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The goal of my research work is to develop novel mathematical programming models
and efficient numerical techniques for a variety of problems in process systems
engineering including: planning and scheduling of batch and continuous operations,
process synthesis, and evolutionary computation for improving the resource utilization
of industrial processes.
Process Operations - Planning and Scheduling:-
Most of the industrial processes can be classified into either a multi-product or
a multi-purpose mode of operation with tremendous opportunities for improving the
resource utilization through proper planning and scheduling of different operations
in an efficient manner. We have developed several novel mathematical programming models
for efficiently solving different classes of planning and scheduling problems including:
cyclic scheduling, short-term and medium-term scheduling, and reactive scheduling of batch,
continuous, mixed production facilities (involving both batch and continuous processes),
and hybrid flowshop facilities (multi-stage plants with several parallel units) with
rigorous treatment for handling different storage policies. These models have been
initially tested on several benchmark examples from the scheduling literature resulting in
superior computational performance. The proposed models have been applied in different
industrial applications: refinery lube-oil plant, crude-oil unloading and loading, tobacco
plant, paper manufacturing, and polymer plants thus resulting in improved resource utilizations.
Evolutionary Computation:-
The main aims of this work are: (a) to study and develop novel techniques for improving the
convergence in evolutionary optimization approaches such as simulated annealing, genetic
algorithms, and differential evolution; and (b) to apply and validate the proposed approaches
for optimization of nonlinear chemical processes.
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