Commercial lubricant
industries use a complex pipeline
network
for the sequential processing of thousands of unique products annually.
Flushing is conducted between changeovers to ensure the integrity
of each production batch. An upcoming product is used for cleaning
the residues of the previous batch, resulting in the formation of
a commingled/mixed oil that does not match the specifications of either
of the two batches. The existing operations are based on the operator’s
experience and trial and error. After a selected flush time, the samples
are tested for their viscosity to determine the success of a flush.
The approach results in long downtime, the generation of large commingled
oil volumes, and huge economic losses. Hence, to overcome the drawback,
our work introduces a solution strategy for systematically optimizing
flushing operations and making more informed decisions to improve
the resource-management footprint of these industries. We use the
American Petroleum Institute-Technical Data Book (API-TDB) blending
correlations for calculating the mixture viscosities in real-time.
The blending correlations are combined with our first-principles models
and validated against well-designed experimental data from the partnered
lubricant facility. Next, we formulate an optimal control problem
for predicting the optimum flushing times. We solve the problem using
two solution techniques viz. Pontryagin’s maximum principle
and discrete-time nonlinear programming. The results from both approaches
are compared with well-designed experimental data, and the economic
and environmental significance are discussed. The results illustrate
that with the application of a discrete-time nonlinear programming
solution approach, the flushing can be conducted at a customized flow
rate, and the necessary flushing volume can be reduced to over 30%
as compared to the trial-and-error mode of operation.