending modelling. A molecular-level nonlinear gasoline blending model is developed based on proposed MTHS method with validation. Key properties such as Octane Numbers (ONs) and RVP are blended by molecular matrix elements, and the influence of molecular composition on bulk properties is obvious. A case of recipe optimisation is studied to show the applicability of the proposed method. The implementation of the developed MTHS method for catalytic reforming and gasoline blending demonstrates the compatibility when characterising different petroleum streams, and provides a common platform to simulate and optimise refining operations on the same molecular basis.

<p>Current gasoline blend planning practice is to optimize blend plans via discrete-time multi-period NLP or MINLP models and schedule blends via interactive simulation. Solutions of multi-period models using discrete-time representation typically have different blend recipes for each time period. In this work, the concept of an inventory pinch point is introduced and used it to construct a new decomposition of the multi-period MINLP problems: at the top level nonlinear blending problems for periods delimited by the inventory pinch points are solved to optimize multi-grade blend recipes; at the lower level a fine grid multi-period MILP model that uses optimal recipes from the top level is solved in order to determine how much to blend of each product in each fine grid period, subject to minimum threshold blend size. If MILP is infeasible, corresponding period between the pinch points is subdivided and recipes are re-optimized.</p> <p>Two algorithms at the top level are examined: a) multi-period nonlinear model (MPIP) and b) single-period non-linear model (SPIP). Case studies show that the MPIP algorithm produces solutions that have the same optimal value of the objective function as corresponding MINLP model, while the SPIP algorithm computes solutions that are most often within 0.01% of the solutions by MINLP. Both algorithms require substantially less computational effort than the corresponding MINLP model. Reduced number of blend recipes makes it easier for blend scheduler to create a schedule by interactive simulation.</p> / Master of Applied Science (MASc)