Increasing demand for sugar and heavy initial investment
for the installation of a new
plant or the expansion of the existing plant have made the
government realize the
importance of the existing sugar plants in the country.
It is always easy to increase the
production in the existing sugar plants with proper planning
of the available resources.
Sugar plants are complex and repairable engineering systems,
comprising of various units,
namely feeding, crushing, steam generation, refining, evaporation,
crystallization, grading,
packing, etc. These units are normally arranged in hybrid
configuration. The concentrated
juice available in the form of thick syrup from refining
unit is heated slowly for a long time
at low temperature condition, resulting into the formation
of crystals. This is called
crystallization process. The semi solid juice from the cooking
pans of refining unit is first
fed to the crystallizers arranged in parallel. Now the juice
mixture consisting of yellowish
sugar crystals is suspended in a semi solid mass (molasses
or magna). This mixture is processed
in centrifuges to separate the sugar crystals from the magna.
These yellowish sugar crystals
are treated chemically to yield white crystals whereas crystal-free
magna is recycled through
sulphitors for more recovery. The sugar crystals are then
sent to the grading unit, which
comprises of a hopper, elevator, cooler and grader, arranged
in series. It finally grades the
sugar crystals according to their shape and size [1].
The decision support system deals with the quantitative
analysis of all the factors, viz.,
maintenance strategies and states of nature which influence
the maintenance decisions
associated with the crystallization unit of a sugar industry.
These decision models are
developed under the real decision-making environment, i.e.,
decision making under
uncertainty (probabilistic model) for the purpose of performance
evaluation. Such models
are used to implement the proper maintenance decisions for
a crystallization unit [3, 4].
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