Chemicals and loss of catalyst was considered as an operating cost based on
usage as were utilities. Labor, a fixed operating cost, was included with the return
on investment. Maintenance cost was fixed as a percentage of the total
maintenance cost.
Model Development
At the same time as the process and cost data were being generated, the basic form that
the model would finally take was also being developed. This consisted of defining the
various optional routes of each stream within the simulated refinery model.
The optional routing of the streams was carefully selected. This selection had to
satisfy at least one of two requirements. Firstly, would such a routing actually
contribute to satisfying the product slate and the premises of the problem? Secondly, would such a routing be feasible under actual operating conditions?
Just as a refinery is described by the units of which it is comprised, so also was
the refinery linear model described. Here, each processing unit was considered a
submodel in itself, and these submodels were defined by their process and
economic data.
These data were arranged in tabular from which were easily accessed and listed in
recognizable terminology. An example of a submodel tabulation as used in this study
is shown in Table 23. From this tabulation, a matrix generator, called GAMMA was
used to assemble the many submodels into a complete LP matrix. The matrix was
solvable by a linear programming system called OMEGA. These tabular input arrays
were also used by the solution report writers as we shall see later.
This complete mathematical model of the refinery was displayed by an equation
listing of the entire contents of the data. The equations showed the interrelationships of the many variables, including the refinery streams, the blending constraints,
the unit to investment ties, etc. These were also in a form which was recognizable to
the engineers working on the project.
Having now assembled all the data in a manner usable for linear programming, it
was necessary to check it for errors. Various computer techniques had been
developed for this purpose, and these, together with a secondary check by the
process engineers, substantially eliminated the possibilities of obvious error and
invalid data.
However, as an added safeguard, a final checkout was carried out by actually
solving a test case. These results were scrutinized to ensure that the output gave a
realistic refinery configuration and that all was in balance.
Optimizing and Other Techniques
A major value of linear programming is that once environment is reflected within an
LP framework, this environment can be optimized. In this study, optimization could
be accomplished either by maximizing profit or minimizing expense. In this specific
Petroleum Refinery Planning and Economics
777
usage as were utilities. Labor, a fixed operating cost, was included with the return
on investment. Maintenance cost was fixed as a percentage of the total
maintenance cost.
Model Development
At the same time as the process and cost data were being generated, the basic form that
the model would finally take was also being developed. This consisted of defining the
various optional routes of each stream within the simulated refinery model.
The optional routing of the streams was carefully selected. This selection had to
satisfy at least one of two requirements. Firstly, would such a routing actually
contribute to satisfying the product slate and the premises of the problem? Secondly, would such a routing be feasible under actual operating conditions?
Just as a refinery is described by the units of which it is comprised, so also was
the refinery linear model described. Here, each processing unit was considered a
submodel in itself, and these submodels were defined by their process and
economic data.
These data were arranged in tabular from which were easily accessed and listed in
recognizable terminology. An example of a submodel tabulation as used in this study
is shown in Table 23. From this tabulation, a matrix generator, called GAMMA was
used to assemble the many submodels into a complete LP matrix. The matrix was
solvable by a linear programming system called OMEGA. These tabular input arrays
were also used by the solution report writers as we shall see later.
This complete mathematical model of the refinery was displayed by an equation
listing of the entire contents of the data. The equations showed the interrelationships of the many variables, including the refinery streams, the blending constraints,
the unit to investment ties, etc. These were also in a form which was recognizable to
the engineers working on the project.
Having now assembled all the data in a manner usable for linear programming, it
was necessary to check it for errors. Various computer techniques had been
developed for this purpose, and these, together with a secondary check by the
process engineers, substantially eliminated the possibilities of obvious error and
invalid data.
However, as an added safeguard, a final checkout was carried out by actually
solving a test case. These results were scrutinized to ensure that the output gave a
realistic refinery configuration and that all was in balance.
Optimizing and Other Techniques
A major value of linear programming is that once environment is reflected within an
LP framework, this environment can be optimized. In this study, optimization could
be accomplished either by maximizing profit or minimizing expense. In this specific
Petroleum Refinery Planning and Economics
777