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Oil & Gas Processing Review - 2006
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Optimisation Is Key to High-performing Refineries
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Nan Zhang
Lecturer in Chemical Engineering, Centre for Process Integration, School of Chemical
Engineering and Analytical Science, The University of Manchester
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Originally printed in:
Oil & Gas Processing Review
- 2006
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The refining industry deals with one of the world’s longest and most complex supply chains, beginning at a natural resource in the ground and continuing all the way through to the end-users. The current picture of the refining industry is characterised by stiff competition, stricter environmental regulations and heavier, sourer and costly crude oils, accompanied by possible disruptions caused by various factors that companies cannot control. Therefore, to maintain the profit margins in this ever-changing market environment, refiners need to have smarter strategies for flexible and adoptive operations.
Refinery optimisation is about pushing towards the maximal profit under certain limitations. The potential benefits of optimisation for process operations in oil refineries have long been observed and refiners are increasingly concerned with maintaining their profit margins in changing situations by improving their optimisation strategy. The most successful refineries are those that monitor their performances closely, adjust their operations correspondingly, identify their key weaknesses and correct them promptly. Although these principles are easy to understand, achieving them is very difficult due to the complex nature of the refining business. There are so many decisions involved to achieve the optimal operation for a refinery. All decisions are highly interrelated and the interactions have a substantial impact on overall refinery profit. Optimisation of individual department or decision does not guarantee the optimal performance of the overall refinery.
The Current Practice of Refinery Optimisation
Due to the fact that, historically, a computation facility could only handle a limited number of complexities, a top-down hierarchical approach is widely applied. It is composed of three main levels:
- planning, at the higher level;
- scheduling, at the intermediate level; and
- advanced control, at the lower level.
Data acquisition and analysis provide the feedback of actual performance of the system. Production objectives are defined over a long term at the planning level, while detailed short-term activities such as the time sequence of actions, the utilisation of storage, etc. are considered at the scheduling level. Then, individual control activities are carried out at the operational level. It can be noted that the importance of time increases (time span for optimisation decreases) when moving from the top to the bottom level of the hierarchy, and the breadth of optimisation decreases.
By assuming complete segregation between defined elements, linear programming (LP) is widely used for refinery planning. In fact, the state of the art is to use LP or mixed-integer linear programming (MILP) for planning purposes of overall refinery operations, such as optimising selections of raw materials and products. LP techniques for overall refinery optimisation are relatively mature and are represented by a number of commercial softwares – Aspen PIMS (AspenTech), GRTMPS (Haverely Systems) and TRIOS (UOP Limited).
In the meantime, many petroleum companies have developed their own LP tools in house. This is not only because of the speed and robustness of LP but also, more importantly, because a complete value structure in terms of marginal prices can be easily obtained, which provides meaningful information on solution explanation and sensitivity analysis.1 However, LP and MILP models are built based on simplified correlations, which are just a rough approximation for many processes with non-linear natures (NLP). In general, due to the simplistic nature of LP, such approaches are restricted to longterm planning for plant-wide optimisation and not suitable for day-to-day operations.
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Nan Zhang is a lecturer in chemical
engineering at the Centre for
Process Integration at the School of
Chemical Engineering and Analytical
Science of The University of
Manchester (UMIST). He has
industrial experience from being a
process engineer with Sinopec.
Dr Zhang's research interests
include overall plant optimisation,
process optimisation and emissions
reduction. He completed his PhD in
the Department of Process
Integration at UMIST, having
graduated with a bachelor of
science in chemical engineering
from Tsinghua University, China.
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